We often hear of the benefit Big Data can provide to businesses intent on managing their inventory tracking, forecasting, customer segmentation, pricing and promotion, etc. However, there is a tremendous opportunity to use the power of deeper analytics that is often not leveraged fully – in HR (and talent development specifically). A recent article in UNC Executive Development highlighted some of the ways that analytics can contribute to better human capital decision-making.
What is Human Capital Analytics?
Human Capital Analytics is the application and integration of sophisticated techniques and mining of collected data to derive insights about human resource processes, decision-making, policies, behaviors, etc. Due to the decreasing costs of data acquisition, retention, integration capabilities between so-called “structured data” (think Excel spreadsheets) and “unstructured data” (posts on websites, comments, or other text-based data), etc. AND the ability now to use predictive analytics or other business insight tools to extract meaning, correlations, causality, and other helpful information from the huge volume of data in ever-shorter amounts of time, decision-making can be greatly enhanced.
Where Can it be Used?
The utility of the information is only limited by the creativity and imagination of the users. However, commonly used applications of Big Data by progressive organizations include:
- Improving the process for recruitment – the interview has been notoriously criticized as an effective way to judge talent. Through the use of data analytics, the potential for more accurate assessment of talent is greatly enhanced.
- Compensation – in many instances, compensation practices evolve with very little forethought or strategy, until it is too late and the pay disparities between like or similar roles within the company (intra-department as well as across different functions and/or geographies) exposes a weakness. Data analytics can allow for careful monitoring of both internal compensation comparisons and allow for external compare/contrast analysis with other organizations.
- Learning/Training – one of the largest “black holes” for many HR departments is justifying the value received with the costs to develop the skills of employees. Data can now be collected and analyzed that allows for a more refined assessment of performance pre and post a training intervention (or be used to compare performance under different training condition – online learners versus those that attended a classroom training as an example).
- Succession Planning – many companies struggle with properly preparing current and/or future senior leaders for their next role, determining which employees would be the best candidate for an internal promotion, and having a way of comparing employees with each other when their functions, locations, and experiences may seem vastly different from each other . Big Data, when properly implemented and used can allow for these kinds of analytics to bolster the confidence the organization and the candidate have in their career development.
Where to Start?
Of paramount importance is using the numbers to support the organization’s objectives and not collecting data for the sake of stockpiling or warehousing data. One can quickly become consumed by data that may be irrelevant, superfluous, and non-value adding. Rather, the organization would be well served to identify at least the first preliminary steps:
- What is the corporate strategy and how can HR decision-making aid in achieving that?
- How will performance to support the strategies be measured?
- What is the “standard” or acceptable level of performance (and where is the organization’s standing based on that expectation).
Once those have been determined, there can be a crystallization around what data is required to support the decision-making necessary to achieve the above. Careful attention is needed to avoid confusing correlation with causation (correlation refers to two occurrences happening together. Causation refers to one occurrence leading to the other – said another way, if one variable is removed, the other variable will also be impacted. Two variables that are correlated to each other may BOTH be caused by a third variable and not each other). The data analysis should lead to improved decision-making by isolating those performance factors that are causal so that new behaviors, processes, policies, etc. can be created to improve the organization’s outcomes (reducing turnover, improving performance levels, increasing managerial competencies, etc.).
Generally speaking, improvements may be slow to recognize initially as the organization learns how to maximize the value of Big Data in this way. However, once it is applied consistently and with greater focus, the HR department’s role will grow in importance exponentially.