What HCM Analytics Drive Business Performance?

In a 2015 Harris Poll, 74% of respondents said that their company needed to be more data-driven (i.e. fact-based) in workforce decision-making.

Not a surprising statistic, considering only 27% of C-suite executives found their HR team able to provide them with actionable analytics to improve their decision-making.

There are useless Human Capital Management (HCM) analytics—then there are HCM analytics that drive business decisions. As far as business leaders go, leave the “useless HCM Analytics” to the academics. What good is it to tell me that my turnover rate has dropped 3% on key employees when reducing key-position turnover has a negligible impact on bottom-line performance?

So how do you start quantifying HCM analytics that can have a measurable impact on business performance?

1. Start with a business problem that you want to solve

Often, the problem with the way businesses use HCM analytics is that they start with the available data and then ask questions from the data, rather than starting with a problem and asking, “What data do I need to take decisive action against this problem.” This will always lead to assumptions regarding the data required to develop a solution to the problem.

If you start by asking questions of the data before quantifying the problem that you want to solve, there’s a good chance that you’ll end up commissioning a study that has no real impact on the organization.

Beginning your HCM analytics program must begin with a problem to solve, and an assumption of the required data needed to solve that problem.

For example, a retail employer may have a problem of overstaffing her stores, and want to ensure she is properly staffing her stores during peak sales hours. The classic analytic that this retailer would want to track would be her Sales Per Labor Hour (SPLH).

SPLH = Total Revenue(for X time period) / Total labor hours(for X time period)

By creating a benchmark SPLH value, she can track the performance of individual stores against that benchmark, identify top performing stores, and work to isolate and replicate performance drivers across all of her stores.

2. Identify the data that you need (no more—no less)

There is a sweet spot for data. Too much can mire decision-makers in petty details while too little can lead to risky assumption-heavy decision-making. In the case or our retailer, the analytic of SPLH is driven by some specific raw data:

• Accurate time and labor information per store
• Accurate sales information per store

She will also need to identify what constitutes a sufficient data sample. For example, if she can only get bulk revenue figures per day, rather than per hour, this may not be a sufficient data sample to drive decisions on building hourly schedules to match peak sales periods.

3. Confirm that the process to compile the required data is manageable

One of the major obstacles reported by HR professionals in implementing an effective HCM analytics program is inconsistent or incomplete data. Along with manual data-collection processes, these two impediments are the leading reasons why 3 out of 4 HCM Analytics programs fail.

When confirming the process for building the required analytic, you must:

• Identify where the raw data driving the requested analytic is housed
• Identify the process for extracting raw data from its database
• Identify the process for merging data from multiple databases to produce the desired analytic

When all of the raw data is on a single database, this can be a fairly automated process. For example, many retail point-of-service systems can track both revenue per hour and labor-hours data, displaying the SPLH analytic in a dynamic dashboard.

When working with multiple databases, this may require custom software integrations that combine data through a batch-sync on a daily/weekly/monthly basis.

In other cases, companies may require these HCM analytical reports to be compiled manually. When these manual compilations get overly complex, it can create data-integrity issues, information lags, and ultimately erode the ability of the data to drive better decisions.

Avoiding the Fad-Response to Business Intelligence

There is a lot hype around Business Intelligence (BI), and its promise to improve business performance. That being said, data that isn’t being used to drive business decisions is nothing but a vanity metric. While vanity metrics may interest the academics, they are a distraction to the C-suite. By starting with a problem to solve, identifying the data required, and building a manageable process of producing analytical reports, even small businesses can have strong analytics programs that drive smart HCM investments.

To do so, it’s important to have the right Human Capital Management Technology in place.

To learn more about Benetech’s ability to support your HC analytics program, learn more about our recent partnership with analytic.li.