Mining masses of data for performance-improving insights is at once the biggest challenge and greatest opportunity presented by big data. While this is true for every function in the enterprise, the wake-up call for HR should have been answered well before now. In fact, i4cp's most recent research on the analytical practices and capabilities of HR organizations suggests that most are woefully unprepared to do little more with a rapidly rising ocean of data than drown in it. While many HR organizations are proficient at collecting and measuring activities, few have the ambition or ability to measure outcomes or identify the factors that most affect results.
i4cp's new report, HR Analytics: Why We're Not There Yet, pinpoints the reasons for these shortcomings and highlights differences in the strategies and practices of high-performing organizations (HPOs) and low-performing organizations (LPOs) in addressing five key factors driving effective usage of HR analytics - ambition, skills, data accuracy, HR leadership's role and level of sophistication.
HPOs use HR data to plan and perform better; LPOs seem content to merely report it.
HPOs take a more calculated approach, using data for strategic, long-term planning over twice as much as LPOs (96% compared to 47%). Far more HPOs (91% compared to 59%) rigorously assess the ROI of initiatives and programs. Not only is the use of data to make business decisions the marker of an astute organization, it underscores that HPOs are focused on far more than simply reporting. HPOs actively seek information that improves the effectiveness of their planning and the performance of their programs and processes. Low-performing companies do little more than meet minimum requirements necessary for business.
Turning data into information is the most pressing analytics challenge and HPOs are better equipped to meet it.
A common challenge cited by HR practitioners is the difficulty in determining what the data that is gathered actually means. This was the top data collection obstacle cited by all survey respondents. As Sue Suver, Head of Global HR at U.S. Steel pointed out, "Data is great if you have it. But without people who know what to do with it, you're still stuck." Sifting through an expanse of big data to pinpoint trends or uncover stories is a difficult and time-consuming task. It requires analytical and interpretive skills, which more than half of respondents from low-performing companies said they seriously lack compared to little more than a third of those from HPOs. Their experience suggests that companies that can transform data into information, and information into profitable action, will reap a competitive advantage.
HPOs take full advantage of processes, automation and standards to ensure data accuracy while LPOs rely mostly on manual checking.
Twice as many HPOs reported using company-wide standard definitions as a method for guaranteeing data accuracy. Both HPOs and LPOs check data reliability, but HPOs use automated processes (68% compared to 38%) to a greater extent, which not only reduces errors, it frees up employee time for more pressing tasks. The most difficult task of all is setting data standards in the first place. Data councils, which convene stakeholders to set policy around activities such as data collection, standards, and security, are pivotal because they enable enterprise solutions and ensure organization-wide consistency.
HPOs' HR leaders are highly engaged in using analytics to drive performance; LPOs are content to supply data to the executive team.
More than twice as many HPOs have HR leaders receiving workforce data than LPOs (81% compared to 33%), which suggests a more robust, analytics-savvy HR department in more successful companies. i4cp's study indicates that HPOs are moving more aggressively toward the performance advisor role identified in i4cp's 2012 report, The Future of HR: The Transition to Performance Advisor. HPOs are also using people-related data and metrics to proactively inform and engage both the senior leadership and line managers on how to better manage talent and improve business performance. Dominique Ben Dhaou, SVP of HR at SGS, a global leader in providing verification, testing and certification services, underscores the importance of having a basis for action regarding data: "If you benchmark or read a report and do nothing with it, it's useless. But if you transform the data you have access to into solutions for business issues, it has value. When business people say HR doesn't understand the business, it isn't that - it's that we don't do anything with the information we have."
Predictive analytics are underused for human capital measures - even by HPOs.
Both HPOs and LPOs are still finding their way in developing the skills and technical capability to perform and use predictive analytics. Few are now using analytics to answer questions such as how many employees are needed, who is likely to leave, which skills will be in short supply, how changes in workforce cost and productivity affect the bottom line, and which HR practices directly increase company performance. Predictive analytics can reduce uncertainty and provide an evidence-based grounding to the decisions of both HR and the business. Using predictive analytics to understand the true drivers of customer service representative productivity, i4cp member-company Sprint was able to improve its customer satisfaction by record levels.
The bottom line: HPOs are ahead in the race to connect HR initiatives to business outcomes through data.
The gap between HPOs and LPOs in mining insights from big data to show how HR initiatives and practices generate hard financial returns is the single-most important difference between the two groups. The ability to close this gap - to find and use data that can show the impact of HR programs - is one sure way that LPOs can become HPOs. By showing the actual financial impact of a program or a practice, the relative merit of each can be seen, and strategies and budgets can be adjusted accordingly. This evaluation of ROI is the key advantage of meaningful HR metrics.