Big data, people analytics, talent science… it’s difficult to read anything online in the human resource industry without running into articles discussing this new wave of change for Talent Management. John Bersin, Principle for the HR arm of Deloitte, stated, “It’s like Moneyball for HR, letting you make better decisions and this year it has really peaked.” Now, I’m not sure how many baseball fans we have in the audience but the concept still applies. At the most basic level, it’s another industry learning to use data to objectively make higher quality decisions about talent. However, the challenge for HR is the breadth and scope of the talent management function; so, where do you begin?
I believe the challenge is even more daunting for small and medium-sized businesses that don’t have the resources to focus on this full time. The thought of aggregating data into one mass database and hiring a full time talent scientist is beyond reason. The question that remains is how do I step into this and reap the rewards of these developments? The answer is simpler than you may think.
- Pick low hanging fruit – there are several tools in the marketplace that can be integrated into current HR processes. Today you have recruiting functions, performance evaluations, succession planning, engagement measurements and off-boarding/separations processes. How can you take market-available tools and easily embed them into your processes to dramatically improve your hiring, development, succession, engagement and separation?
- You can’t analyze what you don’t measure (or something like that) – big data analytics won’t be very useful in the future if you have minimal clean data. Talent management is often handled very subjectively and by untrained hiring managers. Make sure your tools and processes have objective measurements to them and can be used in the future as people analytics advances. You must start measuring today to build a timeline into your data for larger analysis.
- You must have consistent and common language – historically as companies try to implement tools for more objective hiring, development or engagement, it is done on an ad-hoc basis and through varying tools. These measures cause inconsistencies of talent management and will not provide accurate, reliable data for future analysis. For example, companies should have a common competency language for positions. This common language can then be used for hiring, onboarding, development, succession, engagement and separation Having consistent and common language for the entire employee life cycle allows for dramatic improvements immediately in talent management but will allow for much stronger data analytics down the road.
I suspect a number of you are saying, “That is a huge project and I’m sure it will cost a fortune.” But I would encourage you to investigate a little further. Recent advancements in this arena are providing ways to implement these tools far more easily and at a greatly reduced price point.
Today, I’m challenging you to step up to the plate of talent data analytics. It will be far simpler than you think and the rewards will certainly be far greater.
To schedule a demo with us, click here!