We’ve all had that moment where we pull a report, sit down to analyze it, perhaps spend hours formulating insights, and then realize…the data is bad. Whether it’s incomplete, inaccurate, or unorganized, in the age of analytics, data can keep you up at night. The potential for people analytics has never been greater – from optimizing your workforce to hiring smarter, increasing performance outcomes to reducing attrition, the opportunity for the field of people analytics to create real business value is here. So how do you make sure the insights you’re presenting at the board meeting next week are grounded in fact and not just a hunch and feel?
Start with the WHY. Whatever you do, don’t gather data for the sake of gathering data. Understand the why for capturing information and particular sets of data and define the question you’re trying to answer. It’s difficult to sift through data that isn’t answering a question and just making noise in the face of the real answers you’re trying to get to.
Set consistent metrics for reporting. If you’re pulling data across multiple years, chances are the metrics differ year to year, unless you’ve had the same system and system owner. Make sure you understand the right metrics for reporting purposes, otherwise your YOY analysis will be skewed.
Develop a data dictionary. When it comes to data, reporting, and analysis, it’s important everyone is talking about the components of the data in the same way. Take the time to develop a dictionary that includes your reasons for capturing certain information, the defined metrics, and processes around data capture and share it broadly. What you want to avoid is confusing terms and everyone speaking different languages when it comes to your data.
Put processes in place around the input and capture of the data to ensure it’s consistent, clean, and free of bias. Bias in data can creep in either through biased data collection practices or through biased human behavior in the data generation process. If you can’t capture data in an automated fashion, you can minimize your bias and improve cleanliness by putting in processes surrounding your data input. Consider creating awareness around conscious and unconscious bias with your team.
Train your system users in how to accurately input information. Going back to the WHY of data capture, it’s important for system users to understand the intentions behind their inputs. A new system offers opportunity for everyone to learn how to input accurate data in accordance with your processes. Be sure new hires get the same training coming in so you maintain data consistency.
Safeguard your systems. It’s important to keep track of who administers which systems and who has access to input data (and export). For applications that are broadly shared, it’s harder to maintain data integrity and consistency, and your risk of data leakage increases. Ensure your onboarding and offboarding processes add and remove the right individuals in systems and tools.
Prioritizing clean and accurate data across the organization is difficult, but necessary in order to have an informative people analytics function. The insights you glean from data are only as good as the inputs. Consistent, timely, and accurate data is paramount as you begin your analytics journey. When you’ve taken these steps, you’ll be able to answer the question you set out. Tools like Peoplelogic.ai help you answer the important questions about the data you’ve captured.
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