There has been a lot of buzz lately about business analytics, predictive analytics, and big data. Organizations are using such methods to predict behaviors of customers, understand marketing and sales strategies, and inform strategic decisions.
However, a study byTDWI (found here) indicates that of all business functions, HR is the least likely to use these methods. Only 17% of HR groups say they are currently using predictive analytics and only 22% say they plan on using them in the next 3 years (compare to marketing in which 64% are currently using predictive analytics).
So what do these terms mean and how can they contribute to a more strategic HR function? Let’s start with some definitions.
Business analytics: The practice of enhancing business decisions by linking business problems to data analysis. Business analytics involves exploratory and complex data analysis, reporting, and data-driven decision making.
Predictive analytics: Predictive analytics is using statistics to detect trends, anticipate behaviors, and identify anomalies and risks to drive better business performance.
Big data: Big data is defined as a collection of data sets so large and complex that it is difficult to process using traditional data processing and analytics applications. Big data is usually characterized as involving enormous amounts of data that are being collected at a high velocity. In addition, data are from a variety of structured and unstructured sources (think numerical data, visits to websites, social media activity, emails, video). The challenge is how to collect, structure, store, share, and analyze such data to uncover insight.
While all of these terms describe something different, they are related in that they are focused on finding insight by connecting different sources of data that can be used for strategic decision making. For example, in terms of HR and human capital, these methods could be used to answer the following types of questions:
Does the organization have the people it needs to move the business into the future?
Are selection programs predictive of employee performance and how could they be improved?
What culture elements are most important to driving retention, quality and customer satisfaction?
In the end, the goal is to harness the power of data to identify critical trends that lead to more strategic interventions.
Where to Start?
Getting started in using business analytics to get the most out of your human capital can be overwhelming. Here are a few simple steps to get started:
Start small. Instead of trying to analyze data from many different sources, start by picking 1-2 critical business problems. Then, identify a few variables that relate to that question and start your analysis there. For example, employee engagement data could be analyzed to understand customer loyalty. Participation in training could be examined to understand turnover.
Get leadership buy-in. Starting with an analysis that speaks to a pressing business issue will get the attention of senior leaders. Seeing the power of analytics will entice them and give you momentum and support for more robust analyses. Given that linking data is often a cross-functional effort, buy-in from senior leaders is critical.
Prepare to Collaborate. Business analytics requires input from multiple disciplines within the organization (think talent metrics, customer metrics, financial data, etc.). Often the hardest part of such initiatives can be getting the data all in one place in an easy-to-understand useful format. To gain buy-in from busy colleagues, ask them what questions they want answered and how the analysis could be informative to them. If you can identify a “what’s in it for me,” employees from other departments will be more likely to help out.
Enlist a Data Scientist. A data scientist is someone who understands both business and statistics (e.g., regression, structural equation modeling, cluster analysis). Such resources an can be internal or external. A good data scientist is not just a number cruncher, but also can tell a story with the data and link it to business strategy. A good analysis will include actionable insight.
Recognize the Importance of Change Management. In most organizations, leaders manage by their gut instincts and past experiences. Being disciplined about analyzing and using data to make decisions requires a mind shift. Start slow, demonstrate small wins, and show leaders how data can make their jobs easier.