You’re probably a little tired of hearing about Big Data. While you would expect online giants like Amazon and companies like Netflix to be early innovators in the use of data to recommend products or movies, you only care about the answer to one question: what does big data mean for the everyday employee and how can regular businesses extract real value from it?
The good news is that Big Data is making a difference in places and ways you might not expect, particularly in human resources. Companies are analyzing their employee data with workforce analytics to answer a variety of critical questions: Why does one sales person outperform his peers? What is the impact of learning programs on company results? How long does it take for new employees to be productive? Why do certain leaders succeed and others fail?
Black Hills Corp. is one of those companies. A 130-year-old energy conglomerate, Black Hills doubled its workforce to about 2,000 employees after an acquisition. Like many energy companies, a combination of challenges — an aging workforce, the need for specialized skills, and a lengthy timeline for getting employees to full competence — created a significant talent risk. In fact, forecasts showed that, within five years, the firm could lose 8,063 years of experience from its workforce.
To prevent a massive turnover catastrophe, the company used workforce analytics to calculate how many employees would retire per year, the types of workers needed to replace them, and where those new hires were most likely to come from. The result was a workforce planning summit that categorized and prioritized 89 action plans designed to address the potential talent shortage.
For other firms, more effectively using talent data is a key component of an HR transformation, one that seeks to improve the function’s role as a true business partner. In their 2012 book, Transformative HR authors John Boudreau and Ravin Jesuthasan detailed a case study of Ameriprise Financial, a diversified financial services company spun off from American Express in 2005.
When the newly-formed firm began delivering a set of HR activities such as onboarding, training and performance reviews, employee feedback rated the quality of these services as “poor.” Furthermore, Ameriprise Financial had no framework for allocating HR time to talent issues with highest impact.
To improve their HR functions, Ameriprise began to integrate workforce and financial data to align talent investments with business results, and more proactively develop data-driven insights used to predict turnover, reduce new hire failure rates, and manage persistent poor performers. Employee feedback was overwhelmingly positive, with HR transforming from “order taker” to “key business contributor.”
All of this goes back to a prescient observation Murray Gell-Mann, a Nobel physicist, made in an interview with Harvard professor Howard Gardner in the 1970s. He predicted that the trait most valued in 21st century companies would be the capability for synthesizing information. The skill of synthesis is particularly crucial for corporate leaders, given that the decisions they make are fraught with big-picture complexity and the consequences of those decisions are often momentous.
But because leaders sit atop more information sources than most people, there is an inherent risk of information overload. This is compounded by the traditional way in which data is often presented: voluminous spreadsheets containing hundreds of data-points that do little to connect talent metrics to the recipient’s business priorities or decisions.
As such, in the rush to deliver Big Data, organizations should also consider how they can provide better data to their managers to enable higher levels of utilization and faster synthesis of key insights. Consequently, your analytics must be:
Relevant. HR analysts need to apply data to the business issue (a top-down approach), rather than using an unnecessary amount of resources for bottom-up data mining.
Valid. The quality of data is important, along with the way leaders are educated about the credibility of talent metrics.
Compelling. Of the hundreds of HR leaders I speak with each year, one of the most common goals of analytics is to tell a better story with data. HR can’t just present raw numbers and expect the recipient to identify the correct message. Analysts need to understand their audience, create a plot of related storylines, and deliver conclusions that tie together the principal facts.
Transformative. Ultimately, actionable analytics have to change a leader’s behavior. A leader should be able to change his or her thinking and make better, faster decisions as a result of talent data.
There are still obstacles to adopting workforce analytics: Less than half of the global companies use objective data when making workforce decisions, and fewer than 20 percent were satisfied with the ability of their current data management systems to manage talent data, according to an SHL global assessment report published in February. But armed with actionable analytics, leaders and managers have immense opportunities to use talent data in reducing workforce costs, identifying revenue streams, mitigating risks and executing business strategy. As David Crumley, Vice-President of HRIS at Coca-Cola Enterprises, says, “this is where it gets really exciting.”