Using Data Clustering to Effectively Manage Your Analytics

A group of HR leaders look at analytics on a tablet.

Much of HR data comes from different sources and can be hard to combine and analyze. Hadoop clustering can make it happen.

Human resources has a big data problem: Information abounds, but many HR departments aren't sure how to effectively analyze and interpret this information to yield actionable results. According to Employee Benefits, while HR should have "some of the best data in the business," just 30 percent of HR teams are leveraging HR analytics to help manage payroll and performance and only 12 percent are effectively collating and making the best use of their big data resources. While cloud is often tabled as an effective way to manage the data deluge, Jim Haas, principal architect for ADP DataCloud, suggests another option: people analytics informed by Hadoop-based cluster technology.

Haas puts it simply: The evolution of cluster technology makes it possible to "measure things that we'd never before thought about." This includes HR. Human-centric metrics are no longer beyond the reach of data analytics. The result is that while cloud solutions can help streamline HR processes, cluster-based data analysis provides the framework to unlock actionable insight, make better decisions and communicate relevant data to other departments. Here's what you need to know.

The Case for Clustering

As noted by Haas, while "cloud is very good for flexibility and underutilized resources," cluster technology is ideal for maximizing speed. In this case, the speed at which it's possible for HR leaders to analyze disparate data sets, return actionable answers and then make line-of-business decisions. But what exactly is a cluster? How does it work, and why does it matter?

HR is overwhelmed with data, from employee performance information to payroll data, recruitment packages to training programs and benefits management. HR data comes from multiple sources, constantly, and is often unstructured — which means it doesn't neatly fit onto a spreadsheet or traditional report. This makes it difficult to compare and contrast data sets since they may not (at first glance) share common variables or format. The result is that HR leaders are expected to make the most of new data sources but often lack the resources to move from information to insight.

This is the role of Hadoop clusters, which excel at analyzing large, disparate sets of data. Clusters are a collection of "nodes" — machines that communicate across a network to separately but collectively analyze data sets. This allows them to handle large volumes of data while simultaneously being extremely scalable. According to Forbes, this open-source project got its start at Yahoo. Now, big organizations like Google and Facebook use a combination of the Hadoop Distributed File System and Map Reduce (HDFS) frameworks to store, retrieve and analyze data, which is also distributed cluster-wide to prevent single-point failure.

Making the Big Decisions

One key benefit of cluster technology for HR analytics is to gain insight about "anything people do for a living — their skills, evaluation of those skills and best practices around organization." For example, HR leaders could use cluster-based analytics to discover where the organization lacks specific skills and adjust hiring practices accordingly, or identify operational strengths that could be utilized to improve performance while choosing to outsource for other, specific talent shortages. Put simply, the goal of big data tools is to give HR access to the data they need when they need it so they can make the big decisions.

But that's just the first step. Once HR data is handled in a manner consistent with that of other departments, it's possible to improve corporate collaboration by sharing this data across the enterprise. Consider that many HCM systems (both legacy and cloud) operate in a way that doesn't align with marketing, sales or management solutions, meaning these departments rely on what HR tells them rather than having access to data directly. But people-based metrics are relevant across the organization — if HR analysis returns data that suggests increasing employee dissatisfaction, sharing these results with the C-suite could lead to actionable changes that improve the status quo. Providing marketing or sales with information about current staff skillsets and potential shortfalls, meanwhile, can help these departments shift their recruiting focus and diversify their talent pool.

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