Companies have collected employee data for years--from satisfaction surveys to ethnography Historically, HR used data to report headcount or turnover information
Employees generate petabytes of data about themselves every day, says Waber. But that data sits in disparate systems in different formats and is often messy
implementing a single version of an HR information system itself may not sound revolutionary, but it's a critical first step for companies interested in more advanced analytics.
There's a broad array of uses for talent analytics: screening new hires, figuring out who should get promoted, efficiently staffing new projects, uncovering the characteristics of high-performing individuals or teams, and even predicting who's likely to head out the door.
Most companies would love to know if a prospective CEO will be successful or if a key marketing executive is going to quit.
Now lots of them are crunching numbers to get answers. They're scouring data about existing employees to look for patterns of behavior and performance, so they can figure out the best ways to motivate them and see what qualities matter most in certain jobs. They're also tapping information from applications, interviews and résumés to see if prospective hires will be a good fit.
The practice has increased over the past five years, thanks to improvements in technology,.
The systems have gotten simpler to use and can process data from a wider variety of sources. They can also operate over the Web instead of needing to be kept on corporate servers.
Among companies with 25,000 or more employees, about 5% are using predictive analytics in human resources, according to a soon to be published study
Gartner expects the market for BigData and analytics to generate $3.7 Trillion in products and services and generate 4.4 million new jobs by 2015. While most of the talk is about applying BigData to marketing and consumer businesses, there is an even bigger opportunity to apply BigData to Human Resources.
There are around 160 million workers in the US alone, and most companys' largest expense is payroll. In fact in most businesses payroll is 40% or more of total revenue, meaning that total US payroll expense is many billions of dollars. How well do organizations truly understand what drives performance among their workforce? The answer: not really very well. Do we know why one sales person outperforms his peers? Do we understand why certain leaders thrive and others flame out? Can we accurately predict whether a candidate will really perform well in our organization? The answer to most of these questions is no. The vast majority of hiring, management, promotion, and rewards decisions are made on gut feel, personal experience, and corporate belief systems.
When most people in business think of "big data," they think of corporate leaders devising grand strategies in boardrooms behind closed doors. The assumption is that CEOs, CFOs, CIOs and CMOs will dominate the discussion about how to collect more information and channel it into real results. No one else's opinions matter. That shouldn't necessarily be the case, but it may require a long-term process to encourage lower-level employees to speak out and ask to be contributors to the big data movement. Mid-level managers surely have valuable opinions to share, and if they are armed with more data analysis expertise, they can impart their wisdom to the employees working under them, which will make the entire workforce more analytics-savvy. So far, though, it's been slow going. According to Human Resource Outsourcing Today, there's a visible gap between the C-suite and mid-level in terms of analytics expertise. The news source highlighted a recent study conducted by the Institute for Corporate Productivity which found that 48.5 percent of supervisors and 31.5 percent of managers have "novice" or "non-existent" levels of analytical knowledge. Higher up the corporate ladder, this alarming figure is much lower - 26 percent of leaders and 27.1 percent of "functional experts."
Ideally, this gulf would be narrower. But what are today's business leaders to do? One answer is that mid-level managers must take the initiative to speak up more and share valuable data-driven insights with the C-suites within their companies. One thing they can do is point to concrete, actionable findings within their data sets that will get their bosses' attention.
Look for analytical talent in the right places
There's a shortage of analytical acumen in HR, and only a small likelihood that new resources will come from existing HR talent pools. Few of the HR analysts and analytic leaders in attendance had traditional HR backgrounds, and many said their organizations have open positions. Finding the right people to fill those positions and finding talent within the organization was a common concern among attendees. A few recommendations:
Build stakeholders support and engagement
Workforce analytics must be clearly focused on driving business value. If HR uses analytics to build program budgets rather than business results, there's a risk of losing credibility with leaders and line managers. The best analytics solutions are those that are embedded in the decision-making of line managers and other key stakeholders. Without the support of business units and leaders who can use the analysis to make decisions, your findings will fall on deaf ears.
Start small and build core reporting metrics
The various organizations in attendance were generally large, sophisticated companies and federal agencies, yet most acknowledged that they are only just now scratching the surface of their human capital analytics potential. They were simultaneously reassured that others were not as far along as appearances would suggest, or at least were figuring it out as they go along. And there was acknowledgement that sometimes using Excel is the best option to get started.
Make sure your answers and data are accurate and shared
HR analytics is in its infancy, and while more organizations are investing in analytics teams, they still have to prove their value. That's why it's critical that the data and answers provided are as accurate as possible. This is common sense, but if business leaders lose faith in your analysis, proving your team's value at a later date will be an uphill climb. It should be no surprise that, i4cp research has found that nearly twice as many lower-performing organizations do not put into place any known controls for data accuracy in contrast to the practices of higher performers.
According to a recent study released by IBM and MIT, "Analytics in the Boardroom," organizations that are taking a wait-and-see approach to analytics are falling behind their more determined peers. Leading organizations are now executing carefully targeted analytics efforts designed for maximum strategic advantage.
The study focuses on corporate boards' increasing need for, and reliance on, data-supported answers to tough business questions. It reports that nearly 6 out of 10 organizations now differentiate themselves through such efforts.
The study's analyses and recommendations, while not developed specifically for corporate CHROs, speak directly to the problems of credibility, relevance and impact faced by so many of those leaders as they seek to position their departments as indispensable corporate partners. The Analytics Edge
Every organization is analytically minded to some extent, but the range of sophistication is very broad. The study documents this in a number of ways:
57% — The trend is clearly toward analytics. From 2010 to 1011, the number of organizations using analytics to create competitive advantage rose 57%.
2X — Organizations using sophisticated analytics are more than twice as likely to substantially outperform their peers than those who aren't.
5X — The critical first steps in data analytics best practice are agreeing on data definitions and standards, persuading data owners to share the data they control, or to trust the data they don't. Sophisticated organizations are 5 times more likely to have taken these steps.
4X — Sophisticated organizations share data vertically down to front line employees 4 times more frequently than unsophisticated ones.
2X — The ultimate goal of fact-based organizations is their openness to new ideas and to the new ways of doing things they suggest. Sophisticated organizations are almost twice as likely to have reached that goal.
Having made your first important decision about what to measure, HR 's next key decision is how to deliver the reporting and analytics related to the goals you have chosen. This decision is complex, as it involves the technical aspects of IT and data management, as well as, the human elements of decision-making and HR 's role to support business leaders throughout the organization.
From the technical perspective HR leaders need to:
A. Determine which data they need and what source they will use.
B. Decide how to extract, organize and store all of their data so it is available for reporting.
C. Choose an analytics solution to generate reports, dashboards and run ad-hoc queries.
D. Create a process that allows the right business leaders to view the information they need to make better people decisions.
All of the four components listed depend upon each other and it is the interaction between these components that creates the overall value of the final solution.
Starting in 2014, healthcare reform requires employers with over 200 full-time employees to enroll all of their staffers (new and existing) in a healthcare plan.
Additionally, employers with over 50 full-time equivalent employees will be subjected to an assessable payment if they don't provide minimum essential coverage — or if that coverage is not affordable.
Beyond the accounting and compliance headaches this poses for firms, the new law also changes the ROI calculations as to whether or not new people should be hired, and if so, then in what capacity. Is it better to do without a new person, to hire part-time people, to hire a full-time person, or to contract out the work elsewhere? These have always been the choices facing employers, but now the math has changed. The government has its new definitions of full-time (although even that leaves room for interpretation), and the requirements and penalties affect the equations.
Whether or not it is in the domain of the HR function to determine which of the above choices should be pursued is irrelevant to this discussion. What is relevant, though, is regardless of who makes the decisions, HR is in a unique position to offer insight into the decision-making process. So it behooves you as an HR pro to be proactive. Hiring decisions occurring in less than a year (and in some cases, occurring right now) are dependent on and also subsequently affect this analysis.
Both the finance and HR departments probably have overlapping data that can be incorporated into actionable analytics, but only HR can offer insights into the numerator of the ROI equation – the value component. Finance cannot offer analyses of the relative performances of comparable full-time, part-time, and outsourced people. Only HR has the data to mine that information.
Moreover, especially given the government's fluid definition of "full-time," only HR can properly classify individuals at any data collection point. And even the compensation costs that finance uses will ultimately come from HR.
Business analytics is about making decisions based on facts—data—to realize business strategy and drive performance. Workforce analytics zeroes in on how to use data to effectively manage and derive value from an organization's workforce. Given that, for many organizations, roughly 70 percent of expenditures are people-related, the financial and operating risks of making arbitrary or seat-of-the-pants workforce decisions is high (ask me about businesses that have been damaged or even destroyed by such decisions). What we do in our Workforce Analytics practice is to help organizations build the capability and tools that can enable leaders to make sound people-related decisions.
This aim isn't so different from other types of analytics endeavors (such as supply chain analytics, finance analytics, customer analytics, etc.). What is often different, though, is the starting point for a workforce analytics effort and the groundwork needed before the data can be put to work in decision making.