By Jason Taylor, Ph.D., email@example.com
A friend who’s a retail chain operations VP recently told me about his company’s expansion plans, but he wasn’t sure they were expanding in the right ways. That’s when we talked about how data can answer strategic questions with certainty. He already had some of the data, and he could easily get other data that could show which specific departments and employees consistently perform best during which hours and days to determine where to expand.
In the past, retail executives’ success required great intuition and risk-taking abilities bound with hope and luck. Today ‘big data’ is replacing intuition and hope. With technological advances, organizations are now able to collect large amounts of data and turn it into the fuel that drives a predictive decision-making machine. Similar to how retailers analyze data to learn shoppers’ habits, employee selection and succession can also leverage big data to predict success.
The nuts and bolts of gathering and storing data may be IT topics. However; investors want higher profits and customers want better products faster and delivered with superb customer service, which aren’t IT issues. Executives must also predict market needs and then determine who, how and from where these needs can be fulfilled. People make this happen.
In today’s marketplace, successful retailers are searching for new ways to collect and analyze the most strategic business data that provides a competitive advantage. Those that have data and know how to use it are methodically outperforming the competition. In fact, the McKinsey Global Institute determined that data has swept into every aspect of business. In terms of importance, data now sits alongside both labor and capital as critical factors for business success.
The human capital field has embraced the use of big data to identify, select and develop a cutting-edge workforce that will produce and contribute more to the bottom-line at the job level. A call center operations manager, for example, can see productivity by employee down to the day and time to see where improvements are needed.
People are usually the greatest expense on balance sheet. All executives need to maximize the effectiveness of their people is a systemic way to measure performance and tie it back to selection. Retailers now have the ability to use big data to ensure every new hire is selected based on their probability for high performance. Good data reduces the intuition and guesswork. If done correctly, companies can calibrate employee selection criteria and how they measure employee performance and use the new knowledge to improve performance throughout the organization.
Where to begin for good, fast results
To effectively leverage big data, retailers must first have valuable high-quality employee data that provides objective performance level information of each individual in a given role. What isn’t measured won’t improve, so measuring data will enable valuable predictions.
When numbers do not meet expectations, organizations make cuts, sell divisions, or execute other large-scale group changes. Since larger companies typically don’t collect data at the department or individual levels, executives often make strategic decisions based on regional or overall company performance data. They use group solutions to resolve individual performance problems, creating a cycle of reactive decisions – like closing branches – based on very little accurate information.
Big data breaks the guessing game cycle. Regardless of the job, starting with performance and behavioral data at the individual level rather than at the location, role or other level to create prediction models will increase the probability of hiring employees that will improve profits and company performance. Eventually stock price will also increase.
Frequency and timing is critical to effective data analysis. Collect periodic data that provides insight into performance over time. The more granular the information, the more interpretation and insight can be gained from the data. For example, a retailer that collects individual total sales for each calendar year limits the insight and contribution to a prediction model. The company could gain more information by collecting individual performance data at quarterly, monthly or weekly increments. This retailer would now be able to evaluate individual-level performance to understand early stage ramp-up times, sales cycles and each employee’s consistency over time.
How to collect valuable performance data
Data collection is just as critical as the data itself, so accuracy, method and variability are vital components. To become data-centered organizations, retailers should use systems to ensure data is accurate and represents true performance over time. Accurate performance data will provide the data needed to create prediction models that will improve performance employee by employee.