Book Demo
✖
    QUICK FILTERS

    see all

    Featured
    12 min read
    Mike West

    How to Game the Best Company to Work For Awards

    This is continued from a "Gloves Off Friday Post" by Mike West on Linkedin Pulse here: How the Best Company Award is Wrong How The Best Companies To Work For Are Ranked. Different newspapers, magazines and institutes have different methodologies to rank order companies, however the thing they have in common is that a large portion of the rating is based on an employee survey. Below is what it states on the Fortune website: "To identify the 100 Best Companies to Work For, each year Fortune partners with Great Place to Work to conduct the most extensive employee survey in corporate America. Two-thirds of a company’s survey score is based on the results of the Trust Index Employee Survey, which is sent to a random sample of employees from each company. This survey asks questions related to employees’ attitudes about management’s credibility, overall job satisfaction, and camaraderie. The other third is based on responses to the Culture Audit, which includes detailed questions about pay and benefit programs and a series of open-ended questions about hiring practices, methods of internal communication, training, recognition programs, and diversity efforts. Glass Door also bases its rating on survey questions. "To determine the best places to work, Glassdoor looks at company reviews provided by employees between November 13, 2013 and November 2, 2014, in which individuals are asked to consider and rate such factors as overall satisfaction, CEO leadership, career opportunities, compensation, and work-life balance." To the credit of the rating agencies, asking employees at different employers the same questions seems like the fairest and most scientifically valid way to compare employers to each other. How to Game it. Knowing surveys are the most substantial part of the ranking, the key to gaming Best Company to Work For Awards is to know the natural distribution of attitude by employee characteristics and use this information to increase the % of employee surveyed in segments that have higher positive attitude than average, while decreasing the sample rate in those with lower positive attitude than average. Relatively unsophisticated rating agencies, such as local newspapers, could easily be exploited in this manner. On the other hand some rating agencies, like Fortune and the "Great Company to Work For Institute" will reply to this with, "we use a random sample". Unscrupulous HR bosses do not be deterred. While using a "random sample" seems like a great way to prevent manipulation, this too can be beat. The unscrupulous HR boss could beat random sampling by proportionally manipulating the quantity of email addresses from different segments he/she provided to the rating agency (determined by an understanding of the natural response of varied segments) and/or he/she could just filter which emails allowed to pass through the email server. To be clear - I am making a point - I am not suggesting a company should cheat, however if a company wanted to do so this would be how they could do so without directly standing behind employees shoulders while they fill out the survey or offering to throw pizza parties for the groups with the highest results. By the way, you better believe that some companies and managers DO do things like that. I am not aware of a specific circumstance where a company has deliberately manipulated a survey process, however I am aware of circumstances where companies have benefited indirectly without the knowledge of the rating agency. Here is the Problem. How positively people respond to questions at their employer varies reliably by certain employee characteristics and these characteristics are not uniformly sampled across all employers. Characteristics that may statistically matter extend from natural demographic distributions (age, gender, ethnicity..), to natural job type distributions (professional, skilled labor and unskilled labor), to natural geographic distributions, to other characteristics we may not even typically record. I left out the most important. The characteristic that I have found to consistently vary between segments by substantial margin, unrelated to the actual quality of the company, is Tenure Group. Company Tenure is calculated something like this (Current Date - Start Date) and is usually grouped something like this (0-1 Year, 1.1 to 3 Years, 3.1 to 5 years, 5.1 years to 10, 10+ Years). What it Looks Like. A typical tenure group pattern looks something like this: Typical Employee Engagement Pattern by time in job It is worth mentioning that Tenure Distribution is at least partly driving geographic and industry differences. You can see this if you consider that the labor market characteristics of geographies and industries have a relationship with the proportional distribution of Company Tenures. Faster growing local economies and industries have lower overall tenure so these populations would also have proportionally more people in low tenure groups. In the graph below note the growth characteristics of our leading industries. Think about the growth characteristics of the industries to the right. See it? PWC 2015 Report PWC 2015 Report Does it Matter That Much? You might say, "Come on!, How much could this problem really matter? Actually a lot! The phenomenon can be observed in rare cases when the #1 Company Award unexplainably flips away from a company in one year and returns to them in a future year. What explains the difference is decreased hiring rate, relative to other nearby companies on the list. Statista This is a little far fetched but the other thing you could do to game the award is to hire a large number of people right before the time of year of the awards and/or right before you apply for the award the first time. I can't say anybody does that to win awards of this nature intentionally, or anyone ever would, but some benefit from a massive growth rate that ensures this will happen for them whether they try to do it or not. Should We Care? "Google has been on the list for 10 years with this being its seventh time at No. 1, thanks to sparking the imagination of its talented and highly compensated workers, and by adding perks to an already dizzying array of freebies ." The first reason we should care is that the companies that win these awards receive substantial press and as a result receive a remarkable increase in the amount of job applicants. Think something akin to a million new applications to Google. If this is coupled with an increased ability to filter job applicant pools to identify high quality candidates then these #1 picked employers have a substantial competitive advantage in ability to select the most highly qualified workers. Further, these employers have a PR gains from which to take key talent away from other companies and keep their own key talent. Another reason we should care is that many organizations try to imitate the "Best Practices" of the companies that are highly ranked on these lists. The companies that want to be like them may unfortunately be imitating characteristics that have no actual relationship to what makes a great company to work for, or the reported survey results and therefore arbitrary. Recall, correlation does not imply causality. Trying to imitate all of the practices of the purported great companies may result in investments that generate no return and simultaneously decrease margin, thus making it more difficult for the imitating company to compete in the future. This could provide substantial advantages to companies that can make the top of the list AND afford to give up a small portion of big margin to spend above average on salary and unusual employee perks. I have written extensively about how Best Practices lead us astray in a prior blog post : 7 Reasons Best Practices Are Not Best For You. What can be done about it? For starters the newspapers, magazines and rating agencies could sample survey responses in tenure groups to ensure an apples to apples comparison. Instead of a random sample this would be called a "Stratified Random Sample". If they really wanted to step up their game they could also just put all of the data into a single multiple regression model, to isolate a company effect from tenure effects and any other variables that may skew results, be they demographic, job related, geography or whatever. This sounds complicated but actually any undergraduate statistics major or any graduate behavioral level science major could run this analysis. Now, as I state repeatedly, I am not suggesting anyone should really try to game the Best Company to Work For Awards, however I can understand why you would want to up your game to truly improve employee engagement and be a truly great employer. The best way to do so is to look across data sets and use the engagement data in increasingly better ways to get better at actually moving engagement. Survey providers are good at managing the process of constructing a good survey and collecting data but provide a very limited view of the data and no survey providers work with their survey data, plus your other data sources to provide a single longitudinal view of the truth. No survey provider maintains ongoing relationship with your sources that adjust with underlying structural changes automatically and that you can query in real time. I know of some employers who can look at survey data in this way, however they cannot do so while maintaining employee confidentiality as a survey provider would. The world is now in luck - One Model can take data from a survey provider (or tool) and join it into a single view of the truth with other employee related data, allowing a longitudinal view, update automatically, and most importantly, can do this while maintain employee confidentiality just a survey provider would (by not allowing you to report data below a sample size threshold) :-) If this interests you, let us know and we would be happy to provide you with a demo so you can see for yourself what sort of new advantages this can give you! ---------------------------------------------------------------------------------------- This is a continuation of a "Gloves Off Friday" post : How the Best Company Award is Wrong More like it: Why Josh Bersin is Wrong About Embedded Analytics? The Most Dangerous Technology in HR Today What Your HR Technology Sales Rep Doesn't Want You To Know ---------------------------------------------------------------------------------------- Who is Mike West? Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology startups. Mike is currently the VP of Product Strategy for One Model -the first cloud data warehouse platform designed for People Analytics. Connect with Mike West on Linkedin

    Read Article

    Featured
    6 min read
    Mike West

    How your company is doing People Analytics poorly (even if you think you aren't doing People Analytics)

    You may not think your company is yet doing People Analytics but here is one way your employer is already doing so, poorly. Employers routinely use Credit Scores to screen candidates. “47% of employers conduct credit checks to screen potential new potential hires” (Society of Human Resource Management) That is to say, they use a score designed to measure credit default risk and apply it to employment screening on some dubious premise that this may be predictive of success, good judgment or good moral character in the context of employment. What is a credit score? A credit score is a numerical expression of an analysis of a person's credit files, to represent the creditworthiness of the person. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. A credit score is based on a history of financial transactions sourced from credit bureaus. A credit score applies a mathematical "algorithm" to a profile and history of transactions to categorize a loan candidate so that financial institutions can make a better decision about whether or not to loan someone money, and/or to make a better decision about how to price a loan to loan money profitably, with regard to risk.A bank may offer to lend a low credit score candidate a loan but at a higher interest rate. This allows the bank to maintain a certain level of profitability from different risk segments or deny working with certain risk segments all together. Why do employers use credit scores to screen employees? In employment the credit score is presumably used for a similar high level purpose as banks (control risk). In the case of employment you are trying to reduce the risk of a "bad" hire or stated inversely, reduce the risk of not selecting a good hire. Employers have more applicants than available positions and they limited time to consider each candidate, so they want some quick means to make a decision. Or sometimes we make a decision but apply additional rigor through screening devices to prove to ourselves we made the right decision. Directly from a credit reporting firm’s website : Credit Scores can help employers “make decisions quickly and easily when deciding on potential candidates” (TransUnion) To shed additional light on this practice, I bring up a conversation at a recent U.S. Senate hearing… “What is the evidence that there is strong correlation between accessing an applicant's credit history and eventually problems of loss to the employer?” Senator Rosenbaum (Oregon) “We don’t have any research to show any statistical correlation between what is in somebody’s credit report and their job performance or their likelihood to commit fraud.” Erick Rosenberg (TransUnion) Here are some other facts to consider about using Credit Scores for employment screening: - 1 in 4 credit reports have been found to have an error - 1 in 20 have been found to have serious errors - Other issues : “52% of all debt on credit reports stems from medical expenses” CFPB 25% error rate? If you thought your core HR data has problems, maybe it doesn't sound so bad now? Scores are being used as a reflection of "character" but if the medical debt statistic is correct, in half or more cases credit scores may be low because of uncontrollable circumstances - how is that reflective of character? Imagine that as a result of chance circumstances you are in dire need of money and you are also as a result refused a means of obtaining money? Does this practice make any sense? It's predictive but you don't need any math at all to predict this outcome - it's a self fulfilling prophecy - self fulfilling prophecies are convenient if you sell predictions. How do the credit reporting agencies and employers defending this practice? The "all else equal" claim. That is that "all else equal" this is a better way of making a decision than nothing. Is it really? If we can find evidence of errors, systematic bias and NO evidence that there is any relationship with job performance - is this really a better way of making a decision than a coin flip? Or why not do the work to find something else not equal that has less errors, less systematic bias or more evidence of relationship to job performance? Why not find a better way of making things not equal? We have always done it this way or this is how others are doing it. Come on, is this really a good reason? Is this how to make good business decisions? This is not how any great business decisions are made, ever. It seems like a "plausible method" of making decisions by logic or rational argument. Here are some theories that are at least as plausible as the theory that credit reports may be good predictors of performance … Maybe because we really have no idea how to screen a candidate for characteristics that relate to performance without doing this work directly … Maybe because we want to find a way to systematically discriminate against populations that come from impoverished communities, thus discriminating against a high percentage of minorities, without doing so directly… Or we don't delve into the details to see how this math may or may not play out and we don't care. Maybe because we want to eliminate candidates who have high medical expenses and would cost our health plans money, without doing so directly… (This actually seems like the most mathematically plausible scenario to me) I ask again, why are you using credit scores to screen job applicants? How about this, why don't we actually do the work to see what factors drive performance in our organization and/or isolate with data how we can increase the probability of high performance, regardless of starting individual characteristics. To me this is a better way of making decisions and a better way to run a business. Thank you to John Oliver (yes, John Oliver the comedian) for highlighting these issues in a clear, emotionally charged and entertaining way on his show : Last Week Tonight (HBO) – Be advised - wear headphones - there is some language in this that may be offensive and not safe for a work environment. I would argue the practice of using credit reports as screening devices is equally offensive and unsafe. I wish that each employer would put as much time researching the practice of using credit reports as employment screening devices as an HBO comedian did. Seems like it would be even more useful to us to know these things than it is for him, is it not?

    Read Article

    Featured
    12 min read
    Mike West

    Does People Analytics Work? #PAQA

    “Half the money I spend on advertising is wasted; the trouble is, I don't know which half.” Henry Ford, Lord Lever, John Wanamaker... People often ask, “Does People Analytics work?” These are smart people, who genuinely would like a straight answer to an honest question. I will sometimes start a conversation about People Analytics with something like this: did you know that in the United States alone, companies currently spend about 7 trillion on employees in Payroll*? My experience has been that how, where, when and why money is spent matters and results will vary on how well or poorly money is invested. Unfortunately having worked in HR for over 15 years I can tell you that more often than not decisions affecting how people are : a.) coincidental b.) arbitrary, c.) biased or d.) political (by this I mean rooted in conflict – the expression of personal or group advantages and disadvantages). None of these imply a reason that is good for doing business, or people or the economy. (*It is probably over 10 trillion when you include the cost of Benefits and other Perks) Before you can say whether or not People Analytics’ works, you need to know what its job is. We can gather from the name, People Analytics is the application of analysis to people. O.k. but what’s the goal of People Analytics? How can you say whether it works if you don’t know what it’s supposed to achieve? In prior People Analytics Q/A posts I attempted a definition - I was looking for a unique combination of words to represent the essence of a complex concept simply without missing what is different about it. This is what I came up with:People Analytics is the systematic application of behavioral science and statistics to Human Resource Management to achieve probability derived business advantages. I chose these words deliberately but admittedly a little too deliberately to roll off the tongue in casual conversation. So let’s take a step back and talk about it. More simply put, People Analytics is the application of analysis to people in a business. For the sake of discussion we are going to put "people in a business" in the realm of Human Resources. To understand People Analytics lets first understand what is the job of HR? Most people think of HR as a series of low-level administrative activities: record-keeping, recruiting, benefits administration or as the legislation compliance arm of the government that sits in your organization. There is an element of truth to this. No organization could exist for very long without some attention to those things, however you think this is all HR is you simply have not been exposed to HR in a successful, large, modern organization. The need for a dedicated HR function, historically, and for any given organization, occurs when we start to go from something small – where you can know everybody and everything that is going on in an organization - to something big – when it is no longer possible for one person to know everybody and everything that is going on. If at some point no one person can see clearly what is actually going on the organization will grow into chaos. The reaction to chaos now and forever will be, “My God we need to get control over this.” e.g. Ultimately successful organizations eventually arrive at, "We need a Human Resource function". At its essence, HR is about controlling the chaos of organization growth. Control doesn’t sound very exciting. A more exciting way of framing it is, if your investors wishes comes true (growth), then Human Resource Management is your reward. HR is a necessity as a consequence of growth, and growth is the primary way businesses derive value for shareholders over the long run, then it might be accurate to describe HR as both a consequence of growth and a cause. Yes, just as eggs and chickens go, so goes HR. It is a great mystery, however in the case Kentucky Fried Chicken I will abdicate that I think the fried chicken came first, and then HR came along later. The idea that having an HR function may be a good thing for an organization begins with the need to create efficient processes to expand the business and eventually evolves into a more expansive appreciation for what intelligent employees and management expect out of an organization. Tending to The Employment Relationship The main thing everybody in an organization has in common is that we are all getting paid– that makes it a job - otherwise we probably wouldn’t be together 8 hours per day, 5 days per week, 50 (+/-2) weeks per year or whatever you do. You may like each other but you probably don’t like each other that much! The other thing we all have in common is that we are at this job voluntarily – beyond acknowledgement that the dissolution of direct slavery is a good thing – we can all thankfully acknowledge labor markets can sometimes be competitive and therefore opportunities for us abound. There are two sides to the equation and both sides make choices. The relationship between an employee and an employer is a rich interaction that can be understood through many different lenses (Psychology, Sociology, Social Psychology, Labor Economics, Anthropology, etc.) however these interactions boil down to decisions and consequences. Decisions are at the crux of our interaction. The leadership of an organization can truly decide to treat people however they want to but they don’t get to decide if people come or go as a consequence – the people decide this. The people also decide the level of creativity and effort they are willing to exert on behalf of an organization. In the short term, none of these interactions may appear to matter - in the long-term it is clear they always do. Achieving clarity in decisions to achieve desired outcomes is the entire point of management. HR wants a seat at the business table, but the truth is they were silent participants all along. Strategic Human Resource Management After we get the basic blocking and tackling of organization under control modern Human Resource Management extends into the macro-concerns of the organization regarding structure, quality of talent, culture, values, matching resources to future needs and other longer-term people issues related to the organization’s plans – we call this group of activities Strategic Human Resource Management. Strategic HRM gives direction on how to build the foundation for strategic advantage by creating an effective organizational structure and design, employee value proposition, systems thinking problem diagnosis, and preparing an organization for a changing landscape, which include new competition, downturns and mergers & acquisitions. Sustainability, diversity, corporate social responsibility, culture and communication also fit within the ambit of Strategic HRM by reflecting chosen organizational values and their expression in business decision-making. (loose credit to the Society of Human Resource Management for this) If that two paragraph description of Strategic HRM sounds like something straight out of a textbook, that is because it probably was. You and I both wish I would have a better mental firewall. Basically, I’m saying that if you are doing it right the purpose of HR is not really about specific activities or compliance; it is about enabling business growth and as you do that it about designing an organization for competitive advantage. Good HR should help extend the life of organizations by helping them extend the reach of what they do or grow better at what they do over time. Here is a brutal fact : over the long run most organizations fail If over the long run most organizations fail – are you getting a clear picture of how good of a job we are do with this strategic HRM stuff? Then again, maybe it is not HR’s fault, maybe HR had great things to say that were not heard. What is the goal of People Analytics? In 3 words : do HR better. In more words: help leaders and employees make better decisions together – reinforcing organization based competitive advantages - which result in sustained organization growth over time. What is the job of People Analytics? People Analytics provides a means to see and explain what is going on inside of an organization. People Analytics provides a framework to give HR’s disjointed practices a reason, coherence and direction. People Analytics also gives HR a more powerful language to communicate with others. Does People Analytics Work? Here is an interesting 18 minute macro way of answering this question in the form of a Ted Talk : The Surprising math of Cities and Corporations : by Geoffrey West Or you can formulate your own answer in less time than that thinking about the following questions… What is it worth to you, to find out what characteristics in a manager lead to a statistically better performing software engineering or sales team? What is the ROI of using math and science to identify a poor manager, that un-checked could influence the organization to do things that result in devastating class action litigation against your company? How much more likely is a person to be motivated to react to criticism of their management style when presented with data that “We asked the same questions of all managers and you are in the bottom 25th percentile on these measures – please explain”? If you believe that the best hiring criteria for success in your organization is intelligence, represented by an Ivy League education, believing they are more successful, and that is not actually true, what is that worth to you? How much additional do you pay for a graduate of an Ivy League Education x number of employees over time? Further, what additional costs to business performance about being wrong about what makes people successful in an organization? Not to mention, what possible business drags and penalties will accumulate when your organization grows less statistically representative of its community as a result of a faulty premise? What is it worth to you to discover that a many million-dollar Benefit program actually does not relate at all to retain employees when that was the primary reason cited for implementing that program? What is it worth to you, to discover that a 10 million dollar benefits program now, will result in a 100 million benefits program later if your organization continues to grow on the same track? What is it worth to you to avoid the consequence to commitment and morale of offering a benefit and not being able to deliver on it, or slowly taking it back over time? Does the current gain in morale from implementing this program, exceed the future consequences of retracting it? What is the ratio of value to the organization? What is the ROI for a hospital to have a 12.5% 1st Year Tenure Attrition Rate versus 25%? What is the difference in patient satisfaction or outcomes? What is the potential cost of Nurse mistakes? What is the reduction in probability of mistakes by reducing the 1st year turnover rate? … Based on my experience with the question above and real organization data, the question “Does People Analytics Work?” is an absurd one. Would you dream of asking a mathematician, “Does math work?” or asking a scientist, “Does science work?”, or an engineer, “Does engineering work?” or a doctor, “Does medicine work?” Someday I hope we will move past this question for People Analytics. ---------------------------------------------------------------------------------------- #PAQA = People Analytics Question and Answer Series What is People Analytics? What is not People Analytics? Why People Analytics? What is the history of People Analytics? What are the key questions of People Analytics? What is the actual work of People Analytics? What are the alternatives to People Analytics? What is the technology of People Analytics? Feel free to suggest ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model. One Model takes data from numerous systems and organizes it so that you can measure, predict and influence workforce behavior to effect change. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin

    Read Article

    Featured
    5 min read
    Mike West

    #1 Reason People Quit Google and What They Are Doing About It

    # 1 Reason people leave google: to connect with their personal calling to change the world. Ironically, it is also the number one reason people join Google. To be fair, I'm a data guy and I don't have access to their data anymore - so consider these an educated guess. It's great that I haven't seen the data in a long time (fuzzy) because I can speculate without assertions I am sharing secrets. Most importantly you can read the things that Laszlo Bock has to say about what attracts, motivates and retains employees directly from him. I do not believe I am off message at all. Across a number of People data projects we get a big theme : you best attract, engage and retain people by connecting with their passion. The role of the company is to leverage assets to provide support to help people materialize dreams because some of those crazy bets are going to someday hit. Get out of the way Google's head of Engineering, Alan Eustace, once said, "One thing is certain about the next big idea, I probably won't have it." (I paraphrase from memory). They work hard to attract, engage and retain talented employees for this reason. They pay more than lip service to these ideas - they invest big in them. As a result of this effort they have much lower average overall turnover than most companies, and really really low key employee turnover, however even Google can't keep them all. Keeping them all might not even be the right goal (for them, the company or for the world). Here is a human story I worked with the now CEO of Omada Health, Sean Duffy, years ago in the Google People Analytics group. We were analysts. I recall the moment when was accepted to Harvard Medical. He had excitement on his face, I asked him what was going on and he showed me the letter. Looking in his eyes I could see it and said, "I'd bet a million dollars you take it". I was right and I was wrong. He went, but soon thereafter he met up with some other guys and they decided to start a company together. They have been working on it for over 5 years, they have iterated, they raised a lot of money from VCs, and I think they have something. Most importantly I believe they have a unique business model. If I understand their business correctly they don't take money for services up front. They get paid by changing population health outcomes, or if not that precisely, at least there is some kind of reconciliation based on outcomes. They can make money from interested insurance companies, large employers and now, it sounds, from Medicaid/Medicare. This might just be the future of medicine. Historically Google has hired people who were overqualified for their roles : a.) because Google is attractive enough that they can, and b.) because they were betting on the future of this person with the company, not a temporary functional skill-set. The result of that is that you end up with too many individuals in small roles who are chasing too few big roles and you get a jammed up talent funnel. If you are stuck in small thinking, there is no solution. You choose winners and losers. You acknowledge that some of your good people are going to go, and that's o.k. You wish them well and try to maintain good relations through a strong alumni network. However, Google doesn't think small. The most interesting thing for me is to watch the big business changes at Google that I believe may directly stem from people data. One word, many letters : Alphabet. What other company would do this? Crazy? No way. The beauty of Alphabet is many fold. By splintering into many parts Google reaps the following benefits: 1.) Increasing the number of available internal paths to big roles, 2.) clarification of decision making, and 3.) clarification of impact - on an entity level but on an individual level. Opportunity can expand infinitely if you think big. Opportunity + Talent + Resources = an economic engine for the future. Don't take my word, here is what Google Chief Finance Officer said himself : “This belief was the impetus for our organizational structure, which enhances focus on opportunities within Google and across Alphabet, while also pushing our leadership to extend the frontier that we are addressing,” said Alphabet’s chief finance officer, Ruth Porat, in the company’s last earnings call." This quote is taken from the The Atlantic's, "Alphabet, Jigsaw, and the Puzzle of Google's New Brand". Stop a minute and think about this. Google's Chief Finance Officer is talking about organization structure and leadership opportunities as a core component of business strategy. Yes, that just happened. No doubt about it Google's Management team is different. They are also smart, they mean business AND they get people. This is The_New_HR. Say what you will about Google, I believe they have this people growth driver thing pretty much wrapped up if other companies don't get on it fast. Alphabet will allow Google to take apart any industry they want to. Consider this fair warning.

    Read Article

    Featured
    15 min read
    Mike West

    My Learning From People Analytics & The Future Of Work #PAFOW

    I spent four days in San Francisco visiting People Analytics professionals and attending Al Adamsen's Talent Strategy Institute : People Analytics and Future of Work Conference. I am deeply grateful for Al's work, that of the conference speakers, and those people working in the space that were willing to allow me to come by, drink their coffee and chat. Here is what I learned. Sometime last year a Deloitte report indicated that growth in People Analytics had plateaued or was slowing, however, at the conference this week Josh Bersin @ Deloitte suggested that over night we have moved into the "Tornado" on Geoffrey Moore's Crossing the Chasm diagram. Basically, I will paraphrase Josh by saying the long winter is over, the birds are chirping but the sleeping giants have awoken. Another way of putting this is that is that People Analytics has begun the "Tipping Point" of exponential growth and/or that we have started to pass through the infamous "Hockey Stick" point of inflection on a growth curve. Believe me when I say the VC's ears are perking up. Two years ago, when we presented our ideas we got blank stares. It was like presenting at the Elk's Lodge, with less alcohol. Still, they had a point; "who is going to buy it?" We are in a better market for entrepreneurs, but seeing this now everyone else is rushing in too so we all experience something like a tornado until winners and losers are sorted out. If you talk to startup entrepreneurs you get a similar picture, although if you get behind the scenes most will admit some degree of frustration with the market - they are having difficulty connecting with buyers and budgets despite that they have made substantial advances in technology and its possible uses within HR. People Analytics Technologists look at the strange HR customer market in utter disbelief, mirroring how the HR customer looks back. These people need us now more than ever, why aren't they beating down a path to our door? To be realistic, in our winter analogy, I remind, we are still only in February, the Texas Blue Bonnets don't peak until April. Maybe we can go on a walk together then. Regardless of where we are on the curve, the original view of "slowing" or "plateau" was clearly incorrect. The argument digresses into methodology. The Deloitte report suggested growth in People Analytics related titles (on job boards) + their consulting revenue was not what would be expected on an exponential growth path, however this vastly underestimated internal job growth, restructuring and refocusing of staff. It is not a small thing - that may have masked at least a 10x year over year magnitude global increase in investment in People Analytics, in one year. Simultaneously, we see growth at PWC, Hay, KPMG, McKinsey and the rest. This is the nature of this space, we have no sense of the pie or each share. People Analytics related job titles in my network indicate anything less than 2x annual growth estimates are far far far off. 2x growth per year is a more than safe bet. For example, my network of People Analytics friends have expanded by 1000 people in just over the last quarter. We are in a once in a lifetime shift in HR akin to the emergence of Finance from Accounting or Marketing from Sales. There was a time they did not exist - you can't remember it. It is big trend, it will have broad impact on the world, potentially every human being, and we are apart of it. Part of the problem is that to get an accurate estimate of growth we must count roles that can be classified under many different titles (People Analytics, HR Analytics, Workforce Analytics, Workforce Insights, Workforce Intelligence, Talent Analytics). These were all represented at the conference. Additionally, we should consider I/O Psychologists, which may or may not go into a formal People Analytics job title or function but relate to the same underlying trend. I have heard I/O Psychologists are the fastest growing job in the country, with an expected annual growth rate of 53%. I/O Psychologists are 1 out of 4 types of people you will find in these pop up People Analytics groups. When I talk about People Analytics I/O Psychologists put their hands on their hips and roll their eyes at me, "What about us, we have been doing this work in organizations for a long time?". Meanwhile they meet by themselves at their conference, SIOP. If you are not an I/O Psychologist, you probably never heard of it. :-) Love you I/O guys and gals - I just have to take little jabs because I didn't get my phD - it is my own insecurity. We also may or may not be counting folks who sit in an HRIS/HR Technology role that have received a new calling. For many, the focus is shifting from an underlying purpose of organizational HR efficiency to organizational HR effectiveness. The emphasis is changing from "some data, any data", to "how do we get data from here to there?", to the question "What can we do with this data?" We learned that Chevron is well into a multi-year effort to train a distributed network of over 200 HR people on how to support business decisions with data - some of these were previously HRIS analyst positions. None of these people were technically "People Analysts", in a People Analytics usage, and many will never carry this actual title. With no standard professional framework for the function materializing yet (like say Finance or Marketing) it is all very confusing, particularly to those on the outside. Another part of what is going on is technology fragmentation. We see a host of new services and products from names we have never heard before AND we see extensions of old services and products from all the names we have heard before. I think IBM has 12.5 different products relevant to our space. I don't even think people who work for IBM know all their People Analytics related products. :-) In effort to focus on the solution and not the technology, technologists are pretty much all using words now like, "insights", "actionable", "business outcomes", "tell stories with data", etc. such that without looking carefully at the details it is difficult to know what anyone is really doing, why they are different, and why you would go with one over another. Technologists are selling to people that have day jobs and People Analytics typically has never before been one of them - so those who have something to sell are on precarious path. CHROs are inclined to hire and wait. Let's let the new guy (or gal) sort this problem out. The demographic change in HR is exactly what demographic change is to presidential politics. Change takes a while to get going, plays out over decade, not always in the ways we expect, but demographics are the most powerful force for change on earth. Similarly change in the People Analytics space has primarily been in repurposed headcount and where it comes from - The_New_HR pitted against the huddled old HR masses whose only remaining options are to join, resist or spout utter non-sense. In our political skit : Jeb Bush appears overwhelmed with disbelief, Hillary Clinton reminds "I was like you once too", and Donald Trump fights politically correctness with factual incorrectness. Sarah Palin is not in the race but hangs around to entertain us with poetry. With Millennials joining together with GenX in disgust of all that is established, boomers gather in fear, and there either is or should be tear gas everywhere. Sarah Palin, I can write poetry too. Much like the US Republican Presidential debates, it is a crowded field with differing views and plenty of interesting arguments. It is also a complete mess. Indeed, isn't this is the way the whole world changes? Fits and starts, a little discomfort, and then one day you wake up and think, "20 years ago what exactly did we do without the internet?" Millennials, by definition, have no idea. Many of us have already tired of email and Facebook.The Millennials response, "come on everyone, we agree, but let's keep moving." Wasn't this true of every generation before them, just with different technology backdrops? Note the background images of the atomic bombs in our parents generation. I believe I caught the tail end of hiding under the desks bit at school - never mind. Maybe Snapchat and Twitter is not really all that bad after all, not negating the atomic power new ways to communicate have. Absent shared mindspace (among executives, HR and People Analysts) for a dedicated budget the go to pragmatic solutions for HR is: 1.) Hire more people (go figure), 2.) training (go figure) and 3.) try to leverage existing technology until we can figure out our roadmap and make the business case for what else we need to do this thing (go figure). Lately I have had no fight in me for "Gloves Off Friday Posts" - apparently needing to recover from my brutal loss to Excel a few weeks ago. What is the current #1 business tool for analysis in the world? Excel. This is not a scientific assessment, but at the conference the number of negative references to "we had to do this in Excel" or we are "trying to get out of Excel", were something akin to counting "ums" when I speak. "Um, there goes another one." :-) All of the successful examples of People Analytics provided at the conference that I heard were stories of the journey out of Excel. The conference should have been named: "People Analytics And Your Future At Work After Excel". In the field of People Analytics, at the moment, success is found via, "we spent x years and x dollars to get on a scalable technology architecture for People Analytics workflow". In one example provided : 2 years and 3.5 Million dollars: I appreciate the transparency. The stories of this journey provided just at this conference include: Intuit (Michelle Deneau), RackSpace/Tesoro Corp (Robert Lanning), McGraw Hill (Antony Ebelle-Ebanda), Gap (Anthony Walter), GE (Heather Whiteman),... but this is only a small sample. Nobody disagreed. Nobody was shocked. The story is the same everywhere - there are many more interesting things we can do, but there is little way around the journey - wrapping arms around the data is a part of the journey. Confront the brutal facts. It should be mentioned the folks that presented are just the heros, the survivors. I appreciate Tauseef Rahman for pulling me aside and descretely mentioning the problem of "Survivor Bias" - e.g. we do not hear from the failures so we don't know if they might have done the exact same things and got a different result. Survivor bias may be a problem, but unless you go looking around for skeletons you will probably never know for sure. At the moment we have no real measure of success for our field. Success is not measured in terms of speed or cost of implementing a data warehouse and reporting suite, relative to peers. That said there are real differences. Absent any other clear measure of success this might do as a proxy in the beginning. I'm anxious to see what happens with Robert Lanning in his new role at Tesoro - he has a particularly good track record. It is not that Excel and other ad-hoc tools are not useful, it is just that they don't scale with demand after you actually show people what insights it is possible for them to get in our space. Beware of this - they never knew before how powerful this HR Analytics stuff really was - all bets are off when executives finally see it. Also at the conference, someone reminded us of one of Amit Mohindra's (Apple) clever laws, which I paraphrase as "upon exposure, demand for People Analytics related insights increases exponentially" My personal experience is the same. If you believe us, pay careful attention to what this implies. If you do not create a scalable analytical architecture at the outset to meet demand in an equation like T+1 = demand^2, you will fail if you cannot increase resource^2. The math suggests this will probably occur about 1.5-2 years into the role. No worries, opportunities in the space will abound for some time so you will get a second or third chance to get it right at a clean employer. My learning parallels what Ian O'Keefe (next gen Google People Analytics) says, "we must work three problems simultaneously : efficiency, effectiveness, and user experience". Some people are better at managing expectations than I was while working in HR, but in my experience the examples provided indicate this scale problem is a competency for technology, not people. People are for asking good questions, constructing ways of approaching those questions, finding insights, telling stories, consulting, making decisions, etc. --- technology is simply for scale. These are two different sides of the coin that must be managed simultaneously (and well). You can manage expectations with "no", but we are not equally exposed to executives that understand this word - let the conflicts ensue. In my biased, not humbly stated, opinion you must use technology to take basic reporting off the table, but you must simultaneously create means to expose that data to a variety of downstream systems to support a complete analytical workflow : statistical applications for data scientists (R, SPSS, Python), niche people analytics applications to augment (varied), and data innovative visualization applications used by other functions of your businesses and executives (Tableau, Domo...). If your data doesn't join sources AND port to these environments efficiently, I don't care how good it is now, you are going to get stuck somewhere. If you are addressing the problem with people your people needs/costs are going to need to expand exponentially with demand. If you do not have an exponential budget (e.g. think Google) and don't have a scalable architecture for repeatability and change, then you have real trouble. Other troubles. If you can't get data into advanced statistical models you will bore and be eventually replaced. If you can't communicate in tomorrow's visual frameworks you will be dismissed. There is no application that is simultaneously best at all desired functions, or after achieving that mountain will be able to maintain a lead for long, so consider carefully how each technology extends and connects with the other technologies, what technology providers are most facilitative of partnerships, and what the support model looks like. Who is going to do the work to support changes, when, do they fall under your authority to control? Pay attention to all the tiny little details. ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. If you have interest in participating in the webinar series, let me know here: (http://www.misc-peopleanalytics.com/lean-series) And if you know anyone else who you think would, please let them know too!

    Read Article

    Featured
    8 min read
    Mike West

    The Work of Freedom is Not Finished : People Analytics and Diversity.

    January 18th, 2016 In the 53 years that has passed since Dr. Martin Luther King Jr. delivered his I Have a Dream speech America has clearly come a long way, however the work of freedom is not finished. If you listen carefully the words of Dr. Martin Luther King's speech are still as relevant today as ever. While he was speaking primarily to obvious injustices against people of color at in his time - and some of these measures have progressed - specifically, black people suffer less lynchings and can actually directly influence the political process - yet, upon close inspection of facts, it is obvious there is a startling and frustrating lack of progress today. On nearly all fundamental metrics of prosperity we see massive pernicious disparity by race. How could we have worked so hard and arrived in a place not really that much different from where we started? I will not insult you with an easy or absolute answer : the 3 step process for racial equality or the 5 reasons things are or aren't as bad as some would claim. Difficult problems cannot be satisfied by the sticky residue of low hanging fruit. I will share my few observations. I don't know if the world has really become more complex, but it certainly seems like it has. Over my lifetime I have worked for over 10 different organizations in professional and nonprofessional roles and have never met a blatant racist at work (and very few even in my personal life). How much easier it would be to confront the obvious ignorance of racism directly. This type of problem seems it could be tidied up promptly. However that is not our work today. The problem we are dealing with is much much more insidious than this. I can find no racists, but of "unconscious bias" I could dredge up volumes for you. Here is a tidy summary published by the New York Times : Racial Bias, Even When We Have Good Intentions. Lacking an actual human descriptor - we are fighting an elusive thief that we are forced to call some inhuman name "unconscious bias". Who believes in something you cannot see and a story you cannot tell. Imagine the frustration of toiling hard each day, stockpiling the fruit of your labor for tomorrow's meal and finding each night some unknown stranger steals it you in the quiet of the night. You have no way of catching, identifying or accusing this stranger and nobody believes you. Your lack of food must be explained by something else - perhaps you are lazy. You have spoken about it for so long, and this injustice so unbelievable that sometimes even you wonder if you have gone crazy. This is a description of the actual horror of unconscious bias. One of the things we are doing now, different than in the past is that we are beginning to face this thief directly. It turns out that clever people have devised clever ways of actually catching the thief in the act. Here is a video of great work conducted by People Analytics at Google, speaker is Brian Welle, a former colleague of mine : https://youtu.be/nLjFTHTgEVU . Brian will blow your mind. Fundamentally, at its essence People Analytics is about using clever research methods and data to reduce mistakes of human bias, because bias causes us to make worse decisions for our business than we would have made with a more perfect understanding of truth. It was only a matter of time that People Analytics would turn its attention directly upon matters of diversity too. While justifiable in its own right as an effort for "fairness" or for "the law", but we actually don't stand against bias at work just for these reasons - we actually benefit from truth too. This is not benevolence or charity - that demeans it . The most astounding thing about working on issues of bias is that when we make decisions with less bias we benefit directly too! If you did not get this point from your reading of the book "Moneyball", or watching of the movie, go back and watch it again - you missed an important detail. They did not do this "diversity thing" because we feel sorry for people who look different or throw the ball weird - it turns out that if you like winning people who throw the ball weird might make great teammates! This is the beauty of all things good and eternal. In truth there is actually no threat to anyone. Open up the door, let every truth come in. The house only expands. We may never reach a place of perfect truth or perfect answers on this earth, but as I am reminded by Dr. Martin Luther King Jr. I too refuse to believe that there are insufficient funds in the great vaults of opportunity of this nation (as he addressed the United States of America at Washington DC in 1963) I leave you with his words, which even 50 years later, never fail to bring tears to my eyes. We are not where we wanted to be, but Dr. Martin Luther King Jr. is no less prophet if we open our minds, hearts and ears. "In a sense we've come to our nation's capital to cash a check. When the architects of our republic wrote the magnificent words of the Constitution and the Declaration of Independence, they were signing a promissory note to which every American was to fall heir. This note was a promise that all men, yes, black men as well as white men, would be guaranteed the "unalienable Rights" of "Life, Liberty and the pursuit of Happiness." It is obvious today that America has defaulted on this promissory note, insofar as her citizens of color are concerned. Instead of honoring this sacred obligation, America has given the Negro people a bad check, a check which has come back marked "insufficient funds." But we refuse to believe that the bank of justice is bankrupt. We refuse to believe that there are insufficient funds in the great vaults of opportunity of this nation. And so, we've come to cash this check, a check that will give us upon demand the riches of freedom and the security of justice." - Dr. Martin Luther King Jr. Full I Have a Dream Speech : https://youtu.be/I47Y6VHc3Ms ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. If you have interest in participating in the webinar series, let me know here: (http://www.misc-peopleanalytics.com/lean-series) And if you know anyone else who you think would, please let them know too!

    Read Article

    Featured
    17 min read
    Mike West

    The Most Dangerous Technology in HR Today

    Did you think I'd crumble? Did you think I'd lay down and die? Oh no, not I. I will survive. - Gloria Gaynor, "I will Survive". 2002, White House Station, New Jersey, Merck, Organizational Learning I am helping to organize a launch meeting for our new Performance Management process and we need to stand up a website. I studied Sociology and Psychology and finally Human Resource Management with a natural penchant for Abstract Mathematics. My skills don't extend into HTML - in 2002 we didn't have SquareSpace - At Merck we were lucky we had Internet Explorer installed. I was invited to go talk to this guy in IT about creating a website for the event. For reasons I will describe, I'll never forget this meeting. To protect his identity I will call him "Stan". I get to Stan's desk area/cube. First, I see this row of stuffed animals. Let me just stop here and provide context. At Merck, at this time, we wore suits and ties to work every day – presumably in case a member of Congress, a foreign dignitary or a nobel laureate just stopped into the office. We were East Coast Pharma. We were a 100 year old organization and one of the most productive research institutions in the world. In our minds, we saved lives and we put billions of dollars in the economy, and we proud of both - we were buttoned up. Guys like me wore single-breasted jackets. If you were at the top your suit was double, maybe triple breasted. I don't recall what Stan was wearing precisely, but by the row of stuffed animals on his desk, I knew this guy was a strange animal. I was curious how Stan got away with this. Stan told me to come in, pull up a chair and sit down. I sat next to him in his cubicle, side by side staring at a couple of computer monitors, stuffed animals behind me. He had at least three computer boxes. He asked, “what do you want ?” As I recall, at the same time I was speaking he was typing in HTML, occasionally with diversions to chat while he kept typing. With swift keyboard strokes he would move seamlessly between computers, moving things from a design environment, to test, to production. Somewhere in this exchange I thought, "I don't care about the stuffed animals, this guy is pretty cool." I thought in my mind - if I ever go start a company this is the first guy I would take. For fear of losing access to him I told no one else about this. I don't know if I actually ever said this to Stan directly. Time went on, and Stan and I went on our separate ways. A few times after I left Merck I tried to engage Stan in projects I had going, but it has never worked out. I don't know if he kept up with changing technology or not. My learning from Stan is that “most of us use computers; some of us use them differently.” Excel - “most of us have used Excel; some of us use it differently.” I share the story about “Stan” because it makes what I am going to say about Excel just a little more vivid: “most of us have used Excel; some of us use it differently.” I want you to understand the degree to which I understand, appreciate (and even love) Excel before I describe why it may be the most dangerous business application of all time. This story about Stan with HTML foreshadowed how I would some day use Excel. It didn't come easy. Nobody taught me how to work with HR data in Excel. The way I use Excel is different than most people I have ever observed working in Excel. Granted I got there somewhere after spending over 10,000 hours on it over 15 years. I sit sheets like data stores, with pivot tables that feed into lists that other tables sit on, and lookup functions that move data around, transform it into whatever I need it to be, that feed into downstream analysis, and finally, charts. I have figured out how to do multidimensional reporting in Excel. I work data through recursive algorithms in Excel. I use add-ons to run a variety of statistics. I take the charts provided in Excel and strip them down to the bones, rebuilding them into something beautiful, apparently never imagined by Microsoft. When I am working with Excel, I often don't even see the detail of the data – at times I might as well be operating in another dimension. Having said all this, I'm sure there are people who have even more advanced Excel skills than myself. I'm not saying I'm the greatest - simply that there are differences between how people use Excel, so when we talk about Excel it is important to keep this in mind. Excel can be a very powerful application. 2006, Mountain View California, Google, People Analytics There was a time at Google where I was working with employee data in Excel and developing ways to run out reports by all segments we had in the data. I had spent probably 150 hours working iterating report trials off this dataset over several weeks. I started on the Google bus on the way into the office as I travelled from Berkeley to Mountain View, worked all day at Mountain View, stopping only for food and coffee, then continued working on the Google bus on the way home at night, collapsing into bed when I arrived, and starting over again the next day. At one point, while working with the structure of the data, reaching that point just before it was ready to share and it was like the sun rising on a very dark night. In some way it was like seeing God. That or I had reached the ceiling for caffeine consumption - one or the other. If I did not see God at the very least experienced what it must be like to see pure truth. The best example I can provide of what this is like, it is like standing on the edge of the Grand Canyon or an ocean or looking at the stars in complete darkness on a crystal clear night. I have not since recreated a feeling this vivid at work - apparently I lost track of the specific Excel function for this :-) - but what remains with me today is an appreciation that there is a truth encapsulated in data and a beauty in its mathematical structure, which also happens to be powerful. If you spend enough time in it, you might see it. Don’t take my word for this, here representative thought from a series of quotes about the beauty of mathematics: “It seems to me now that mathematics is capable of an artistic excellence as great as that of any music, perhaps greater; not because the pleasure it gives (although very pure) is comparable, either in intensity or in the number of people who feel it, to that of music, but because it gives in absolute perfection that combination, characteristic of great art, of godlike freedom, with the sense of inevitable destiny; because, in fact, it constructs an ideal world where everything is perfect but true.” Bertrand Russell (1872-1970), Autobiography Thoughts about the beauty of mathematics The way that Excel is different from most other applications for working with data is that in Excel, you can actually directly see the data you are working with. There are a number of other reasons why Excel is the most used business application of all time, but I won’t bore you with the nuances. This quote sums it up: “Microsoft Excel is one of the greatest, most powerful, most important software applications of all time. Many in the industry will no doubt object, but it provides enormous capacity to do quantitative analysis, letting you do anything from statistical analyses of databases with hundreds of thousands of records to complex estimation tools with user-friendly front ends. And unlike traditional statistical programs, it provides an intuitive interface that lets you see what happens to the data as you manipulate them” (The Importance of Excel) I love my friend excel, but I'm about to shake his hand and then pummel him. The main argument against Excel has been that the things that make Excel great are also its biggest downside. First, Data Quality: Excel makes it too easy for people to make mistakes Excel makes it too easy for people to lie For starters, while it is incredibly easy to get started making spreadsheets, it’s also incredibly easy to make mistakes that cost companies millions (or billions) of dollars. In 2008, University of Hawai’i professor Raymond Panko published a summary of 13 field audits that checked spreadsheets used in ‘real-world’ environments. His analysis found that a whopping 88% of the spreadsheets had errors! In evaluating possible solutions to the spreadsheet errors he described in his 2008 paper, Professor Panko wrote: “… few spreadsheet developers have spreadsheeting in their job descriptions at all, and very few do spreadsheet development as their main task.” One problem is that since everybody has at least some knowledge of how to use Excel, many people misjudge their own expertise, as well as that of others. This is different from when how we hire and judge software developers. Business managers don't know that there is a problem (actually, lots of problems) with spreadsheets, while IT regards spreadsheets as falling outside its jurisdiction. So spreadsheet management falls into a black hole. While Excel the program is reasonably robust, the spreadsheets that people create with Excel are fragile. There is no way to trace where the data came from, when, and what was done to. The biggest problem is that anyone can create Excel spreadsheets—badly. Because it’s so easy to use, the creation of even important spreadsheets is not restricted to people who understand programming and do it in a methodical, documented way. There are a number of public examples of Excel mistakes, some with substantial impact 2012, London, "The London Whale" The problems of Excel apply to anything and anyone who’s working with data in Excel, not just HR. Here is a description of the particularly high impact example at JP Morgan: Microsoft Excel Might Be The Most Dangerous Software on the Planet. “After the London Whale trade blew up, the Model Review Group discovered that the model had not been automated and found several other errors. Most spectacularly, “After subtracting the old rate from the new rate, the spreadsheet divided by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a factor of two and of lowering the VaR . . .” The explanation in English: someone at JP Morgan was running bets (to the tune of tens of billions of dollars) in Excel and there was an error. There may have been other negligence or nefariousness going on as well, but I found the most outrageous part of the story that this sophisticated derivate work was completed in Excel in the first place. Stupefying actually. At the time I was getting paid 1000 times less money to do similar work in Human Resources- you have to ask yourself, "Maybe I should have considered a different profession?" "I could totally screw up derivatives, maybe even 1/3 as bad as this "whale guy". The other ways that Excel falls down are: Difficulty seeing workflow (e.g. how the data goes through stages) Difficulty documenting workflow, process audit trail. Difficulty with dependence - difficulty transitioning spreadsheets from one person to the next. Stale data and/or constant rework as a result of stale data. Difficult to see the real cost of manual work in Excel being performed across the organization. Inability to secure data. 2013, Austin, Texas, ACMETech, Workforce Analytics Very few people know that between the time that I worked at Children’s Medical in Dallas and started my own consulting company, two and a half years prior to joining One Model, I worked for a technology company in Austin. Let’s call them AcmeTech. AcmeTech lured me from a children’s hospital in Dallas with higher pay, better benefits, a well stocked micro kitchen, free lunch on Fridays and a ping pong table. I felt bad leaving the children. Little did I know at the time, as a result of this decision I was going to hell. When I interviewed with AcmeTech I described the important analytical work I had done in HR at Merck, PetSmart, Google and even at a very modestly funded non profit children’s hospital. The emphasis of this work was in automating analytical workflow AND then spending my time in more sophisticated and high value analysis like exit prediction models. I thought we were on the same page. Well, I started with ACMETech and soon learned that this, in fact, we were not on the same page. I was expected to create weekly HR reports for the division I supported in Excel. We were dumping data out of WorkDay into Excel, aggregating into metrics and reporting by segment. I have a history creating prototype reports just like this in Excel but these always were temporary, not long term, solutions. At ACMETech this is something they had been doing for years and there were some unique nuances to the way they were doing it that prevented full automation. My predecessor diligently showed me how to copy data from one sheet to another, change a series of things (to be recalled by notes or by memory), check for these other things that may or may not go wrong, then publish the reports out by email. A single report would take me a full day to complete and there were several different versions of these reports for different stakeholders. There were a dozens points of possible failure. There were five of us on this team doing the exact same reports for different divisions. When I raised this issue to my manager and my ‘managers manager’ I was told, “We want you to keep doing the reports the same way they were done by your predecessor.” This is my problem: somehow I had gone from at one point of the time one of the most brilliant People Analytics teams in the world to now something slightly above “human cog” in a car headed to nowhere, driving off a cliff. Here were my views on ACMETech's Excel Based Workforce Analytics Process: There was not much value in these reports as produced, relative to other work we could be completing on behalf of the organization. The incredible waste of time and money in this approach - not to mention life effort. When things eventually went wrong or people were through with this it would be our neck on the line (in the case of my division my neck specifically) The way we were running these reports were affecting the quality of experience for the recipients of the reports. I knew there were better ways that would save ACMETech HR time, money and credibility that could be put in place very quickly. My mind just didn’t work the way we were running these reports - I could operate more effectively in other capacities. At the end of the day I went back and said, “You hired me for my expertise, there is a better way.” The reply was, “Do it our way or leave”. My reply, “o.k. then”. That’s when I decided to go start my own company. I wanted to work with people who really wanted to work with me, or not at all. I acknowledge that ACMETech HR can choose to do whatever ACMETech HR wants to do and that is fine, but if you look at what they were spending each year on headcount, turnover and hire reports for their organization it was at least $500,000 and if you calculate that over 5 years they have spent at least 2.5 million for a very basic reporting framework - without any real semblance of advanced analytics we know as People Analytics and full of holes. This, my friends, is why Excel in HR is dangerous, and a great case study for why you should consider an alternative solution for analysis and reporting! There are a variety of options today, One of which is One Model - which in full disclosure I recently joined - I'm a little biased. Other options are out there: seeWhat Your HR Analytics Technology Sales Rep Doesn't Want You to Know. ---------------------------------------------------------------------------------------- Disclosures: This is a "Gloves Off Friday" post Mike West is a bad man Mike West writes way too much about People Analytics Mike West is currently VP of Product Strategy for One Model ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose.

    Read Article

    Featured
    8 min read
    Mike West

    5 Reasons HR Analytics Efforts Stall and a New Way Forward

    "Life is a struggle, and then you die" So go make something of it. Work on something important and watch over these things. The 5 Reasons Most HR Analytics Efforts Stall: #1. Not having enough precision on what is the right problem to focus and what questions you need to answer to solve that problem. The typical fail is that you will spend a huge amount of time, money and effort to get a HR reporting environment set-up but downstream users do not use them. People say the information is nice to have - they just don’t have time to go look at your reports. Sometimes the problem is that the information has little relationship to important decisions, or little bearing on the work that anyone is doing. Often the people supported will request an infinite assortment trivial changes in the desperate hope that each change will produce a better result. Or with no specific reason provided you and your solution just go from hero to old hat over night - and you are left to wonder what actually went wrong. The problem is that you spent your resources and time working on the wrong problems and questions. More could have been accomplished with your time and effort had clarity been achieved at the outset. #2. Not having all the right data you need to answer the questions you want to answer. The worst possible outcome of analysis is to produce a statistically significant finding that increases certainty in a wrong answer. This is a common outcome of the “missing variable problem” (the unknown unknowns that wreck most analysis) This is some portion of the 80% of the variance your model did not explain. You ran the analysis but did not include the right control data, so you get an answer, but you get a wrong answer, and you have no way of knowing you got a wrong answer. Sound like a nightmare? This is not a nightmare, this is a real problem. The second worst possible outcome is when you do all the work and don’t achieve a statistically significant finding but could have produced a finding if you had included the right variables. In either case, not having a basic theory that would explain what variables to include in the analysis results in you never achieving a satisfactory outcome from your effort or you may double or triple the hours to reach a conclusive answer because many passes are required. These problems are why we pay university scientists the big bucks. Big Bucks!? O.K., not really! University scientists try harder because they know their work will be 'Peer Reviewed' by other really smart people who also know something about this topic. We don’t have this check inside organizations - we have non experts reviewing the work of experts. Major danger. #3. Expecting technology to solve the whole problem (absent analysts). You have made an important investment in supporting technology, but you may not get anything of lasting value out of this investment because you did not factor in the cost of acquiring (or creating) skilled operators of this technology. It is as if you have this wonderful piece of machinery sitting in idle. The success you have with analytics is dependent on the experience and preparation of the people working the analysis. You can achieve two different analysis outcomes with two different analysts, using the same technology! The worst part - if you get it wrong, how would you even know? Between different analysts you will find different choices about what data to include, chosen research method, as well as differences in skill in executing chosen method. Clearly, you need to think about analysts, but you must also think about the rest of the organization. It does not help you to have this group of "really smart data people” and nobody else who knows how to use their work. You need everyone on the same page on where we are trying to go, what everyone's role is, and how it all fits together. #4. Expecting your analyst to solve the whole problem (absent the right technology & support). Analysts are evaluated based on results. Some other HR roles can produce activities (implementing programs, policies, processes and systems) and we declare victory at the conclusion of the activity without respect to impact (which conveniently is never measured). Success is defined as completion of the project on budget and on time, then on to the next. Analysts do not have this privilege! For Analysts, the proof is in the pudding. If you tell the truth, "based on our data and the tools I have I found nothing of lasting significance to you" - your reward is you don't get invited back to the meeting. Analysts either produce very little value and stick around (satisfied with the activity for pay, as opposed to outcomes) or they leave for another opportunity to do better analysis. They either have a fire in their eye or they don't. You want the one who cares, or don't bother even starting. You have made an important investment in a person, but you can get nothing of lasting value out of this person without providing the tools and support they need to complete their work. Managing an environment like this is difficult, but not impossible - it requires skill and care. #5. Expecting results without someone putting in hard work. Your typical project management wisdom applies - choose one out of three : time, quality or cost. Every new question you want to answer will involve investment in new data collection, cleanup, transport, joining, reshaping, statistics and figuring out how to best communicate the results. We inevitably want automation of routine analytical workflow, but there is a first and second priority constraint : what should be made routine? How can it be made routine? We (technologists) will try our best to design out of this, but the first pass is best handled by a human - this will be hard to get around. It will fail if no one has put in the work. This doesn’t necessarily mean you have to do all the work - or even the hard work - just that somebody does and there is no way to escape this cost. You can bring in consultants to do the work, you can hire enough people in your organizations to do the work, or you can buy packaged solutions that help with part of the work. In this you will be making big trade offs on time, quality of delivery and cost. Beware - no silver bullet will kill this beast. You must make a commitment to ongoing refinement of the analytical process or you get an analytical process that really does nothing for you. If you get into the real day-to-day work of HR Analytics, the People Analysts are dealing with data that generated for some other purpose that does not conform to basic needs and expectations of our existing purpose. The best way to understand what must be done to automate an analytics workflow is to have someone work through the analysis one time to understand what data is there, what is not, what is wrong, and figure how what is there must be improved for successful analysis to occur. Often we implement expensive reporting solutions on the hope that these will produce useful insights. Why invest in automation (repeatability) if you can not achieve a useful finding through a manual effort? Hope? Hope is a great attitude to apply to all situations, but not a great strategy. Why not run through it manually one time and figure out if it is worth automating? The most important question is - when you got to the end of it all manually, do you end up with a report that is useful to the organization? If you did, great, now is the right time to make decision about automation. ---------------------------------------------------------------------------------------- People Analytics is difficult, no doubt about that, but... I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems.

    Read Article

    Featured
    15 min read
    Mike West

    What Your HR Analytics Technology Sales Rep Doesn't Want You To Know

    “This the real world, homie, school finished. They done stole your dreams, you dunno who did it.” Kayne West (no relation) A New People Analytics Blog Series : Gloves Off Friday! At the moment, in People Analytics I see a lot of the same keywords repeated over and over. Two examples: insights and storytelling… I find myself thinking: It is great you are using the right words. These are what we want out of our analytics system, and great claims, but what do these words really have to do with your application today? Are these words real or are they hype? The first hard truth is this: most of the time enterprise analytics system keywords are just marketing hype. Sell the people what they want to be sold. Keep in mind if an existing system has any real market traction it probably was designed well before we even started using the keywords we use today. If the product was designed 5-10 years ago, you might ask: so really, what has changed? Are these just old solutions packaged a new way? If these systems have existed all this time, why are they suddenly going to help HR in a different way now that they didn’t in the past?” My suggestion is this - when you peer under the hood of a reporting system look at the following: 1.) make sure you are finding what would constitute viable insights or stories to substantiate the insights and storytelling claim. 2.) make sure you can find examples of insights you could only derive in this system (not something you could substitute almost any other system of the same family for to achieve the same result) 3.) make sure that the way these insights are achieved will work in the real world (that it based on clear and accurate assumptions and with a process that is scalable) I’m might be going out on a limb by myself, and maybe I could get fired for saying this, but I will stand on this view: at the moment, insights and stories are mostly functions of human beings – not systems! Let me say it again: insights and stories have mostly to do with the observations of the operators of systems, analysts; little to do with the systems themselves. My position could change someday, but for now I’m not holding my breath. Instead, when you review a potential solution you should ask better questions: How does this application enable my analysts to derive insights in a different & better way than any other application? What do analysts think about this application? The second hard truth is that the problem addressed by the analytics systems of today is the efficiency of the analyst at producing the insight, not the insights. Despite differences in features and presentation, there really is not a lot of meaningful difference between most broad use case enterprise reporting applications in the range of possible insights produced. If you see that the essence of the problem these application are designed to solve is the efficiency of insight production, you will know how to better prioritize your decision. By nature the principles that underlie the use of technology are: automation, repeatability & scalability. How does this application support automation, repeatability & scalability of the analytical process? Where does the data come from? How is the data loaded? How do we deal with the unique nuances of our organization and quirkiness of our data? What do I do when we change our underlying systems? How can this application adjust to changes? What level of expertise is required to do the work? Who will do the work? Pay very careful attention to those features that promote a sustainable path to insights (with less manual work per insight produced) - producing ongoing long-term efficiencies in the analytics process. The devil is in the details. Ask the people who do the work what they think. This statement is not intended to overemphasize the importance of efficiency over all else– I’m just saying let’s be clear about what real problem we are solving for when we implement a technology system – the alternative is a disaster waiting to happen. The system will eventually roll out and it will eventually be held to the standard of how it was promoted. What I mean is this : if the assumption is that after this is implemented there will be no analysts required, does the system actually produce insights without an analyst? If the major difference in this application is how it can bypass the analyst to get the insight directly to the downstream user, you must observe, does the downstream user take on the work to go in to see those reports? Do the reports viewed by the downstream user translate directly into any useful action? Is the insight better than what could be achieved with the assistance of a specialized analyst. Be honest. If the answer to any of these questions is no, but you sold it as so, you will be tearing that solution out in a few years. Fool me once_________, Fool me twice _________? The third hard truth is that THERE ARE REAL DIFFERENCES in how the enterprise analytics systems approach efficiency in the production of insights. The first great simplifier - consider, where is the system maintained and delivered? Is it an “On Premise" or “Cloud” solution? The keywords here are: Cloud, Software as a Service (SaaS) This is the first major branch in your decision tree. Cloud and SaaS are not very human words and at this point already feeling a little tired out, but are still very important to pay attention to. There are technical nuances to the definition of cloud but in layman's speak essentially we are getting at is: are all customers on the same instance over the internet (cloud) or does each customer maintain their own instance on their own servers or desktops (on premise)? Software as a Service usually goes together with cloud - this refers primarily to the method of payment. With a SaaS solution you are renting the software, rather than buy it. My opinion. If you are not moving to the cloud “You are going the wrong way!…” For fun, here is a great wrong way clip – https://youtu.be/_akwHYMdbsM - I just love John Candy and Steve Martin together in this movie. For example - there is a reason Google just pulled out their homegrown Human Resource Information System (HRIS) –GoogleHR (GHR), which giant teams of Google engineers had worked on for over 10 years, replacing it with WorkDay, a cloud, Software as a Service (SaaS) HRIS. The reasons are: A.) Your business is XYZ, not whatever this is. Unless Google planned on getting into the HRIS business, Google had to ask, what the hell are we doing fooling around with HRIS? A very good question. B.) You will spend less on a cloud solution than an on premise solution. The cost of cloud infrastructure is spread among all customers, as opposed to a single entity. Fundamental economics, 9 out of 10 times cloud will be a better value than on premise. Also, because you are billed for your use of this software over time, if somewhere down the road you don’t like it you can go with another solution. You just turn it off! This is a lot easier pill to swallow than a PeopleSoft or Oracle implementation used to be. C.) Cloud companies are faster innovators. Cloud companies have a single instance to invest continuous innovation in – consequently they push more frequent updates out to all customers on one platform - therefore they are faster innovators. Google is a cloud company too, this fact is too close to home to miss. Technology is evolving so rapidly - if you buy something that sits on premise (or build it), it will be out of date before it is fully implemented. ------------------------------------------------------------------------------------------- By the way, I’m NOT for or against WorkDay specifically. WorkDay is just an example the overwhelming trend in HR going into the cloud. We were not sure if we wanted our HR data in the cloud at first – now the market is tipping to the cloud dramatically! There are a number of different cloud HRIS product options. Even the old on premise providers (Oracle and SAP) have options that are cloud now. WorkDay is just a working example of an HR application in the Cloud that the market has already wrapped its mind around. -------------------------------------------------------------------------------------------- If you are looking at an enterprise analytics solution that is not in the cloud and not delivered as software a service – and you don’t have a really unique good reason for this - you are probably making a mistake. The next consideration to pay attention to in HOW is : what functional use case or domain was this enterprise analytics system designed for? This is the next big divider – here is where we get into the nuances of important less obvious choices you will be making. The second great simplifier - is this system designed for generalized purposes or specific? This is another major branch in your decision tree. Option A : a generalized analytical system that can theoretically be applied to any analytical problem (but that is not designed specifically for any). Option B : a solution that is designed specifically for a particular domain, customer type, or use case. To use the HRIS example again, back when we were actually debating questions like this you could a.) build your HRIS system (an HR database) on a generalizable database structure – say generic Oracle – or b.) go with a database designed specifically for HR - PeopleSoft, Oracle HR, SAP HR, Lawson HR, Ultimate Software, WorkDay, etc… It took us time to figure this out, but the market decided that buying a system designed for HR is much better. Do you really want your IT team to be learning, following and servicing obscure changes to payroll, compensation or arcane HR needs that ultimately drive database design? That argument was long ago concluded. Overwhelmingly, with no uncertainty, the big girls and boys do not build and service their own HR databases. It turns out that what you are using the database for impacts important design decisions! It also follows out that customizing a generalized solution for an HR purpose is overwhelmingly more expensive over time. (because of labor costs and other problems unforeseen at the outset). Option A : Generalized Analytical System. Option A - Generalized, may on the surface look less expensive because it is a single solution that can applied across many functions, however you have to factor in the labor costs to bend it to the reality of each business function and their needs and maintain that. You won’t want your critical IT and Software Eng. teams working on HR stuff, so don’t do it. HR problems are notoriously needy and difficult. Stay out of it. The big costs in technology are not in software, they are in the design and set-up. For example, I can buy a single license of Tableau for $2000 (+ $100K+ on a Tableau server to distribute that report over the company intranet with security), but to apply Tableau to my HR reporting needs I might actually “spend” $300K on IT labor for build of my ETL (extract, transform, load) and Data Warehouse path, which must occur prior to delivery of the data into Tableau. After this I will probably spend another $100K in labor to get my Tableau dashboard designed to do what I want it to and to look good. Was this actually a $2000 solution? No. Not counting the server (presumably used by other business functions outside HR) the solution actually cost me $402K, and possible a lot more. I will go through those labor costs several times while iterating towards the right set of metrics and reports for HR on a generalized analytical platform. Who will support me when I need to change the ETL? I have been involved in one way or another with these generalized solutions four times over my career: Cognos at Merck, MicroStrategy at PetSmart, MicroStrategy at Google and Tableau at Jawbone. Google simultaneously experimented with Tableau and ClickView and MicroStrategy. There were people at Google who called MicroStrategy, “MicroDisaster” or “MicroSadly”, or (insert your own hilarious Micro explicative). The general consensus was, it actually may be a great solution, we are not sure, but WAY to difficult to implement and WAY complex for the typical user. Keep in mind, this was GOOGLE! Do you know the kind of people they hire? I’m wondering if anyone can figure out MicroStrategy? Sad indeed. In some regards, Tableau and ClickView is designed to be more accessible but get into the nuances of Tableau and ClickView you will see they are going down the same path. Arcane nuances. Sub menu within sub menu. Flip this little switch in submenu 24, under the heading of a new word we invented, and then the report will work right… Seriously, that is your solution? Tableau promotes this as a simple solution, FOR EVERYONE. I'm holding them to it. Do you know what Tableau Professional Support Services costs per hour? $250 per hour. I once spent $5000 in Tableau Professional Services to find out that if I changed the way the data looked to Tableau on the way in then everything would work right, if I didn’t there was no solution to my problem. Major question - who is going to design and support the right ETL to accommodate this thing I am buying, what does the solution really cost? Choose carefully. I can go down the line but the premise is about the same – you might save costs on software by leveraging the same software across functions of your organization, but you give that back on labor costs bending those applications into your functional needs and use cases. It may not be a silver bullet. It may not be cheaper. It may not be easy. The other non-obvious consideration here is that if you are lucky enough to have a Business Intelligence (BI) team - IT people who specialize in business reporting - HR may be able to get their attention briefly, but my experience has been that 4 out of 4 times they tire very quickly of this BI ignorant HR person and their silly needs. The BI people go away. They disappear. The result is a solution that never gets where you want it to go and ultimately doesn't work. If you are going to go in house, you need dedicated resources - dedicated resources cost money. You also have to find some really smart technology people who actually like and want to work on HR problems, rather than try to invent the next Facebook. Non coincidentally, this is extremely hard - even at Facebook. Especially at Facebook. ------------------------------------------------------------------------------------------ For the record, Tableau could be a great downstream data exploration and data visualization application, IF YOU HAVE a viable data warehouse and ETL solution in place for HR. ------------------------------------------------------------------------------------------ Option B : Specialized Analytical System For HR Here is a sample of options for varied HR Analytics purposes (alphabetical order) CultureAmp CruncHR Glint HiQ Lab One Model OrcaEyes Sapience Analytics SAP SuccessFactors Workforce Analytics (used to be InfoHRM) Visier ZeroedIn (Folks, feel free to add other HR Analytics applications to the comments of this post and I will edit this list to include those later) Maybe someday I will do a detailed analysis of all these applications – for now, here are the main questions you should ask as you evaluate each option? (you and your team need to answer these in a no-spin zone) What is the focus of this application? (consider depth, breadth, etc.) What are the 1-3 main differentiators of this application relative to its peers? What non-transparent assumptions underlay those 1-3 main differentiators? What other data management effort and applications are required to produce the final insights and stories that I am looking for? What is the data management strategy to get the data from your varied HR systems to these environments? (consider at the go live and consider the ongoing refresh) This was a Gloves Off Friday Post from Mike West Disclosures: Mike West is a bad man Mike West writes profusely about People Analytics Mike West now works for One Model

    Read Article

    Featured
    11 min read
    Mike West

    Reasons Why I am Joining One Model

    To whom it may concern, For the last 2 years I am proud to have run my own People Analytics consulting company - PeopleAnalyst, which I like to call the first Independent People Analytics Design Company - but On January 1st - I will be joining One Model. These are the reasons: 1. I believe that People Analytics is important to the future of HR, the future of business and humanity – perhaps one of the most important business trends in our lifetime. I recently shared the principles I hold, supporting this thought here: Future of Human Resources - in 10 Principles. Beyond these principles I frequently try to point out that we had Accounting before we had Finance, we had Sales before we had Marketing, and we had HR (People Operations) before People Analytics. In every historical case, the application of an analytical framework to the pre-existing operational function revolutionized the way business was done, and those who were early to it were able to exploit lucrative informational advantages for a brief period of time before they became ubiquitous. In face of history these changes did not occur that long ago but today we think of them as always being the way they are. People Analytics’ time is now – in the future it will be “required for entry” in the big girls/boys club, but not be as much of an advantage as it is now. Pay very careful attention to the investment companies like Google and Walmart have made in People Analytics – 30+ people each and growing, going back several years. These companies are not stupid – this says something - they saw something. Google is “cleaning up” on the application of data to the People Operations/Talent area and in many markets they are a force to be reckoned with - nobody is anywhere near them on what they offer and how they do HR today. They are a steamroller. 2. Long ago I decided that my work - the application of data, math and science to HR - is my reason for being and part of the intentional legacy I want to leave on this earth. My commitment to what we now call People Analytics is unchanging - the key for me as I go through life is just figuring out where my efforts will have the most impact. I make moves when I sense the math on this has changed. Merck -> PetSmart -> Google -> (brief cross functional divergence)-> Children’s Medical ->PeopleAnalyst (my consulting company - the first People Analytics design company) -> now One Model… As we move forward, I think my area of greatest contribution will be to embed my unique way of approaching People Analytics into a technology environment, making it more realistic, accessible and affordable to more organizations. It is quite an amazing thing for a guy like me to have access to an engineering team with seed funding – I’m not going to pass on that opportunity. 3. Team - I believe in the magic of teamwork. I saw this video, which reminded me of what can be accomplished with teamwork : http://wapo.st/1U19g0M Gives me chills - the good kind. InfoHRM --> Success Factors --> SAP If you know anything about the history of enterprise reporting solutions for HR, the foundational predecessor to modern HR Analytics, you will find that the engineering team at One Model has a very interesting pedigree. Going back 10 years, the only system in this space was a little company called InfoHRM – they were out on the leading edge of HR reporting, essentially running a “cloud-like” solution before we even knew what the cloud was. InfoHRM was acquired by Success Factors (purportedly to help them crack the HR data reporting challenges they couldn’t seem to solve on their own), and then Success Factors was later gobbled up by SAP. I don’t know the whole behind the scenes story, but my general impression of what happens to people in these big company acquisitions is that how the product is perceived, the dynamics of working for an organization, as well as where you fit into all that really change. These guys fell out of that. When you see people who helped built a product category before anybody else was doing anything like it, who say now we are building something better knowing what we know now, you stop and listen - at least I do. TechStars – The Top 1% of Startups Another thing I really like about One Model is that they came out of the TechStars Accelerator program. Accelerators like TechStars are super-selective--less than 1% of applicants get in. You could say they are pickier than schools like Harvard, Stanford and MIT. In addition to direct assistance in getting the business model on the right track, and the well know “Pitch Day” TechStars alumni have access to a network of investors and advisors for life. In the Startup world, access to capital matters and access is primarily determined by your network. An element of this may seem like a superficial game of “who you know and who knows you”, but an element of this is ability to get to people who have been through good and hard times and can help you solve really difficult problems. Austin - SXSW and Food Trucks about say it all. These guys came to Austin to launch their company a little after the time that I did. I’m an HR guy – I’m all about culture and Austin has the right culture for me. Austin is hip (some call it weird), is second only Silicon Valley for startup community, in a US state that is friendly to business, has a lower cost of living than either US coast, and is well positioned geographically for US enterprise sales – 4 hours by plane to either coast and within driving distance or very short plane flight of 3 other major cities (Houston, Dallas and San Antonio). The startup community is tight-nit, collaborative and with a lot less of the showmanship and games you see in the Silicon Valley – I think a higher percentage of people here take creating a real business more seriously. Beyond these intangibles, when it comes to HR data, keep an eye on Austin, this is where it will be, there is some important stuff going on this space here right now. Mostly I just love Austin – it is an island of authenticity and creative energy unlike anywhere else. 4. Product Focus – oh where we can go together. One of the big mistakes I see in the field right now is that most people that are thinking at all about the space are thinking too narrowly. They think People Analytics is just one type of question, one type of data, or the application of a certain method of working with data. Let’s say prediction, for example. Examples include, how do you predict hires who will perform well in your environment or how do you predict what people are most likely to leave in a given time frame. However, some of these strike me as gimmicks - not standing up on real solid data - People Analytics is much more than this. For example - I have personally worked with data on HR on decisions involving how organizations select (staffing), onboard, pay (compensation), perk (benefits), the origins of happiness and motivation at work, quality of managers, employee commitment/turnover, performance, diversity, learning/training, time off policy, the relationship of HR outcomes to sales outcomes, etc… Others have worked on topics I haven't - the list continues. On top of the varied subject matter focus, you can focus on how you collect data, the tools to make data flow more efficiently, the methods you can use to analyze it, statistics, how you visualize the data, how you distribute to other people, etc. Any and all of these are potentially viable areas of business focus that you will see niche products in. As methods, machine learning algorithms and prediction are hot right now – all these are in our future, but we still have a lot of work to do on them. Here are the main perceptions I will offer on product focus at this time: People Analytics is eclectic, expansive and inclusive. In its essence, People Analytics is the systematic application of behavioral science and statistics to Human Resource Management to achieve probability derived business advantages. We need solutions that enable analysts to be better analysts, in the world of possibilities, not try to replace the analysts entirely. We need solutions that create more heros, not less. People tire quickly of gadgets and nobody wants to purchase and manage an ever-expanding assortment of gadgets (or only if they are all made by Apple). I’ll put it another way - One Model looks more like an aircraft carrier to me than a paper airplane. Organizations operate as holistic systems, therefore the answers to problems span across areas of specific functional responsibility, expertise and operational data stores. We have a lot of silos of data in HR – HR has undergone progressive advances through technology specialization and will continue to. The great irony is that the future of HR Analytics may be just reversed: synthesis, not specialization. The differentiating premise of One Model is synthesis. Many advantages will stem from this vantage point. If you care about synthesis of data in HR One Model should be on your short list. To do any analytics, simple or advanced (prediction, forecasting, optimization), accurately, regularly, in a timely and efficient way requires address of sprawling un-integrated operational HR data sources and process. One Model decided to start with the ‘data munging’ fundamentals and build from there. That doesn’t sound sexy and is a little more difficult initially to get the same kind of PR as a result, but it is important, and they will steadily deliver increasing value on that foundation, offers a lot of possibilities, and takes customers into the future in steps, not all at once. Imagine showing up to the board room with predictions about employees but you can't accurately answer or get to quickly any of the basic employee ins and outs questions. Begging the question - do you really know your workforce? What exactly do you do here anyway? I'm all for prediction and One Model is going there but don't over promise, really get to know HR data specifically, get the 'data munging' fundamentals right, organize more sources data more efficiently than anyone else, and delight and surprise the customer progressively as you go. I agree with this – I think it works better, fits the needs of today's HR function, and matches my practical MidWest (US) values. Cloud / Software as Service is here, is the future. The premise of One Model is that they can invest big in infrastructure and innovation on that infrastructure and distribute those gains to everyone. It only gets progressively better and more efficient over time. Why should every company invest in homegrown infrastructure for HR Reporting and Analytics independently? To reinvent all HR Analytics workflow internally at every organization is unrealistic for most organizations as it is a ludicrous business proposition. We no longer design our own homegrown HRIS systems today - why create and maintain our own technology infrastructure for HR Reporting and Analytics? I think 5-10 years from now we will look back and wonder why we used to do this at all, evoking the puckered sneers that “legacy HRIS solutions” get today. Don’t get me wrong - you should invest in ultra advanced or niche innovations in analytics unique to your business, in your environment, however in order to have the time and resources available to apply that kind of focus, you can rent everything else. How about getting on a platform that can speak to those applications and everything else. Like I do, these guys believe in "play nice with others" and that good guys do win too sometimes. You want to come along for the ride? ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 10+ years of experience building People Analytics from the ground up at companies such as Google, Merck, PetSmart, Children's Medical, Jawbone and other places. Mike's passion is to develop thought leadership and to cross pollinate the frameworks and processes he helped develop and pioneer as an employee at these places. Mike spends most of his time teaching, coaching and writing on all things People Analytics.

    Read Article

    Featured
    21 min read
    Mike West

    Welcome to People Analytics, Are You Sure You Want This Job?

    “Men* wanted for hazardous journey. Low wages, bitter cold, long hours of complete darkness. Safe return doubtful. Honour and recognition in event of success.” Serious question. If you stumbled on the employment ad above, would you respond? The ad is purported to have been posted by Ernest Shackleton to recruit people (“Men*”) for his Endurance expedition to the South Pole. There is some debate whether the ad was actually written by Shackleton, never-the-less whoever wrote it could get credit for compelling ad copy, as well as possibly the first example of a realistic job preview. *This was around 1914, and apparently whoever actually wrote this was unaware of woman’s interest in suffering in the workplace for 25% less than men! This reminds me of a pernicious glass ceiling and wage disparity problem – and now suddenly taking a little boat down to the South Pole doesn’t actually sound so challenging to me. Lots more I can say about the journey into problems of diversity and gender, but we will circle back to HR data and diversity another time. For now, let’s just continue with our juxtaposition of the cold, icy cold, Antarctic and HR Analytics, in general. A Brief Summary of The Expedition (Wikipedia): Endurance became beset in the ice of the Weddell Sea before reaching Vahsel Bay, and despite efforts to free it, drifted northward, held in the pack ice, throughout the Antarctic winter of 1915. Eventually the ship was crushed and sank, stranding its 28-man complement on the ice. After months spent in makeshift camps as the ice continued its northwards drift, the party took to the lifeboats to reach the inhospitable, uninhabited Elephant Island. Shackleton and five others then made an 800-mile (1,287 km) open-boat journey in the James Caird to reach South Georgia. From there, Shackleton was eventually able to mount a rescue of the men waiting on Elephant Island and bring them home. On the other side of the continent, the Ross Sea party overcame great hardships to fulfill its sub-mission. Aurora was blown from her moorings during a gale and was unable to return, leaving the shore party marooned without proper supplies or equipment. Nevertheless, the depots were laid, but three lives were lost in the process. Clearly the expedition failed to accomplish all of its objectives – yet it is recognized as an epic feat of leadership, endurance and one of the last of “great expeditions”. Some time ago I came across the ad, which tickles my sick sense of humor and my imagination. Who would take this job. I wonder - who wouldn’t take this job? In my mind I notice subtle similarities to the choices I have made in my career – which circles on the question how to change Human Resources, if not the direction of work as a whole, with data. Despite difficulty and a seeming complete lack of possibility for fame in this work, I am excited by what I do and I love every minute of it. I probably would have responded to the ad. I am also intrigued by others who take an extended interest in my strange field - partly because historically extended interest has been rare and partly because it is far from what most would consider a "rewarding career". I have been tracking new roles in HR Analytics, Talent Analytics, Workforce Analytics (what I call People Analytics) through Job Postings off and on for a long time. More recently, I have been searching for these people on LinkedIn. It is a question that begs to be answered – who are these people, what is their story, what do they care about, what do they want to achieve, how are they received within their HR organizations, what are they working on, what are their struggles? Those questions are a work in progress. For now, the fresh faces and backgrounds I see in these roles in pictures and Linkedin headlines is already truly inspiring to me. They surpass my imagination in magnitude and breadth and to me represent "pure energy". I think the people who are drawn to these roles are special and amazing. I have no doubt that as a collective they/we will ultimately work through the difficulties to reach our destination (Destination? This too is a great question). “Honour and recognition” hopefully forthcoming. In real life who would respond to an ad like Shackleton’s? I think a high proportion of those people today would be these People Analysts. The Vast Expanse of Human Resources The real HR is vastly underestimated and mostly lost on most people without direct exposure to a leadership role in HR in a large modern organization. By large I don't mean 500, I mean 50,000. HR doesn't really come into the spotlight until you reach 5,000 employees. At this point, you start to realize the inherent value in getting HR right - that is, if it is not too late for you! If you reach 50,000 employees you probably have figured HR out and now you preach it, never-the-less you are in a world of much bigger HR issues. If you are in a leadership position in HR for one of these companies few people know your precise struggles. It is a lonely world. If you could peer inside the little windows you would see that HR in large modern organizations is a complex network of technology, policy, process and people within discrete areas of specialization. HR can be grouped broadly in categories of Staffing (Sourcing, Recruiting, Onboarding), Total Rewards (Compensation & Benefits), Diversity, Talent Management and Organization Effectiveness (Performance Management, Succession Planning, Organization Design), Training and Development, Employee and Labor Relations, and HR Law & Govt. Compliance.It is a veritable alphabet soup of things to learn, with an entire language of its own. Sounds crazy, but yes, it is a diverse world of very real and very different things, all in this thing we call "HR". If you want to dive deeper you can find more granular silos of responsibility, but I will refrain from the arcane details and just leave it at the high level. I always forget - how many ways can eskimos describe snow?, and why? I don't know the answer, however I can surmise that they must spend a lot of time with the stuff. This is a good place to pause – are you still with me? Here is a more down to earth human story for you. Several times I have spoken to recruiters (an actual real life role in HR), and as I attempted to describe the complexity of tieing out data from many HR sources to some meaningful business conclusion and what that actually requires I have been interrupted with a reply something like - “It is nice you have had exposure to all those other things, but to be clear in this role you will deal exclusively with HR data. Are you o.k. with that?” With HR data? After only just having described a variety of HR data sources, not really even making the point about how these sources should be tied to business data, I am perplexed at who and what I am dealing with here. I am left to assume that by HR, they mean Staffing, a subset of HR and the rest of it is lost on them. Clearly, per their guidance it will also be lost from me and the organization if I take this job under these conditions. Do I correct them, or proceed? I am sorry to be blazingly disrespectful, but I have been tempted to stop right there and say, “Hello, Hello, are you still with me?" Should I proceed ahead or turn back? O.k., for now I will spare everyone the graduate level course in Human Resources but if you would like one they offer specialized programs in this stuff at the University of Minnesota, Cornell, Rutgers, University of Illinois, USC, somewhere in Michigan, and a few other schools! Some of the world's most respected CHROs of the largest organizations of the world came from those programs - I’d be happy to make introductions. By now you are rolling your eyes at me. No problem, I’m used to this. I am not sure what other people were doing when Shackleton was hacking away at the ice that was destroying the hull of his ship - I am sure his choices in life made a few eyes roll as well. Leadership and decision-making for HR sub-functions is distributed among members of the HR Leadership Team (HRLT). In large organizations the HR Leadership Team generally consists of 7-10 Sr. Directors reporting to a “Chief Human Resources Officer”(CHRO, SVP of HR, etc.). The Chief Human Resource Officer may have leadership, decision-making authority and budgetary control over the HR function, but in some cases the big budgetary decisions may go all the way up to the CEO and/or the board. Staff/Employees/Humans typically represent a spend of 50-85% of most modern organizations revenue. Usually over 75%. Stop - check it out in the annual reports, these days available on the internet. These are real numbers. Where is the profit? It is the little bit that is left over after all those people are done with their work and paid. The shareholders are buying a share in that little bitty slice. "Our most important asset". Have we looked at how we do HR with data - not much, not really - we know it is somewhere down there - it's a line item. In most advanced organizations the head of HR will report to the CEO, but often they report to the head of Operations or Finance – this varies by company. It is usually a sign post – a little piece of ice floating – be sure to keep an eye on it, something big may actually be under this ice. Regardless, wherever they report, it is contingent upon the CHRO to drive alignment of HR goals with “the business” and alignment between HR sub-functions on a central strategy and operational architecture for Human Resources. Seen as a “support function” annual HR goals and budgets typically FOLLOW development of business strategy and goals. HR goals are assembled in a last minute hurried manner sometime AFTER all the other business functions have had their shot at the podium. If those others strategies and goals are not yet fully clear, or the HR/People consequences cannot yet be ascertained with the information on hand, or the HR leadership team fails to communicate the people implications of the business goals, then HR will follow the business in a misaligned and disheveled path. In my experience, these are accurate actual descriptions of HR goals. Whether or not the HR team is able to formulate a coherent plan HR has last shot at organizational resources - consequently HR efforts are often unclear, underfunded and unrealistic (relative to the magnitude and difficulty of the goals). Examples of strategic HR objectives include: “Improve Employee Morale”, “Improve Employee Engagement”, “Reduce Employee Turnover”, “Change our Company Culture”... Hey, a big challenge excites me, but FYI these population level averages are resistant to dramatic changes. I’m not saying they don’t matter or can’t ever change – the polar icecaps are melting as we speak - I am saying moving these things in a different direction than they want to go requires serious attention to detail, resources, teamwork and commitment. It starts with data – let’s try to have a look at it together. Before Data, HR Systems Historically HR has vacillated between a single system ERP (Enterprise Resource Planning System) or HRIS (Human Resource Information System) that is average at everything or a series of “best of breed” applications that are better at a single purpose functions (Applicant Tracking Systems, Performance Management Systems, Compensation Systems, Learning Management Systems, Time and Labor Systems, etc…) Since HR CHROs and/or critical stakeholders in HR change over every 2-4 years you can guarantee exchange of favored technology to solve whatever shortfalls exist. Meanwhile, within a few years of implementing a solution we have new leadership and who prefer a newer, more optimistic path. Never wanting to get trapped in the ice, there is constant pull towards fragmented systems stemming from HR sub-function implementation of “best of breed” applications that align with HR sub-function operational objectives. If I am the head of Staffing and my team is tasked with improving Staffing, why would I settle for anything less than the best Applicant Tracking System? If I am the head of Compensation and my team is tasked with getting a grip on Total Rewards, why would I settle for anything less than the best Compensation Management System? So on and so forth. There is nothing wrong with this, however it results in a lack of technology optimization across sub-function silos and some important data consequences that must be addressed before or during reporting and analytics. Larger, established HRIS systems (PeopleSoft, Oracle, SAP, Lawson, and recently Workday), have more dependencies to worry about and thus are inherently slower to innovate than the collective of single purpose systems. The core systems will always lag in one or more sub-function operational areas. For example, even 10-15 years ago you could facilitate the Performance Management on SAP or Oracle HRIS, however Success Factors came along and offered a solution that was designed for this and consequently much better at it. So for 5-10 years Success Factors took the HR market for Performance Management, creating a hundred million dollar market and adding yet a new system category for HR to manage and integrate. Success Factors was later acquired by SAP (for 3.4 Billion) , and to this day can still be purchased by your organization as a stand-alone Performance Management application, with or without SAPs HRIS product. To this day most of the organizations I have worked with or for have NOT fully integrated Performance reporting between Success Factors and their HRIS. They prepare for the process to begin by manually setting and loading a file. There is someone deep within the bowels of the organization doing several spins on this. Probably also with a smart phone going off widely in the middle of dinner with the family - if this person even gets dinner with the family for a month or two of their life. Getting it out and rejoined to a changing organization and broader analytical purposes is another thing entirely. You will then discover, you can't export all the data you want directly from the same report, and there may not even be a common key! You might ask, “You mean to tell me we have no efficient method to join and report this data that is central to HR and to the business? Are you kidding me?" No. We have seen the same in Staffing, Learning Management, and Compensation. Compensation is especially bad - I like to call Compensation Planning (an annual event at every large company) a "planned emergency". 99.9% of the problems is embed in systems, 100% of it is caused by choices made by humans. By virtue of the many simultaneously moving fronts of sub-function innovation HR will undergo constant fragmentation and change in systems. Innovation in HR systems is good for us, but it is also very disruptive. Let’s Talk About HR Data Contrary to widespread belief HR actually has a lot of data, and a lot of good data. It is just locked away in systems not designed at all for reporting or analysis. Universally, HR systems are designed to facilitate operational processes and while they can perform their intended operational functions well (by design), they often do so at the expense of reporting and analysis. I will take a bucket of ice water over the head if someone shows me a single HR system that can perform a chi-square or binary logistic regression out of the box. How about any statistics beyond addition, subtraction, multiplication and division . We are waiting. Still waiting. Frankly, I like to keep my statistics and data visualization software options open – I don’t want my HR system to do everything for me, but I do need to be able to get the data out of my HR systems. Often even this seemingly basic task (get the data out) is difficult. Apparently, nobody thought of about getting the data out. If you are laughing, stop laughing. I’m not kidding. We can’t get deep insights or complete picture of the story until we get the data out of multiple systems, join it, and have a look at it through applications designed to work with data for reporting and analysis. Most HR “Analysts” are cobbling data together inefficiently with manual, undeveloped or broken data process. Some 80% of the effort of HR Analyst efforts are attributed to manual augmentation of non existent or inefficient data workflow. They spend very little time on actual science, statistical analysis and presentation of analysis. I have been an HR Analyst in one form or another for over 15 years, I speak to HR Analysts weekly, and I have seen the surveys. HR data are/is complex because people and organizations are complex and the sub-functions to support HR (as described at high level in above) are varied. There is hardly any similarity in reporting between Compensation and Benefits, let alone Staffing, Training or Employee Relations. Who owns Turnover reporting? Who owns the Employee Survey? These come from completely different systems, with different data structures and different metrics. The desired metrics can escalate into the hundreds and with variations in the thousands. Most HR metrics are compound and with dozens of potentially relevant dimensions to monitor. Let's take a look at a very commonly produced and seemingly simple HR metric: “Employee Attrition/Turnover”. This metric is formulated as a compound calculation (Time Period Exits / Time Period Average Headcount). Now before you shout "Eureka! - I have it - you just divide this number by this other number" keep in mind that you will need to calculate this by segments along multiple dimensions: location, division, business unit, tenure, grade/pay group, performance group, age, ethnicity, gender, etc.… You may have 50 locations. Some locations have 5 people and some have 15,000 people. What if you want division by location by gender. Also keep in mind that in annual form the denominator (average headcount) requires 12-13 or more data points for each of subset of each dimension, and these subset counts must all align in definition and time period consistently with the numerator. Also keep in mind that organizations can be expressed in different ways (people reporting relationships or cost center reporting relationship, which do not typically match), and that the only constant is that organizations are constantly changing. Imagine a data set that changes simultaneously along multiple dimensions over time and you are trying to report on this consistently over time to demonstrate a "trend" or "story" - how exactly does this work? If you are flabbergasted, don't worry, most of us People Analysts can work this problem in our sleep. This isn't even the hard stuff. I just described the calculation of one metric, now multiple this effort by 20 more metrics with data sitting across different systems and try to derive some meaning from that. Shouldn't we be analyzing this stuff together to tell a story, not independently? Most of the people doing good work on employee turnover have long ago moved on to more advanced ways of looking at exits (logistic regression, survival analysis, hazard curves, predictive models, etc.) and incorporate data from many many more data sources. I can ask employees three questions and based on their answer tell you if their chance of leaving in the next year is "about average, 2x average or 3x average" without knowing anything else about them. Give me their job and salary and tenure and a few other details and we can clean up on this problem. Let me join it to performance and compensation and now you can decide carefully how you want to distribute the limited budget you have to work with. Why not? If you are not working on this, what exactly are you working on? Speaking of clean up. For most organizations, you will get started on a seemingly simple problem, and bang into an inconsistency or perceived data quality problem, which requires people sitting in another sub-function you have no authority over to make a decision, fix a process, or change how they do something. This is not simple any longer – this is really difficult. You can circle on the same issues for years. Welcome to People Analytics, Are You Sure You Really Want This Job? Then There Were People HR professionals are not selected for a background in technology, math or science and so depend on people outside of HR for augmenting support in these capacities. Since HR is seen by “the business” as lower in priority than other business functions (software engineering, finance, marketing, sales, etc.), and therefore not a very prestigious appointment for anyone - IT and data science support suffers. The head of HR is on the phone about something they want and the head of Sales, which call should I take? If HR is already underfunded for its goals, HR IT is even worse, not receiving adequate talent pool or funding. Budgets are divided among the heads of functional HR silos and so no unifying technology solution can be reached. There are simply too many different jobs to be done in too many different systems. Data architecture for this is an after-thought. Each Sr. Director will compete for the time, attention and resources of whatever HRIS or HR Analytics professionals exist to try to achieve their objectives first (at any and all cost to others –their performance rating is on the line!). If they cannot get that attention they want from IT or from their HR Analysts they will try to go outside the organization for the support. Talk about being crushed in the ice. You will be loved and despised, sometimes consecutively, sometimes simultaneously. How this can happen is one of the great mysteries of the universe - outside of light being possible to be described both as a particle and wave. It is sort of like the possibility of dying of thirst while standing on water (ice). Input fire and maybe drown. Now I am being dramatic. This is just to say that I think Shackleton and company did a pretty good job, given the odds. I have had opportunity to be in these data oriented HR roles for variety of very well respected organizations (Merck, PetSmart, Google, one of the best Children’s Hospitals in the country to name a few) - believe me when I say these HRIS and HR Analytics folks have many more “bosses”, a combined list of objectives much longer, technically more complicated, and with less resources than anyone else. There high turnover among HRIS and HR Analytics professionals for a reason. You got one. Great. I hope you have a backup plan! Other People Analytics posts by Mike West ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 10+ years of experience building People Analytics from the ground up at companies such as Google, Merck, PetSmart, Children's Medical, Jawbone and other places. Mike's passion is to develop thought leadership in HR and to cross pollinate the frameworks and processes he helped develop and pioneer as an employee at these places. Mike spends most of his time teaching, coaching and writing on all things People Analytics.

    Read Article

    Load More
    Ready to Get the Most Out of Your HR Data?
    Let's Talk