With all of the buzz around people analytics, you might assume that most organizations are making decisions based on predictive recruiting methods and machine learning retention algorithms. But, you’d be wrong!
A recent Bersin study shows that 86 percent of People Operations teams have yet to scratch the surface of advanced analytics, with the majority, 56 percent, still relying on ad-hoc reporting methods and manual data entry. And yet, companies like Google and JetBlue continue to benefit from their people data and analytics capabilities.
As Matt Hoffman, VP People at DigitalOcean, has noted, “the ability to use data to inform and drive decisions that impact employees is one of the biggest things that differentiate good People organizations from great ones. It helps ensure the programs you deliver to your company will be more powerful and meaningful, and gives People leaders more credibility and a bigger voice at the table when advocating for decisions on behalf of their organizations and their employees”
The forerunners in this field have shown how efforts to impact diversity, engagement, attrition and hiring can all benefit from leveraging people data. So, what’s happening here? If the opportunity is so vast, what’s holding everyone back from taking advantage?
Welcoming People Ops to the Data Party
People Ops is facing the same challenges with people data that marketing, sales and product teams have had to overcome with customer data. Executives want to see data-based information on ROI, yet struggles with data management, accuracy and consolidation have proven to be significant barriers to realizing the potential of people analytics. Unfortunately, People Ops’ fashionably late appearance to the data party doesn’t come without these challenges.
At Shape, we’ve dedicated time to understanding, identifying and addressing these problems so that you can focus on what matters most, helping your people do their best work. To guide you in this process, we’ve outlined four of the most common data challenges and how you can overcome them.
Challenge One: Data Management and Organization
“Not everything that can be counted counts, and not everything that counts can be counted.” — Albert Einstein
You’ve likely been collecting people data for years within your HRIS, recruiting, ad-hoc spreadsheets, payroll, performance review tools and more. Before beginning to clean and analyze this data, it’s essential to understand the data at your disposal — where it’s located and how it’s managed.
A great first step is to make a list of all of the People Ops tools you’ve used over the years that could be holding valuable information. The list might include, for example, BambooHR, Zenefits, Greenhouse, Lever and CultureAmp. It’s likely that you also have spreadsheets and smaller tools floating around as well, so be sure to connect with all relevant parties to compile the most accurate and up-to-date list possible.
Beside each tool, outline the types of data that could potentially be extracted. These might include employee location, gender, tenure, performance reviews, salary, hiring manager, promotions, ethnicity, training history and more.
The goal of this exercise is to create a snapshot of your organization’s people data. This framework will guide the data cleansing and consolidation process, as well as help you to address any notable gaps or inconsistencies early on.
Challenge Two: Cleansing Old, Dirty, Inaccurate Data
47% of companies have People Operations software that is more than seven years old, according to a Bersin report. The data collected by this software (or more often, multiple pieces of software) has likely passed through multiple hands with varying degrees of uniformity throughout time. This can make even the most trivial questions like, “what is our turnover rate?” or “how many employees do we have?” difficult to answer.
A deep cleaning will ensure the accuracy of future analysis and will set the stage for data consolidation. While the process itself may seem tedious, a thorough data scrub should only have to be done once to prepare a clean foundation for the future.
Cleansing consists of combing through relevant data, removing any duplicates and inconsistencies in the process. At Shape, we start by reviewing all of the data sources that our customers can supply us with. Then, we conduct some fundamental analyses that quickly reveal any gaps, inaccuracies or areas that require attention. For example, if one data source shows that the company had 234 employees in March of 2016, but another shows 215 employees for the same period, this immediately raises a red flag. Our team then works to correct any inconsistencies in the data.
Prepare yourself — it takes far more time to extract and clean data than it does to perform any statistical analysis. Moreover, the quality of the analysis will only be as good as the underlying data. So, before rushing off to build predictive models, it’s essential to dedicate this time to giving your data a good spring cleaning!
Challenge Three: Data Integration and Consolidation
Are you at risk of losing your top performers? How can your engagement scores inform compensation decisions? You can begin to ask and answer questions like these when your people data is integrated and consolidated within a single platform.
Traditionally, you might refer to a recruiting tool for information on hiring, or a performance feedback tool to understand if individual departments are outperforming others. But, by looking at one tool at a time, you see an incomplete picture of your employee experience. It’s important to inform your decisions with consolidated data from all your tools.
For example, you might be interested to learn if certain hiring managers deliver more successful candidates than others. Looking only at your recruiting platform, you can quickly see how many hires a manager has made. But, what happens after the manager makes a hire? Does the new employee become a company rockstar? Do they exceed performance expectations? Or, do they leave after their first few months?
By unifying disparate data sources, you can ask deeper questions of your people data and gain a more comprehensive view of your employees. Consolidating data empowers you to make meaningful, data-driven change within your organization.
At Shape, we’ve built a software solution dedicated to doing just this. Our platform integrates with the dozens of tools that you already use to unify your people data, and helps you answer questions that were previously impossible to ask.
Challenge Four: Embracing an Analytical Mindset
The effort to aggregate, clean and consolidate your data is only worthwhile if you do something with it. Knowing what questions to ask of your data and what to do with the insights you gain is the next step in harnessing the power of people analytics.
Embracing an analytical mindset allows teams to uncover patterns, trends and inconsistencies that lead to action. As employees become comfortable mapping people data directly to business outcomes, they can begin to ask deeper questions, for example, which high-performers are most likely to create next quarter’s disruptive feature?
A key part of this mindset is recognizing when the data does not show statistically significant results. Many organizations have suffered from making decisions based on models that claim someone is going to leave the company, or that another should be hired, without realizing that the model is simply not good enough to make reliable predictions. At Shape, we take a mathematically rigorous approach to evaluating and presenting our confidence in each prediction we make, and we suggest you do the same. Understanding the limitations of your data is just as important as understanding its potential utility.
Keeping this in mind, we believe you’ll find the effort is well worth the reward. We’ve seen our customers harness their data to improve productivity, employee retention, diversity, workforce planning and data-driven recruiting. The opportunities that lie ahead with your people data are limitless, but only with the right preparation.
Ready to start harnessing the power of People Analytics? We can help! Reach out to our team at Shape to get a free people data audit!
