Talent Management - An Analytics Approach

An organization’s talent is by far their greatest asset and best competitive advantage. The best product or service on the market is marginalized if the approach to talent management isn’t well thought out in terms of acquiring, growing and moving talent to best suit business outcomes.

By definition, talent management refers to the anticipation of required human capital for an organization and the planning to meet those needs. I believe that is only half of the definition. Talent management should include human capital leaders in a consultancy and collaboration role with business leaders to define unique strategies that prepare and develop current and future talent for targeted business outcomes. In a sense, HR must be agile in their approach to talent management because one size does not fit all for different business units in the same organization.

There is an incredible amount of pressure today to perform well in business that the talent management strategies must deliver a competitive advantage. When partnering with your client leaders to identify the talent management strategies, I am an advocate that data and analytics should drive all planning and decision-making processes. If used properly, data and analytics can play an important role in devising targeted and effective talent management strategies.

Using data in talent management isn’t a new concept, and at the same time, many Human Resources teams don’t fully utilize a robust approach with the data from which to make decisions. Much of the data would appear to be lagging indicators, but if used effectively and creatively, the data can tell stories or at the least raise enough questions that can be an effective tool in developing talent management strategies.

There are four levels of Talent Analytics that provide different insight into talent challenges and opportunities.

Level 1 – Reactive – Operational Reporting

This is your most basic reporting found in many organizations. This includes ad-hoc operational reporting and are comprised of lagging indicators. This data, such as turnover rates, is reactive to business demands and while better than nothing, does not tell much of a story.

Level 1 HR analytics is defined by using data to understand and reflect on what happened in the past—and using this data to ask questions to uncover why past events transpired in the ways they did. In Level 1 you are understanding currently available data and with some ideas of what the data is telling.

Level 2 – Proactive – Advanced Reporting 

Here we start to use operational reporting for benchmarking and decision making. The data is multi-dimensional and provides greater analysis and is effectively displayed in dashboards. The difference between Level 2 and Level 1 is the frequency of the data reporting. At Level 2, reporting is proactive, routine or even automated. Here we begin to look at relationships between variables.

For example, if a new curriculum was just launched, you would start to analyze the current new hires' performance on the job 90 after class compared to new hires on the old program. You may also go a level deeper and look at performance differences by leader, geography or even facilitator. This information may be excellent data to tie back to the talent acquisition source and see if there are any stories or lessons to be learned, good or bad.

Level 3 – Strategic Analysis

In Level 3 we start to peel the onion back more and look at segmentation, statistical analysis, development of “people models” and analyze the dimensions to understand cause and delivery of various solutions that will target specific challenges, issues or opportunities for the business.

HR and talent management leaders operating at Level 3 are at the front end of detailed and robust analysis. These analyses may occur in the form of developing causal models or looking at how relationships between variables affect outcomes. An example may be a talent management and HR leader assessing drivers of turnover, asking questions to really segment or strategically identify underlying trends. At this level, involving the business leaders in the discussion can add some effective insights into more specific questions to ask.

Level 4 – Predictive Analysis 

In Level 4 the complexity increases, and predictive models are developed, scenario planning, risk analysis and mitigation are undertaken, and talent management data is seamlessly integrated with strategic planning with the business. This will then drive the correct workforce planning and talent acquisition decisions.

At Level 4, talent management and HR leaders are gathering data and using it to predict what will happen in the future, while also planning for it. An example of Level 4 operations would be using turnover, promotion, and market data to model scenarios that help with workforce planning. Here this data becomes strategic because you can plan for headcount needs, but also for the right type of individual.

You can see by the percentages just how underutilized the four levels of talent analytics are in organizations with the majority using only Level 1 and only 4% using all four levels.

Regardless of the level of data, talent management leaders must look creatively at the data and raise thought-provoking questions about what the data may say. Involving the HRBP and the client leaders will add additional insight into the data. Additionally, better questions can be asked wit more insights that will help you dive deeper, peeling the onion back one layer at a time to uncover the richness the data offers in pursuit of developing the right talent strategies to help the business thrive.