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3 min read
The One Model Team
One Model was founded around a goal of helping teams tell data-informed stories that lead to brilliant, data-driven talent decisions. By leveraging data and story, we can help teams communicate a deeper understanding of the tangible benefits of diversity, equity, and inclusion initiatives, and how they contribute to the success of a business. Data-informed stories can be a powerful tool for uncovering how the work environment is impacting our employees. Through data, we can demonstrate the positive impact of treating people well, and how this can drive business success. Let’s walk through one of the “classic stories” we hear in HR and people analytics for a fictional organisation called Innovative Enterprise. We’ll start with the story, introduce the data, and then apply One Model’s data-informed storytelling framework to the story to show how our platform easily weaves the narratives together. This is a common story that HR teams are asked to tell around employee experience and the impact that a positive work environment can have on the overall business. Story alone: Within Innovative Enterprise, while we have a diverse workforce, this diversity is yet to permeate our leadership effectively. Our leadership team, although competent and committed, does not fully represent the diverse perspectives present within our broader team. This lack of representation in leadership could potentially influence our culture and engagement levels. Data alone: Internal data at Innovative Enterprise shows that while 49% of our workforce identifies as ethnically diverse, only 15% of our leadership does. Recent industry studies that the people analytics team analysed indicate that organisations with diverse leadership teams outperform those without by 35% in terms of innovation and creativity. Moreover, organisations that boast diverse leadership report a 25% higher employee satisfaction score compared to companies with less diverse leadership teams. Data story: At Innovative Enterprise, the lack of diversity in our leadership team becomes evident. Our internal data reveals that while our workforce is 49% ethnically diverse, only 15% of our leadership reflects this diversity. It's clear we're falling short, and this is a challenge that we share with many organisations across our industry. However, industry data provides a clear directive: organisations with diverse leadership teams are more innovative and creative by 35%. They also report a 25% higher employee satisfaction score, indicating a more engaged and motivated workforce. This compelling combination of our internal situation and broader industry data paints a powerful argument for enhancing diversity, equity, and inclusion at the leadership level. The data provides clear guidance — it's time for us to take action. Ready to learn more This example from Innovative Enterprise demonstrates the power of data-informed storytelling in HR. For more impactful stories and detailed analysis, download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organisation tell compelling, data-driven stories.
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Featured
7 min read
The One Model Team
If you're preparing for people analytics, there’s a lot to do before you hire that first data scientist. To build the right foundation for success, there are five important steps you should follow that don’t even involve data, insights, or statistics. Following these steps will help you establish and support an efficient and impactful people analytics practice at your organisation. 1. Find your why Understanding why you're pursuing people analytics is vital to your journey. This not only means identifying the specific business needs that would benefit from a better understanding, deeper insights, or more precise analysis of your workforce, but also exploring the underlying reasons behind those needs. You could start by asking questions like: What are the biggest challenges or pain points we're facing as an organisation? What are the key areas where we could improve our workforce, and how would we measure success? What are the most critical business decisions we need to make, and what do I need to know to help us make them more effectively? What are the specific gaps in our knowledge that we need to fill in order to make better decisions? Without taking the time to find the why for your organisation, you risk getting lost or going off course before you even begin. By finding your why early and holding onto it through the process, this will keep you focused throughout your people analytics journey. 2. Look upstream When starting your people analytics journey, it’s important to remember that the data you’ve generated is only as good as your processes and technology. There’s a flow we like to think about from process to tech to data to analytics. When people analytics teams run into challenges, there’s likely an upstream challenge in one of these steps to address. Begin by examining your processes. Technology is only as good as the process it’s automating, so if your processes are poorly designed and documented, your technology is unlikely to be implemented correctly. Technology should reflect how you want your business to run. If it doesn’t, you’ll likely end up with incomplete or incorrect data flowing out of the technology — making it difficult or impossible for people analytics teams to create value.This is not to say “don’t start on people analytics until the rest is done”. People analytics teams can absolutely provide great value, and some of the best teams out there are scrappy with what they have on hand. This is more of an acknowledgement of the flow and a callout that if you want long-term success of your people analytics team and to unlock that next level of value, you’ll have to address these upstream challenges. A strong people analytics leader will also be able to help you identify and navigate these challenges upstream. So begin by ensuring that your processes are well-designed and documented. Next, double check on your technology implementation and ensure that it matches your processes. Finally, check in on the data. The data ultimately doesn’t lie, so it will tell you if the processes and tech are clean. Doing so will ensure that data flows smoothly and accurately from the technology preparing you for analytics. 3. Address data management Another early focus for starting down the path of people analytics is data management. Without data, there’s not much for people analytics teams to do. It’s the oil to the people analytics engine. We’ve seen a number of teams get started, but then plateau around a lack of good data. At times the resources to fix data problems sit outside of HR, which makes it all the more important to navigate and commit that resource request up front when pursuing people analytics. Making sure your data is accessible is critical, but raw data extraction is also only the beginning. A robust workforce-specific data model, proper data architecture blending your different systems data, and HR-led workforce data privacy and workforce data governance are also part of your people analytics foundation. This may require marshalling what are typically scarce internal resources, capabilities, and priorities from IT or data engineering teams to ensure that your data is clean, systematically organised, and readily analysable. Or you can save those internal resources by working with people analytics platforms like One Model. We were founded to make this upstream challenge easier. We provide named data engineering resources, have experience developing business-specific workforce data models, and provide the data foundation that people analytics teams need to thrive. If you skip this step, you may experience the following problems: Missing data: Without the right data management structure in place, you may find it difficult to extract the data you need for a given project. This can lead to incomplete or incorrect data and difficult analysis. Slow data: Improper data management can leave you with only monthly (or quarterly!) snapshots and that pace just doesn’t reflect how fast your business moves — let alone back-dated changes, which are frequently found in HR. Inability to build predictive models: Data management is critical to building predictive models. To develop predictive models, you need to extract data in a very specific way (e.g. time-stamped changes). It’ll be difficult or even impossible to build accurate and effective models without this proper data management. By addressing data management early on in your people analytics journey, you can avoid these symptoms and ensure that your people analytics initiatives are successful. To learn more, here are five tips for getting HR data extraction right. 4. Set the tone Setting the tone at the top is crucial for demonstrating that data-driven decision making is the way forward. This involves garnering support from your organisation's senior leaders, as well as regular reminders, activities, and actions from the CHRO or HR head. If you’re in a leadership position, setting the standard that data is required for new projects and investment decisions goes a long way. Cultivating a data-minded culture will trickle down from the top, setting a precedent for the entire organisation. Without this high-level endorsement and sustained backing, making significant strides in people analytics can prove challenging. 5. Find help Consider engaging with a seasoned people analytics leader either full-time or as a consultant to spearhead your people analytics initiatives and education within your function. Experienced people analytics leaders, with their unique combination of data analysis skills, HR orientation, ethical understanding, and team management expertise, can provide invaluable guidance. They’ll work to ensure alignment between your analytics efforts and broader business objectives. Remember to also tap into the people analytics community. This strong and enthusiastic network can provide invaluable support. Engage with professionals on LinkedIn, ask questions, and use the expertise of vendors in the space. The team here at One Model is always willing to connect and assist at every stage of your people analytics journey. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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Featured
5 min read
The One Model Team
A well-crafted data-informed story can effectively influence decision-making, foster understanding, and drive meaningful change within the organization. A data-informed story blends the art of storytelling with data-driven insights, creating a compelling narrative that resonates with the audience and inspires action. Here's a framework for how to develop data-informed HR stories: Business Objective: Setting the Stage Every compelling data-informed story begins with a clear business objective. It's essential to know what you want to convey and the actions you want to inspire from your audience. Defining your objective gives direction to your story, shaping its structure and maintaining its focus. A well-articulated objective ensures your story remains purposeful and impactful, driving the narrative towards your desired outcome. “Using people data to get to a scientific insight is only half the battle. If you can't step back and crisply describe your findings in terms of business impact, you quickly lose the room, lower credibility, and break trust with business leaders.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Evidence: The Backbone of Your Story Your story's credibility stems from its findings: both data evidence and the story context. Data Evidence: Collect and analyse data pertinent to your objective. This data acts as the backbone of your story, supporting your narrative and revealing valuable trends, patterns, and insights. It's the facts and figures that make your story believable and persuasive, reinforcing your arguments and enhancing your story's validity. Story Context: Context adds depth to your data, making it meaningful and relevant. Explain why your data matters, its relation to broader organisational objectives, and its direct impact on your audience. This context helps your audience comprehend the data's significance, allowing them to connect the dots between raw data and its implications. Visualization: Bringing Your Data to Life Visualising your data helps to clarify and accentuate your key messages. Rather than presenting raw data or lists, craft clear and engaging visual representations of your data. This could involve charts, infographics, or diagrams, which enable your audience to quickly grasp the information and easily identify the patterns or trends you're emphasising. Narrative: The Art of Engaging Your Audience Narrative is the act of weaving together data and insights into a compelling story that resonates with the audience and inspires action. By using an engaging narrative, relatable examples and analogies, and emotional appeal, HR professionals can effectively communicate the human impact of organisational decisions and drive meaningful change. “To infuse more storytelling into People Analytics, understand the business and people context, use narrative techniques and visualisations to present data engagingly, and go beyond data by exploring the human factors driving it. Enhancing storytelling in this field can significantly boost its impact on business outcomes.” — Tony Truong, Vice President of People Strategy and Operations, Roku Engaging Narrative: To captivate your audience, weave your data and insights into a compelling narrative. Ensure your story flows logically, featuring a beginning, middle, and end, each part reinforcing the key message you wish to convey. Relatable Examples and Analogies: Examples and analogies act as bridges between complex data and familiar concepts. By relating your data to real-life scenarios or recognisable concepts, you make it more accessible and understandable for your audience, making your story more relatable and engaging. Emotional Appeal: The magic of storytelling lies in its ability to evoke emotions. Incorporate elements that resonate with your audience on an emotional level. This could involve personal anecdotes, inspiring stories, or connections between the data and the organisation's values and goals. “People Analytics insights have an easier path to landing as a compelling story if quantitative findings are combined with qualitative findings. Pulling anecdotes from HR and non-HR leaders, managers, and employees in your business lines is a validating and powerful storytelling device.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Interactivity: A Living, Breathing Story Data stories are not static monologues but dynamic dialogues. Build your stories in a way that allows you to be prepared for follow-up questions and additional requests. Consider building your data stories in platforms where you can treat them as living documents, flexible and adaptive, fostering interactivity and ongoing engagement. This approach will enrich your narrative, keeping it relevant and resonant over time. Action: The Impetus for Change The goal of any data-informed story is to inspire action. Conclude your story with a clear call to action, outlining what steps you want your audience to take based on the insights presented. This crucial step ensures your story doesn’t merely inform but also drives engagement, leading to tangible change. Ready to learn more? Download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organization tell compelling, data-driven stories.
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Featured
4 min read
Dennis Behrman
Phil Schrader and Stephen Haigh had an opportunity to attend the People Analytics World Conference in London April 26-27, 2023. During their visit, Phil was asked to give a public demonstration of how HR analytics software works. While we can't speak for other people analytics tools, we can speak to One Model. The crowd was mesmerized and had lots of questions at the end that you definitely have to watch. Join Phil as he walks through data import, export, and all the magic in between — even showing in real time how an AI model is built exclusively on your data. Phil, always cheeky and fun to watch, is a great teacher in all the things you should look for when assessing which people analytics tool is right for you. Compared to other HR analytics tools on the market, you'll quickly see that One Model is more transparent, easier to use, and more open than any other option on the market. Want your own personal tour of One Model? Request time to meet today. During the video, Phil walks us through each of these layers: The Consumer Layer: At the top of the platform, users, such as HR Business Partners, can access data, insights, and storyboards through a user-friendly interface. The storyboard feature allows users to interpret data visually and navigate through various tools like Explore, Storyboards, and Data. These tools enable users to slice and dice analytics, explore heat mapping, and gain insights into different data sources. From Consumer to Analyst Layer: One Model's flexibility empowers users to transition from the consumer layer to the analyst layer effortlessly. Here, analysts can customize the views, rearrange elements, and dive deeper into the data. With simple clicks, they can transform data into charts, change metrics, and connect multiple systems to gain a holistic view. Configuring Metrics and Data Engineering: As analysts continue their exploration, they can configure metrics according to their organization's specific requirements. They can modify calculations, adjust inclusion/exclusion criteria, and create unique views tailored to their audience. Furthermore, One Model offers transparency into data engineering, allowing analysts to delve into the underlying data models, processing scripts, and data sources. Unleashing the Power of Data Science: Finally, One Model empowers advanced analysts and data scientists to build predictive models. With the augmentation feature, analysts can create and maintain multiple models, evaluate their performance, and put them on schedules. The platform provides a guided walkthrough for model building, enabling users to define their objectives, select relevant metrics, and generate predictions. The prediction capabilities extend to specific employee segments or the entire population.
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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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Featured
8 min read
Phil Schrader
Last week I was doodling some recruiting graphs in my notebook, with an eye toward building out some new recruiting efficiency dashboards. I was thinking about how requisitions age over time and I got an idea for a cool stacked graph that counts up how many requisitions you have open each month and breaks them out into age buckets. Maybe some supporting breakouts like recruiter, some summary metrics, etc. Something like this: Phil's Beautifully Hand-illustrated Cholesterol Graph (above) This would be an awesome view. At a glance I could see whether my total req load was growing and I could see if I’m starting to get a build up of really old reqs clogging the system. This last part is why I was thinking of calling it the Requisition Cholesterol Graph. (That said, my teammate Josh says he hates that name. There is a comment option below… back me up here!) But then I got to thinking, how am I actually going to build that? What would the data look like? Think about it: Given: I have my list of requisitions and I know the open date and close date for each of them. Problem #1: I want to calculate the number of open reqs I have at the end of each time period. Time periods might be years, quarters, months, or days. So I need some logic to figure out if the req is open during each of those time periods. If you’re an Excel ninja then you might start thinking about making a ton of columns and using some conditional formulas. Or… maybe you figure you can create some sort of pancake stacks of rows by dragging a clever formula down the sheet… Also if you are an Excel ninja… High Five! Being an Excel ninja is cool! But this would be pretty insane to do in Excel. And it would be really manual. You’d probably wind up with a static report based on quarters or something and the first person you show it to will ask if they can group it by months instead. #%^#!!! If you’re a full on Business Intelligence hotshot or python / R wiz, then you might work out some tricky joins to inflate the data set to include a record or a script count a value for each time the reqs open date is before or within a given period, etc. Do able. But then… Problem #2: Now you have your overall count of reqs open in each period. Alls you have to do now is group the requisitions by age and you’re… oh… shoot. The age grouping of the requisitions changes as time goes on! For example, let’s say you created a requisition on January 1, 2017. It’s still open. You should count the requisition in your open req count for January 2017 and you’d also count it in your open req count for June 2018 (because it’s still open). Figuring all that out was problem #1. But now you want to group your requisitions by age ranges. So back in January 2017, the req would count in your 0 - 3 months old grouping. Now it’s in your > 1 year grouping. The grouping changes dynamically over time. Ugh. This is another layer of logic to control for. Now you’re going to have a very wild Excel sheet or even more clever scripting logic. Or you’re just going to give up on the whole vision, calculate the average days open across all your reqs, and call it a day. $Time_Context is on my side (Gets a little technical) But I didn’t have to give up. It turns out that all this dynamic grouping stuff just gets handled in the One Model data structure and query logic -- thanks to a wonderful little parameter called $Time_Context (and no doubt a lot of elegant supporting programming by the engineering team). When I ran into $Time_Context while studying how we do Org Tenure I got pretty excited and ran over to Josh and yelled, “Is this what I think it is!?” (via Slack). He confirmed for me that yes, it was what I hoped it was. I already knew that the data model could handle Problem #1 using some conditional logic around effective and end dates. When you run a query across multiple time periods in One Model, the system can consider a date range and automatically tally up accurate end of period (or start of period) counts bases on those date ranges. If you have a requisition that was opened in January 2017 and you want to calculate the number of reqs you have open at the end of every month, One Model will cycle through the end of each month, check to see if the req was opened before then and is not yet closed, and add it to the totals. We use this for all sorts of stuff, particularly headcount calculations using effective dates and end dates. So problem one was no problem, but I expected this. What I didn’t expect and what made me Slack for joy was how easily I could also deal with Problem #2. Turns out I could build a data model and stick $Time_Context in the join to my age dimension. Then One Model would just handle the rest for me. If you’ve gotten involved in the database side of analytics before, then you’re probably acquainted with terms like fact and dimension tables. If you haven’t, just think vlookups in Excel. So, rather than doing a typical join or vlookup, One Model allows you to insert a time context parameter into the join. This basically means, “Hey One Model, when you calculate which age bucket to put this req in, imagine yourself back in time in whatever time context you are adding up at that moment. If you’re doing the math for January 2017, then figure out how old the req was back then, not how old is is now. When you get to February 2017, do the same thing.” And thus, Problem #2 becomes no problem. As the query goes along counting up your metric by time period, it looks up the relevant requisition age grouping and pulls in the correct value as of that particular moment in time. So, with our example above, it goes along and says, “Ok I’m imagining that it’s January 2017. I’ll count this requisition as being open in this period of time and I’ll group it under the 0 - 3 month old range.” Later it gets to June 2018 and it says, “Ok… dang that req is STILL open. I’ll include it in the counts for this month again and let’s see… ok it’s now over a year old.” This, my friends, is what computers are for! We use this trick all the time, particularly for organization and position tenure calculations. TL;DR In short, One Model can make the graph that I was dreaming of-- no problem. It just handles all the time complexity for me. Here’s the result in all it’s majestic, stacked column glory: So now at a glance I can tell if my overall requisition load is increasing. And I can see down at the bottom that I’m starting to develop some gunky buildup of old requisitions (orange). If I wanted to, I could also adjust the colors to make the bottom tiers look an ugly gunky brown like in the posters in your doctors office. Hmmm… maybe Josh has a point about the name... And because One Model can handle queries like this on the fly, I can explore these results in more detail without having to rework the data. I can filter or break the data out to see which recruiters or departments have the worst recruiting cholesterol. I can drill in and see which particular reqs are stuck in the system. And, if you hung on for this whole read, then you are awesome too. Kick back and enjoy some Rolling Stones: https://www.youtube.com/watch?v=wbMWdIjArg0.
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