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6 min read
Tony Ashton
The recent announcement by SAP SuccessFactors to sunset its legacy people analytics product leaves SAP SuccessFactors customers facing significant uncertainty. The sunset signals the deprecation of some* key reporting technologies used by SAP SuccessFactors —Canvas, Classic, Table, and Tiles and Dashboard Reports. With these SAP SuccessFactors reporting tools being shut down, businesses reliant on them face potential operational hiccups in their decision-making processes. However, this development also presents a unique opportunity to explore superior alternatives that can ensure continuity, innovation, and enhanced analytics capabilities. In other words, it could be the perfect time to consider an upgrade. Why Settle When You Can Ascend In contrast to SAP SuccessFactors’ winding down approach, maybe it’s time to look for a forward-thinking analytics and reporting platform that does double duty: Addresses the immediate gaps left by the SAP SuccessFactor updates and provides a robust foundation for future growth. Look for a solution that offers: Advanced Analytics and Reporting: Leveraging state-of-the-art technology to deliver deep insights and customizable reporting capabilities that grow with your business. Seamless Integration: Effortlessly merge data from various sources, including SAP SuccessFactors, ensuring a smooth transition and continuity of operations. Future-Proofing Your Analytics: Ensure that your chosen solution’s analytics capabilities evolve to meet future challenges head-on with continuous updates and a commitment to innovation. Learn more about getting People Analytics out of SuccessFactors and your other HR tools. Transitioning to a More Capable and Dynamic Solution The journey from SAP SuccessFactors' legacy reports to a more sophisticated and comprehensive analytics platform like One Model can be seamless and transformative. Get started by: Conducting an Analytics Audit: Understand your current analytics and reporting needs and how they might evolve. Evaluating One Model’s Offering: Explore how One Model’s features and capabilities align with your business objectives. Planning for Migration: Leverage One Model’s support and resources for a smooth transition, ensuring minimal disruption to your operations. While the deprecation of SAP SuccessFactors’ legacy reporting tools marks the end of an era, it also opens the door to embracing a more advanced, flexible, and comprehensive analytics solution like One Model. By choosing to upgrade, organisations can not only overcome the challenges posed by SAP SuccessFactors’ transition but also position themselves for stronger, more data-driven success in the future. Is it time for an upgrade? Embrace the future of analytics with One Model—where innovation, integration, and insight come together to drive your business forward. Note: After recording this video we noticed that SAP SuccessFactors had deferred a couple of their deprecation announcements. Table Reports and Canvas Reports will stay around for a while longer, while Classic Reporting and the Tiles & Dashboards are still being deprecated. This illustrates the complex data structures and variety of different technologies at play in the SAP SuccessFactors reporting landscape remains a challenge. For a complete answer to this, come and have a chat with us. We'd love to show you a better solution. Let us know you're interested, and we'll reach out to schedule time.
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Featured
28 min read
Tony Ashton
[This article is taken from a presentation I delivered as part of a broader session on People Analytics for the Australian Human Resources Institute (AHRI) QLD Analytics Network on 7 October 2021. It uses the presentation slides and accompanying presentation script only slightly modified from the spoken word to fit the written form. thanks, Tony] This article focuses on setting up a people analytics capability in your organization and thinking about what the key challenges are and how to resolve those. Let’s start by talking about data-driven insights, people analytics is the focus. But more broadly, there is an untold number of articles and research papers on the importance of data-driven decision making and my bookshelf is full of these books and papers. And I'm sure yours are too (or your virtual bookshelves). Here is a nice example from Deloitte to set the scene. Of the organizations they surveyed 39% of those have a strong analytics culture, and 48% of those were significantly exceeding their business goals. Compared to those that didn't display a strong analytics culture, only 22% were significantly exceeding their goals. There's a double whammy in terms of the proportional impact that Analytics gives to an organization. But importantly, also here, there's this angle of culture. In the survey, most executives believe that they weren't really that insight-driven as an organization. So there is this challenge between the ability to pull data together, derive insights, and actually make decisions and make that a part of the framework for how business is done. So why is this important to HR? If you can't connect people to business outcomes, then you're really just doing stuff because you think it's a good idea. Doing stuff that you think is a good idea is ok, but it will only get you so far - being able to prove it is a good idea and measure your impact is another thing entirely. The importance of data-driven decision making for HR Using data helps you prioritize your strategies. You can't do everything, so you need to focus your resources on HR. Metrics and data help you do that and this helps you build that culture of data-driven decision making. If you think about the people space, people related decision making and HR processes are all underpinned by principles like merit, natural justice, fairness and transparency. Without a data-driven approach to this, you're very kind of at risk of replicating the same diversity issues that you see in many organizations: pay equity issues, how promotions and pay increases are awarded, or who should be hired. Some of these examples are on a macro scale, for example, your whole company's diversity profile, and some at the micro-level, but the same general principles apply. Data is important for setting strategy and for tactical decision making At the micro-scale let’s take a specific example of an individual hiring decision. We have selection criteria for hiring to ensure we get the right person for the role and use multi-source inputs to the process to base decisions on evidence and to avoid bias, nepotism, discrimination etc. At the micro-level, you heavily rely on good processes, training and company culture. Ideally, guiding these processes and strategies would be a great analytical understanding of your organization’s diversity profile, the skills and capabilities required for the next 2-3 years, market pay rates for similar roles, the complexity of the role, expected time to productivity… and on. Hiring strategies in the absence of this data are going to be much less effective than they would otherwise be. The reality is that HR has been somewhat late to the party around the use of data and people analytics. If we think about this from a simple business accountability perspective HR teams are custodians of lots of systems. Not many organizations have just one system. And even if you have just one, we still have to curate and care for that information. It's a rich asset to the organization. Putting data in the hands of managers is critical for creating a data-driven culture Let’s look at some research from the Annual HR Systems survey. This survey provides a rich set of longitudinal research and here I’ve highlighted some insights they developed regarding the differences between organizations that are data-driven compared to those that are less so. This construct is similar to the Deloitte research we talked about earlier. The bars on the left of the chart are the results for organizations that are described as not being data-driven, and bars on the right are those that are identified as being data-driven. As you would expect all segments on the right-hand side are higher than on the left, but by far the biggest difference and the thing that really stands out as being different is the deployment of information to managers, putting information in the hands of decision makers. I have circled this segment in red on the chart. So, this gives us something to think about in terms of what drives success. Success isn't necessarily having a great dashboard, success is determined by whether or not people are using data and making decisions with it. The “maturity” of people analytics There has been a lot written on this topic across the decades, there are more books and research papers than you can imagine. Just a few examples here. This is extremely well-trodden terrain and there is no shortage of great information to draw from. Facilitating the Utilization of HR Metrics – The Next HR Measurement Challenge; Irmer, Bernd E (Ph.D); Ellerby, Anastasia (MBA); Blannin, Heather, 2004 Early research on driving the adoption and use of people data In terms of the topic of adoption, this is a key theme for this discussion, and the focus is on the actual use of data in organizations. The image above is an extract from a paper published around 2004 by the InfoHRM company in partnership with the Corporate Leadership Council within the Corporate Executive Board (subsequently acquired by Gartner). This research identified the key phases of sophistication around the use of HR data for business impact. The phases were characterized as: getting your house in order by automating reporting and reducing the load from ad-hoc queries by introducing self-service starting to use more advanced metrics and multidimensional analysis, and then deploying more broadly into everyday decision-making and impacting business outcomes. Through a detailed survey and interview process companies self-identified into one of these categories regarding the maturity of their use of HR data. There was a big difference in what was required of companies in phase three and we will talk more about this. Facilitating the Utilization of HR Metrics – The Next HR Measurement Challenge; Irmer, Bernd E (Ph.D); Ellerby, Anastasia (MBA); Blannin, Heather, 2006 Two years later, the study was re-run and the framework was updated based on more findings and longitudinal data. There was an even stronger focus on understanding how to drive adoption and found that there was the dotted line after phase two was even more pronounced and that it was a really hard barrier for companies to jump across. The research provides a number of tactics and best practice advice to address this, but it was clear that having the technology to help scale, automate, improve quality etc. is necessary, but not sufficient for success and success takes good change management, cultural alignment and business impact orientation. It is the latter topics that also drove the creation of the additional phase, i.e. those companies that were truly having an impact on business outcomes through the use of HR data. This research was happening at a time when HR itself was heavily focused on the prevailing thought leadership of Dave Ulrich around HR and business alignment and other leading work by Mark Huselid, Brian Becker, Richard Beatty, John Boudreau, and others. A big part of being a business partner and a business driver was the use of data and evidence-based decision making. Maturity models enter the mainstream Interestingly, around a similar timeframe, Gartner was building its own model for how companies could be more data-driven, and the use of business analytics across an organization [the earliest reference to this I can find is from 2009]. Gartner’s framework described this in the form of a four-phase model describing increasing difficulty for companies to move from descriptive analytics up to being able to deliver prescriptive analytics for the highest value. Bersin & Associates (since acquired by Deloitte) published this model around 2010. As you can see lots of similarities to what has come before and presents a maturity scale of using people analytics starting from operational reporting through advanced reporting, advanced analytics and up to predictive analytics. Defining success While these models have helped companies and people analytics teams assess where they are and the opportunities to make more of a difference, I have a problem with all of these models. The problem is that they set up prescriptive or predictive analytics as the main destination everyone should be striving for and if you're not doing predictive analytics, then you're not really doing anything of worth and setting up an expectation that is hard to reach and not necessarily the right destination. Something I'd recommend considering is how you see success and what matters to your organization is the most important thing, how you get there is just a part of the journey. Build a sustainable capability and avoid the key person dependency risk So, what do we need to do? Many of you, myself included, may have had a role that could be characterized as “the Excel ninja” in your organization, or HR team. You are able to crunch through data, get data from lots of places, massage it, put it together, create some amazing reports and dashboards, and share them around. But then if someone wanted to see that data cut differently, that became a pile of work and maybe your weekend. This is all great for job security and feeling important and needed, but before long you get bored or burnt out, or both. And then you leave or move on to another role. You may have written some great handover notes, but there is an immense amount of tacit knowledge locked up in your brain and everyone likes to do things their own way, so the next person would invariably reinvent everything. In between times, it is probably hard to fill the role, because people with the right skills are scarce in HR. Basically, relying on the Excel Ninja isn’t a great idea for any company as at some point all their people's analytics capability is going to walk out the door and they have to start again. Data Scientists are amazing, but you need to build a broader people analytics team The lesson there is around building sustainable capability, not just relying on a single person. Now, get ready for a feeling of déjà vu. We are in a very similar position today with the role of the data scientist. Everyone wants to be at the top of the maturity scale right? So, how to get there, just hire a Data Scientist! But, you are actually creating a much worse problem than you had with the Excel Ninja. The data scientist is definitely a superhero and is able to do amazing things. But, as before, if you rely on only one person, you're at risk of not creating a sustainable capability for your organization. It is compounded here too, because 80% of the time the Data Scientist is cleaning and aligning datasets and curating predictive models. Most of the time this work is not repeatable and is designed for specific investigations, which can result in great insights, but pretty soon they get fed up and move on and you are left with a massive hole in your people analytics capability yet again. https://www2.deloitte.com/us/en/pages/human-capital/articles/people-analytics-and-workforce-outcomes.html Deloitte has been doing some nice work around evolving this thinking to be more focused on capability creation, as opposed to an escalating pathway of sophistication. Peter Howes webinar discussing this and other related topics: https://www.onemodel.co/events/peter-workforce-planning-webinar Reviewing all of this material I was reminded of a webinar I hosted in 2019 with Peter Howes. As many of you know, Peter is a giant in the industry. He was the founder of Infohrm and a pioneer in strategic HR, and HR systems, a speaker, educator, and author - a true thought leader in every sense of the term. Peter created this model in around 1980. His core principles are all still valid today and remain probably one of the best characterizations of people analytics done well that I have seen. Essentially, if your team is wrapped up in administrative tasks, you should aim to shift the mix to include professional and strategic activities for greater business impact. You still need to do operational and tactical reporting, that never goes away. Getting greater efficiency and automation for these activities frees you up to do work with greater business impact. The biggest challenges with People Analytics It is pretty clear that the challenges for adoption of people analytics have been around for a couple of decades now, and while technology has caught up with our desires, there is a lot more to success in harnessing technology and developing a sustainable people analytics capability. 1. Data is spread across multiple systems Even if your company has purchased an HRIS suite, you will still have issues pulling data together from across those different applications and invariably you will also have data in lots of different systems. You spend 80% of your time assembling data and probably no more than 5% of your time doing true insight generation. 2. Data is not trusted by leaders If someone doesn't like the message they are hearing from HR, they're going to attack the data. If you have any data quality issues, then that's going to show and it will undermine everything. Even if the inaccuracy is minor and doesn’t affect your message, it is an opening - a weakness. People will start generating their own data, and use different definitions, resulting in a lack of consistency and trust. 3. Analytical tools are not being adopted If your tools are too complex, then they won't be used. This is why many tools don't get used in most organizations, not just people analytics products. There are too many options, too many things to click, and that is a barrier to adoption. Focusing the solution on the real needs of the different users and personas is critical. More is not better in this case, focused insights and fewer options for the end-user is what will bring success. 4. Data security & privacy is really complex Obviously, in HR, security and privacy are critically important, and often a major reason why people data is not shared around organizations. Think back to the life of the Excel Ninja, they are probably generating hundreds of different spreadsheets and emailing them to managers. This is a lot of work, but it is also inherently risky. 5. High expectations for Data Science and AI/Machine Learning Machine Learning (ML) and Artificial Intelligence (AI) is seen as being too futuristic for most despite the incredible amount of hype. “How do we even get started?” is an all too common refrain. 6. Data and predictive models in HR apps are very “black box” Predictive models and even basic data transformation models are often locked in the head of your Excel Ninja, or in a black box from your software vendor. This means you have a lack of transparency in understanding if there are any quality issues in the movement of data and calculations, or if you have an inherent bias, or how reliable and trustworthy those models are. Back to where we began this discussion, if you are making decisions that impact people’s lives, you need to have good reliable evidence. Solving these challenges is necessary for success. So how do we actually do that? Let's talk through some tactics and ideas. Solving the People Analytics challenges STEP ONE - Bring your data together Naturally, bringing your data together is step one. Ideally, into a single data model, or if not, at least a repeatable process for merging your data together so you don't have hands involved in the process. This is really important, because if you have any manual processes you are again spending time on less value adding work taking you away from insight generation, and it's opening up opportunities for errors. STEP TWO - Create a set of key metrics and definitions Creating a set of metrics and a set of definitions is really important, because then you've got consistency. You can then drive reliability and quality through that process. STEP THREE - Deploy simple, guided storyboards/dashboards & data exploration tools Then with a set of defined metrics and storyboards (or dashboards, or whatever you call it) that are consistent and easily understood, you are able to start driving adoption. People get familiar with the frame you are presenting, the terms and the language, and the definitions. This brings a baseline of shared understanding and learning and the ability to then start adding to that through time. STEP FOUR - Wrap everything in role based security from the start In terms of security, you should think about security through the concept of user personas for which you construct roles, not thinking about security for individuals. Think about your Executives, GMs, People Leaders, HR Business Partners (HRBPs) etc. and what the different roles are, what data they need to see and then craft the security around the roles. This allows you to set your data free using security as a way of deploying content, not holding it back. Drowning in spreadsheets is often seen as a problem for data consistency, effort etc. but it is also a major security issue that can be avoided by taking this approach STEP FIVE - Leverage technology and skills to enable the use of ML/AI & predictive insights The technology issues around ML/AI are completely solvable. There is lots of technology available and it is not really a technology problem anymore. It is more an issue of capability and understanding. The key is to leverage technology in a scalable way and not fall into the key person dependency trap. STEP SIX - No magic allowed - make everything fully transparent & explainable This leads into the last point, which is don't allow the use of black magic and closed systems. Make sure that everything is explainable and understandable when it comes to metrics and predictive models or whatever kind of analysis that you're doing. Some practical examples of People Analytics in practice Let me share a couple of quick examples. Here is a storyboard that is structured around a specific topic and has the key questions your audience would be asking and these leading them through the data. So, it’s really easy to understand what's going on with layered complexity from the high level summary trends through to the details. Everything is interactive, you can click and drill. We are leading people through the topic pre-empting the questions that commonly arise when consuming this content. Below is an example using more of a classic KPI style Storyboard. Here you can assemble and browse through the KPIs from the simple to the more advanced, but the layout is consistent and easy to track from the big headline number through the trends and the detailed breakdowns. At any point, you can click and drill. One of the most important features here is this pervasive library of formulas, definitions and explanations. As important is the ability to drill into the details and see who the people are for this analysis (naturally all this is seamlessly controlled by role based security). The ability to drill down lets you validate the information, but also gets you into action. You are able to quickly dig into key employee segments, identify risks and target interventions. These are just a couple of these examples of what you can do to get started fairly simply, but quickly make a big difference in your organization. Building on the previous examples, in the scatterplot below we have added a correlation, which is normally something scary for the average non-statistician, but if you look at the text above the chart you can see an automatically generated written interpretation of the results using simple business language. Instead of just providing the numbers and expecting people to understand what a correlation coefficient is, or how to interpret significance, be explicit and explain whether something is significant or not – this goes a long way. Here is a zoomed in view so you can see this more clearly - the chart heading is in the form of a question and the text is directly answering this question. A Summary of Tactics to Build your People Analytics Capability The slide above summarizes some of the tactics we have covered, with a few additions to help you build a people analytics capability in your organization. If you don’t have the skills in HR, borrow from other disciplines, find the experts in the organization who can help you. Reach out to the broader people analytics community. There are lots of resources, networks and people ready to help. The People Analytics practice and network is bigger now than it has ever been. Also remember that it's not always just about the data, you're in HR, let's talk to people, be sure to check your findings, go around the organization and build your own network to better understand what's actually happening. Some final thoughts By way of some final thoughts. Focus on the questions that matter to your business, start with a small set of things that are repeatable and build trust. This will then give you time to do the more interesting stuff, find opportunities to drive success, and then market your successes. You can build a groundswell of people wanting to get analytics as opposed to you forcing it upon them. And again, it's about insight, not necessarily just about the data, but the actions you can take and the impact you can make. People Analytics is one of the hottest areas that organizations are looking to hire into internationally. The above framework is designed to help you put all this into practice. You need to deal with the job of data orchestration to get all of your data into one place and one logical construct. Focus on Storytelling, not just generating Dashboards. Blend Predictive Analytics into this and some think of it as an add-on. Answer the questions that matter If you are interested, One Model has heaps of assets that we can share with you. For example, contact us if you want some inspiration around the questions that matter. We have a great library of these and this is a really engaging way to talk to people in your organization about people analytics in a non-technical way. We also have an e-book titled "Explore the Power of People Analytics" that’s a great resource to get started.
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Featured
7 min read
Tony Ashton
Following recent media reports that another significant Australian institution has been involved in an underpayments scandal, we thought it appropriate to write a blog about the technical and systemic risk of underpayments for large organisations with disparate HR and payroll systems. While some underpayments are deliberate actions by employers, our experience from over twenty years of working with people data for large organisations is that the overwhelming majority of staff underpayments are driven not by malice. In fact, most HR and payroll professionals care deeply about the people in their organisations. Our experience is that the complexity of the industrial relations system coupled with technology and process mistakes are much larger drivers of ongoing mispayment of employees than deliberate intent. That is why HR and payroll teams need support to prevent these issues from developing. It is important to note that the intent of the mispayment is irrelevant to regulators, your customers, the media, and most importantly - your employees. Whether your organisation, the HR team, or the specific line managers involved intentionally or unintentionally underpaid their staff doesn’t justify the underpayment or limit the risk of such a scandal to your organisation. This is a serious issue and the reputation and employee satisfaction risks to any organisation of an underpayments scandal are immense. Worst still, as in the case of the Made Establishment restaurant chain, if the underpayments are systemic and ongoing, they can become existential for that business and lead to collapse and liquidation. Ensuring your employee payments system works and integrates into other parts of your HR technology ecosystem is crucial to the success of your business. So, how can a minor systems or process issue manifest itself into a broader underpayments scandal? Let’s take an employee who was recently promoted. Information as to their performance and resulting promotion is held in their employer’s learning and development and HR systems. However, a process oversight means that although that individual is listed in a higher pay band in one HR system, their promotion either hasn’t translated into the payroll platform or the information was entered into the payroll system manually and a mistake was made during this process. Let’s also assume that the increased salary is about $2,500 a year - so $50 a week, and it comes during a period of change in that organisation’s payroll practices (they moved from monthly to fortnightly pay cycles) so the employee doesn’t readily notice the absence of their increased salary. If you are an organisation of 2,500 employees and this situation occurred to just 5% of your workforce during a yearly performance review, by the end of the second year your underpayments would total almost one million dollars. In large organisations these relatively minor employee changes occur thousands if not tens of thousands of times a year. Extrapolate that out over multiple years and what initially appears as a minor reporting mistake can quickly become an underpayment scandal involving millions of dollars. In the example given above, a $100 per pay cycle oversight becomes a one million dollar problem. This example is deliberately simple in order to illustrate how quickly these minor issues can multiply. In reality, the employee remuneration frameworks in most large organisations are infinitely more complex and individualised, making it much harder to identify and isolate problems before they spiral into a major scandal. This is compounded when organisations are using outdated software or an amalgamation of different non-integrated IT systems to manage this process. Why should you invest in preemptively isolating and resolving issues of underpayment? Firstly, paying your staff their correct salary is not only the right thing to do, it is the law. Secondly, underpayment and wage theft scandals cause untold damage to an organisation's reputation. Thirdly, underpayment is expensive - not only in wage repayment - but in potential fines from regulators and fees to external consulting/accounting/law firms to understand and resolve your underpayments issues. Underpayment at best is expensive, time consuming, and distracting to your organisation. At worst, it could kill your organisation. Finally, to your employees - your most important assets - underpayments represent a crucial failure in the mutual obligation you both have to do the best for one another. How can an organisation expect the best out of its staff if it can’t pay them properly? How can I use people analytics to anticipate and resolve underpayments issues before they become major scandals? Our experience is that large organisations have at a minimum seven different systems in which they store employee information. In larger and group structured organisations that total can be much higher - in some cases more than twenty! Many of the high profile instances of underpayment, especially those where the organisation has self-reported, indicate that the organisation was unaware of the issue until it was too late. This further indicates that they didn’t have the internal capability to understand what was occurring with their people data and aggregate it in a meaningful way. Part of the reason we created One Model is to fix this exact problem. One Model enables our customers to aggregate their disparate people data into one system (hence the name - One Model), so that they can more comprehensively understand their organisation and avoid systemic issues like underpayments. The ability to compare data in different systems and flag any discrepancies is a core feature of the One Model platform and a feature that is required to avoid the technology and process issues outlined above. Every conversation we have with a customer or prospective customer begins in the same place - organisations are worried about the quality and accessibility of their people data. Sadly, this is often used as an excuse not to invest in people analytics. However, if we have learned anything from these underpayment scandals, it is that you need to take control of your systems, processes, and data. Implementing a people analytics capability helps you achieve this. Internally, we often talk about the importance of One Model aggregating your data to become a single source of truth for all of the people data in your organisation. It's essential to trust the information that is presented to you and make confident decisions based on accurate information. Underpayments are something that your organisation needs to get right and we think accurate people analytics is one of the tools that you can use to get it right. One Model wants to work with enterprises to make sure that these issues are discovered and resolved before they turn into existential threats to your business. If you would like to continue this conversation and learn how One Model can help, or have any feedback on this blog, please comment below or click here to schedule a chat.
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Featured
5 min read
Tony Ashton
In the last One Model product update post I talked about our new user experience and hinted at some exciting new developments on the horizon. In this post I want to share some more information on those future designs. Thanks again to our customers for sharing their time and collaborating with us on our UX developments and everyone in One Model, but I want to make a special mention of the powerhouse behind One Model’s product designs - Nicole Li - a true UX unicorn! While the new user experience showcases Nicole’s incredible work, the new stuff is where things get super exciting. This content is being shared to provide an insight into future product developments planned by One Model. This should not be interpreted as a commitment to deliver any particular functionality or to any defined timeline and may be changed at any point by One Model at its discretion. Purchasing decisions should be made based on current product capability only. Having said this, we are super excited to share what we are working on and actively engage with you regarding product innovation and the future of people analytics. At the risk of overusing some classic cliches; startups run on pizza and business runs on PowerPointTM (well slides anyway). The slides phenomenon has been prevalent for the last couple of decades and when I ask almost any company how they share information with managers, executives, boards, or in general meetings the answer over 90% of the time is “slides”. Storyboards & Slides When we recently announced One Model’s new Storyboard capability we hinted at a broader vision and here is part of that vision starting to unfold. The new Storyboards will have two modes, one where you have a fairly traditional tile based layout and the other where you are in presentation mode. Online interactive use of One Mode is growing rapidly, but much of the content from One Model still ends up in a presentation at some point, so we want to reduce the effort to create and maintain this content. Storyboards are then acting as both a modern interactive storytelling dashboard and interactive slide based presentation without the need for any rework. This will save a massive amount of time and also pre-positions the content for the most common destination to meet the consumers where they are. So, how does this work? The Storyboard view lets you arrange tiles on a forever vertically scrolling space with control over layout, size etc. You can then flip to Slides view to get an auto-arranged presentation with one tile per slide and controls to optimize the display for presenting to a group of people. Within the Slides view you can manage layout, decide which tiles to show or hide from the presentation, combine slides together etc. To help you create a narrative for your presentation you can open the outline view and craft the flow of your story. Being able to present online from within the One Model platform is powerful and provides you the ability to interact with the data in real-time to really engage with your audience. And, “yes”, to the question you are starting to form in your mind… you will be able to export this to PowerPointTM to blend with other presentations you are creating offline :) Telling a Story Using Narrative Insights Having assembled a compelling set of data isn’t sufficient to drive action. You also need to engage your audience and the best way to do this is through storytelling. The next major feature to our Storyboards vision is the ability to add a narrative to any tile that describes what is going on in straight-forward business language. Initially this capability will include information from One Model’s metric library and your own narrative, but over time we will incorporate insights powered by the One Ai machine learning platform. Captions can be rearranged as elements within a tile, or a separate, linked, tile with controllable positioning and layout depending on how you want to arrange your storyboard. The Storyboards vision is incredibly exciting and customers we have engaged in the design thinking behind these innovations can’t wait to get their hands on this new capability. Neither can we! Stay tuned for more information as the roadmap unfolds. This article has been primarily concerned with the developing technology of Storyboards, but I also want to let you know that One Model has a vast library of content to help you tell the story of how people drive impact in your business. We’ll write some more on this soon, but reach out if you want to learn more about our metric catalogue and ever-growing library of topic storyboards. When combined with OneAi, our Machine Learning platform, you can generate automated insights, future forecasts and identify key risks to answer the most pressing business questions you have today.
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10 min read
Tony Ashton
Here at One Model, we are incredibly excited to have Tony Ashton join us from SAP SuccessFactors as our first Chief Product Officer and there is no better way to introduce the company’s first Chief Product Officer than for Tony to share his thoughts directly below. Why One Model? I'm incredibly excited to join the One Model team as the Chief Product Officer. I'm writing this blog to share my enthusiasm for One Model and also a bit about my background to hopefully serve as a guide to how we drive product innovation going forward. So why One Model? Simply put, One Model is doing the most exciting, innovative work in the people analytics space today. People Analytics is one of the most complex analytical domains due to the variety and complexity of the data. Even in systems purporting to provide a complete suite of integrated HR solutions the underlying data models for all of the different functional areas remain varied and complicated, if not impenetrable. Just think of the underlying data models within the core HRIS or Recruiting, Performance, Succession, Payroll, Benefits, Learning et.al. Then overlay concepts like date effectiveness, position management, multiple occupancy, changing organizational structures! I could go on. The One Model team has decades of experience in dealing with this specialized HR data domain and is the best company in the world at transforming all this data into one unified data model. No other company is going as deep and innovating as fast as One Model on the data modelling side and this is essential for success in People Analytics. Good clean data is critical for data science and most data scientists have to spend over 80% of their time assembling, organising and cleansing data. One Model solves this problem and provides scalability for data science. Beyond this, One Model is also leading innovation in the areas of Artificial Intelligence and Machine Learning in the People Analytics space. AI & ML are massively over-hyped, particularly when applied to the Human Resources domain. In large part this is because most data science in HR is based on one-off projects and not built to scale. The One AI platform One Model has built - it isn’t a generic toolkit, it is purpose built for developing insights for People Analytics. The introduction of Artificial Intelligence, Machine Learning & Robotics are now starting to drive real change and this has an incredible impact on how work gets done in business today. Analytics provides understanding and through the use of advanced technologies like One AI you are able to model the future, build alternate scenarios, understand the things that are driving change and take control of the future of work. I’ll talk more about “Why One Model?” at the end of the blog, but now want to turn to how I see the world of people analytics product management. Customer centricity and deep understanding. When building products you always need to think from the outside-in to understand the real problems you are trying to solve for your customers and this is my philosophy. The contemporary term for this is the "jobs-to-be-done theory", which has been around for a while and basically says you should focus on the task someone is trying to perform, or the outcome they are striving for and then design your solution to help achieve that outcome. When you say it out loud it is incredibly obvious, but then most of the best ideas are. Here's a great quick primer for you that's also a fun read: https://hbr.org/ideacast/2016/12/the-jobs-to-be-done-theory-of-innovation. (I have hired many donuts in my time - this will make sense when you read the article 🙂 ) I'm excited by ideas. Big ideas and concepts are important. I studied philosophy and history (with a focus on the history of innovation) at university and this passion nicely intersected with my business life when I read Clayton Christensen's quintessential book "The Innovator’s Dilemma". This book was ground-breaking in 1997 and the concept of the “Innovator’s Dilemma” is now part of popular parlance, but the principles are still impacting business today, so I recommend you have a read if you haven't already been there. Going further back, I was also influenced by Thomas Khun's seminal book "The Structure of Scientific Revolutions", where he coined the phrase "paradigm change" before it was hijacked for pop-psychology purposes. We are in a time of revolutionary change right now and understanding this is critical for success. Making something great. A mantra I borrowed from my good friend Philip Haine when we would work on new product designs together was this phrase he would often use: "What would be amazing?". You can run a detailed design thinking process and generate lots of ideas and this is a great structured way to involve people in designing a solution based on empathy, etc. but standing back and thinking about a problem from the perspective of the person at the center and simply asking “what would be amazing” for them is an awesome way to cut through and quickly generate truly ground-breaking solutions. Another mentor along the way was Dmitri Krakovsky, who would always ask this simple question of any project: "Is it great?". Mid-way through a product development cycle, if you sit back and ask yourself "is it great?" and it isn't then you should seriously think about what you are working on and why. Applied Technology & Innovation. Building on the 'making something great' discussion above, the best technology becomes seamlessly integrated with your work/life and genuinely helps you get things done. It should also be cool and fun to use. Have a think about what apps you like to use. Some I use everyday include Pocket, Flipboard, Slack, Evernote, Dropbox, Google Maps. What do all these apps have in common? They have a focus, they do what they do incredibly well and don't try to be something they aren't. Why isn't enterprise software like this? Why are all analytics products just like using a big spreadsheet, or so complicated you need a degree in statistics? Some products look pretty and appear simple to use, but often when you dig into it they just don’t deliver the goods. It doesn't need to be this way. The Art & Practice of People Analytics. When I found myself working in People Analytics I felt I found my calling. Building my skills along the way I recall a seminal event was when I attended an HR technology conference and saw an amazing presentation by Peter Howes on multidimensional analysis in HR using what was ground-breaking technology at the time. I then went to one of his workshops on how to measure the Return on Investment (ROI) of HR Interventions. Not long after I met Peter's business partner Anastasia Ellerby through a public sector project measuring and benchmarking the effectiveness of the Human Resources function. Through this project I started using their company's products as a customer. The company was Infohrm. With the help of serendipity I started working for Infohrm and the company built the most impactful workforce analytics and workforce planning products, practice and community in the world - it was cool. This then continued through two acquisitions, first by SuccessFactors and then by SAP. What always keep me engaged was working on the cutting edge of innovation in the field, working with companies all over the world and working with a great team of passionate people. We managed to build some innovative stuff, but I found myself in a 90,000+ employee company and it was becoming increasingly more difficult to deliver focused people analytics and planning innovation for customers and I wanted to get back to my passion. Everything I have discussed above dovetails perfectly with what One Model is all about. We are passionately creating the world’s most amazing technology specifically designed to help you deliver people analytics insights that accelerate decision making and drive positive outcomes for your business and a workforce planning capability that helps you plan, forecast and built a talent strategy for today and tomorrow. I’ve now written a much longer blog that originally intended, but hopefully it shows how enthusiastic I am for this domain and for this new role. The One Model team is incredible and the product is awesome. We have shared history and shared values stemming from the original Infohrm company and we do whatever it takes to make our customers successful. I'm grateful for the opportunity and super excited by the innovations we are cooking up and can't wait to share these with you soon. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Learn more at onemodel.co. About One AI - Trailblazer Trailblazer is One Model's newest way of helping leaders incorporate workforce analytics and distill big data into every HR decision for recommendations that are smarter, faster, and more efficient. The Trailblazer program does this by giving HR teams access to the only openly configurable, HR-focused, automated machine learning engine in the world: One AI. Introductory Offer: Try Trailblazer out for a month - $1,000 Visit onemodel.co/trailblazer-program to learn more.
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