The Transformation of HR: Part 2 Overcoming data analytics challenges for smarter decision-making

BrandPost By IDG Contributing Editor
Mar 21, 2018

In Part 1 of this two-part series on redefining the HR function and its business value through technology, we honed in on an emerging dichotomy between HR organizations.

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Credit: kentoh

In Part 1 of this two-part series on redefining the HR function and its business value through technology, we honed in on an emerging dichotomy between HR organizations.

On one hand, we have HR organizations that are boldly shaping the future of their business with cloud-based platforms that take advantage of intelligent automation (IA). But we also have others that struggle with uncertainty and an enterprise-level focus.

According to KPMG’s 2017 HR Transformation Survey, three out of four organizations that undertook HR transformation have successfully executed complex initiatives such as implementation of cloud HR technology. Among this group, there were common characteristics such as: changes to operating models alongside their implementation (72%), building a business case with clear measures for success (73%) and being viewed as adding strategic value to the business (89%).

But in Part 2, we examine another statistic: The study also found that more than half of respondents (51%) cited weak data and analytics capabilities in their HR IT systems as the biggest barrier to adopting, deploying and exploiting predictive analytics focused on both HR and business outcomes.

In addition, 90% of “failed” HR initiatives did not identify the right measures for success and manage their program against those metrics. Respondents recognize that succeeding in this area is part of an unprecedented opportunity for HR to move from its traditional instinctive, intuitive function to a data-driven, fact-based decision-making function that offers better business insights and improved decision-making.

According to Todd Randolph, Advisory Principal at KPMG, challenges in capitalizing on the immense value and competitive advantage of data and analytics is a trend he sees over and over again with clients, especially as organizations transition to a cloud-based solution. “They don’t think about the reporting and data they need up front, so they push it until the end,” he explains. “It gets lost in the shuffle and organizations then try to play catch up, rather than developing a strategy upfront.”

Instead, he explains, HR organizations need to build in a reporting and analytics track at the beginning of any transformation program. “Typically we’ll suggest right up front, let’s have a horizontal track, a strategy assessment and roadmap for both reporting and analytics — and evolve that strategy as you move forward with the overall program.” 

Tiers of Data and Analytics Transformation in HR

At the lowest level, organizations need to develop a path to a basic reporting and analytic competency, which will include embedded reporting and analytic capabilities within their core transactional systems to support business processes, Randolph explains: “Becoming an analytically driven organization is an iterative process, starting with embedding analytics into your core transactional system and business processes.”

The next level of analytic maturity incorporates strategic analytics, he adds. “HR data tends to be spread across multiple functions and systems, from HR to payroll to recruiting and talent management, so becoming analytically competent requires common metrics that are rationalized, with a common data repository, whether on premise or in the cloud, with a self-service capability that allows for a single source of truth to report on,” he says.

Finally, to be considered a leader in analytics, organizations must begin to incorporate prescriptive and predictive analytics into their business decisions, which offers many use case examples of how it can potentially take HR transformation to the next level.

“One common example is using analytics to predict high potentials that are likely to turn over,” says Randolph. “After identifying the different characteristics that drive turnover, you can create a model that will help you score each of those individuals in your HR solution that are most likely to leave the organization. Then, from here,  you can start creating action plans around retaining those high potentials.”

 How HR Organizations Can Improve Their Data and Analytics Capabilities

Historically, HR has always been a bit behind from an analytic perspective, says Randolph, but over the past five years there has been a big uptick in interest in this area. “People have started to realize the value of the data they have around the HR function and how the information can be used to improve the employee experience,” he explains.

The organizations at the bottom of that maturity curve are still at the lowest level mentioned above — they are just now figuring out how to get some sort of common repository and governance around HR data.  They are also starting to figure out how to use those capabilities and enable mobile capabilities. The rare upper echelon is also talking about strategies related to predictive analytics. “Very few clients I work with are at that point — it’s more testing, they don’t have it widely implemented,” he explains.

It is essential that decisions around data and analytics should be owned by both the business and IT. “HR knows the type of data they need and the decisions they make, while IT typically knows where that data is and how it can become a common repository for HR leadership and line managers,” he says. “Both need to work together, as more of a business-led initiative rather than technology-led initiative.”

In addition, as explained earlier, it’s essential that reporting and analytics not be an afterthought. “Maybe you’re moving from an older solution to a cloud-based solution — embedding analytics should be a beginning pillar that goes horizontally across all of your tracks, including the technology track, the change management track, and the technology solution track,” says Randolph. “Data and analytics need to be embedded at the beginning of the process, incorporated into your overall program.”