Analytics for Talent Management

The age of Big Data is here, and just about every business knows they ought to be tapping into it to generate actionable insights and better business decision-making. Generating reams or gigabytes of data, however, is the easy part. Without the analytics tools and expertise to make sense of that data, the promise of Big Data will remain unfulfilled for many companies.

makingsenseofbigdata

Some prefer to call this Business Intelligence (BI), and there’s some truth in their preference. The data itself remains largely meaningless until it has been analyzed and interpreted in a way that turns mere data into intelligence. Consider what the Central Intelligence Agency (CIA) does for the president. It collects astonishing amounts of information and data from field operations, but that’s just the first step in the process. All that information has to be analyzed and processed into extremely concise intelligence that is then handed over to the president in order to help him make better decisions. It’s really no different in business.

Data-driven decision-making in talent management and human resources is both possible and desirable. What compensation packages attract the best candidates? What qualities and skills are the best predictors of performance success in this particular company? Which recruitment sources out there are most likely to offer the kinds of candidates the company seeks?

A commitment to being data-driven opens up the possibility of removing much of the bias and discrimination rampant in many hiring processes. It also holds the promise of reducing costs and speeding up the talent acquisition cycle. It would be a mistake to say the algorithms that help automate aspects of hiring can replace recruiters, but they can make recruiters more effective.

The Right Tools

Software offerings around analytics are becoming increasingly sophisticated and powerful. More and more TM/HR platforms have added analytics functionality, with varying degrees of success. A few features to keep in mind when evaluating TM/HR analytics software include the following:

  • Easy visual prioritizing of candidates, perhaps with simple red, yellow, and green indicators.
  • Clear potential problem indicators for different areas such as recruitment, retention, performance, etc.; the economic impact of such problems; how such problems related to specific corporate goals; and the probability of such problems occurring.
  • If it’s not already clear, the most utility is to be gained from predictive analytics because that’s what’s needed to make a real, data-driven impact on outcomes.

That last point about predictive analytics is the driving force behind a changing TM/HR software landscape where companies are scrambling to add predictive analytics to their offerings. This is why Workday acquired Identified, Cornerstone OnDemand acquired Evolv, and SuccessFactors acquired Workforce Analytics for HR (and SuccessFactors has since been acquired by SAP), while LinkedIn acquired Bright.com.

The Right People

In addition to the right tools, companies would do well to develop or hire-in data analytics expertise that knows how to use these tools and get the most out of them. This means having people on board with particular skills, including the following:

  • Analytical and critical thinking.
  • Problem-solving.
  • Drawing conclusions from data analysis.
  • Presenting and communicating findings.
  • Decision-making.

There are a growing number of options out there for training people in analytics without going so far as becoming full-fledged data scientists. Here are several options to consider:

Having both the right tools and the right people to implement and leverage data analytics in talent management and human resources is proving to be a high priority for companies and organizations in 2016.

June 25, 2016   Updated :November 16, 2016   analytics, big data, data, data analytics, talent management   

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