What does “data science” have to do with talent optimization?
Updated: May 10
Data science is a buzzword these days—and, as a data science manager, I’m excited about that! But what is data science? And how can it help your talent optimization efforts?
What is “data”?
Very simply, “data” is information.
But more than that, data is stored and retrievable information. It’s information you can retrieve and work with. What sets useful data apart is that it’s stored somewhere safe (i.e., you won’t lose it if you need to restart a computer or if someone leaves the company) and it’s accessible (i.e., you can retrieve it when you need it). A human resources information system (HRIS) or a customer relationship management (CRM) system are examples of places you can store and retrieve data.
Good data is also accurate, complete, and important—but don’t sacrifice accuracy for completeness. Keep in mind the axiom: “Bad data is worse than no data.” Finally, think carefully about what to collect and store—such as quality people data.
What is “data science”?
Ask a dozen data scientists what “data science” is, and you’ll probably get a dozen different answers—often mentioning machine learning and AI. Maybe you’ll get a tongue-in-cheek answer, like “data science is statistics with better marketing.” But at its core, it’s really quite simple:
Data science is about making connections between different sources of data.
It’s meant to help you make better, more informed decisions.
Maybe you want to understand the cyclical nature of your sales, so you look at your sales numbers (data source No. 1) as a function of the day/week/month (data source No. 2). Perhaps you want to see if scores on a pre-hire assessment predict customer ratings for customer service reps. Or maybe you want to understand how employee engagement is related to turnover at your company. All of these are data science questions—seeking to understand the relationships and connections between different data sources.
Typically, the actual mechanics of retrieving and connecting the data sources, forming specific hypotheses, analyzing the data, and communicating the results are the responsibility of a data scientist. But asking data science questions should be the responsibility of every strategic leader looking to optimize their talent strategy.
Thinking strategically about data.
If you want to leverage your company’s data to solve challenges relating to your business and talent strategy, here are a few tips:
Build a data culture.
To really leverage the power of data science in your organization, you need to build your culture in a way that gets everyone focused on data. Everyone should be asking questions like “What can we collect?”, “Is there some additional data that would help me make a decision?”, “What if we connected these two sources of data?”, or “How can data solve this problem I’m struggling with?”
Getting everyone to start thinking in terms of data will lay the groundwork for success at your organization.
Measure what matters.
You’re probably already tracking a lot of the data that matters to your organization—such as performance metrics, sales figures, etc.
But have you thought through what’s important to measure for each challenge you face?
For example, if some high-performing employees are leaving, are you collecting the talent data (e.g., engagement scores) you need to make informed decisions on how to refine your talent strategy? Are you recording why they’re leaving?
By giving serious careful consideration to what’s important and what problems you’re trying to solve, you can start recording what really matters to your organization so you meet your talent and business objectives.
Make the data accessible.
Not everyone at your organization needs access to all the data all the time. Some of it may be confidential or proprietary. But the right people certainly need reliable access to the important data you’ve collected.
Whether you choose a cloud-based database (e.g., Salesforce) or a self-managed database, having your valuable data accessible to the right people is critical to begin finding insights and solving challenges.
Once you’ve laid the groundwork, it’s time to start leveraging your data. Working with people skilled in data analysis and data science who can communicate the results of their findings will provide you with the insights you need to connect your business and people strategies.