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Whether known as big data, data analytics, data mining, machine learning, cognitive computing, or artificial intelligence (AI), data science is a hot topic.
IBM, Microsoft, Google, and others offer a variety of powerful development tools, platforms, and services for IT organizations to incorporate these capabilities into the applications portfolio. Think of the recommendation engines for Amazon and Netflix, or the analytics capabilities of Caterpillar or John Deere to predict equipment failure and schedule preventive maintenance.
But for the vast majority of organizations that do not want or need to develop custom applications, there is another emerging trend. Commercial software providers are embedding sophisticated analytical capabilities into packaged software and services. These are not general tool sets that IT organizations use to extend legacy systems. These are applications of data science embedded into off-the-shelf in business systems.
Six HCM System Providers with Embedded Data Science
Human Capital Management (HCM) is turning out to be fertile ground for providers to develop use cases for data science. The recent HR Technology conference (HRTech) in Chicago provided an excellent opportunity for us to learn about the offerings of six such providers.
There are other enterprise domains where data science is making headway, such as marketing, salesforce automation, customer service, and supply chain management. But as seen with these six providers, the HCM space may be one of the early test beds for embedding data science into business applications.
Think Beyond the Initial Use Cases in HCM
Although HCM is a fertile field for data science, many of the current uses cases are focused on a few areas, such as reducing employee attrition. But even here, we need to go deeper. Brian Sommer runs down a list of deeper questions he’d like providers to address. Instead of just identifying when an employee is at risk of leaving, how about “why” and “how” the employee might leave? He writes:
Proactive approaches have rarely been created by HR or HR vendors but are badly needed. HR vendors need to do more primary research to understand WHY people leave companies not WHEN. Then, these solution designers can create employee timelines that suggest the best points when HR should be having a detailed career discussion with the employee. Again, primary research is needed to help HR figure out what and how to drive that conversation so that the employee stays motivated and remains with the company.
In our view, there are many more interesting problems in HCM beyond the attrition/retention problem that data science can address. Here are just a few:
Admittedly, many of these questions raise the specter of “Big Brother,” and employees may push back if they begin to feel as if they are being profiled. But with the stakes so high, can any business leader afford to not consider tools that, properly applied, could mitigate significant risks to the business?
In private conversations, some HCM providers (not necessarily the six highlighted earlier) indicate that they are thinking about how they can address these types of issues. They may not be talking about them publicly, but new applications of data science for HCM may be coming soon.
Questions about this research? Contact the Analyst.