Kelly Cartwright is Vice President, Corporate Development at SourceRight Solutions and a member of the HRExaminer Editorial Advisory Board. Recognized as one of the Top 100 HR Influencers by HR Examiner, Cartwright has more than 15 years of experience in the Human Capital Management industry with deep expertise in the rapid development, delivery and implementation of professional services and technology solutions. Prior to joining SourceRight Solutions, she was the general manager of The Newman Group, a Futurestep Company. Full Bio »
Workforce analytics: What is there to get?
by Kelly Cartwright
For decades, the subject of metrics and reporting has caused heartburn for HR and business leaders alike. Now there’s “workforce analytics.” Like workforce planning and integrated talent management, the promise of analytics is easy to evangelize and difficult to achieve. You might look around and conclude that other companies are somehow doing better. It seems like everyone else is winning the race. Of course, this is simply not true.
A 2009 IBM whitepaper, Getting Smart About Your Workforce: Why Analytics Matter, still resonates today; and it notes that approximately 40% of survey respondents claimed to be able to obtain basic workforce data. That means 50 to 60% of respondents did NOT capture basic workforce data. Capability has grown since then, but most would agree that workforce analytics is still an unrealized promise for a significant proportion of companies today.
The report cites some unsurprising barriers to effective analytics, including “data consistency, systems integration, information accessibility and analytic capabilities of end users.” Clearly, implementing a workforce analytics function is a feat of human nature, as well as of process and technology. So, with that in mind, I will ask, “What do companies need to get in order to achieve an effective workforce analytics capability? I see three main areas.
Getting on the same page: measures, metrics, analytics
Measures, metrics and analytics are not the same. A GPS navigator provides all three. Miles and hours are types of measures. The navigator applies these measures to achieve meaningful metrics for tracking progress: miles per hour. It applies those metrics to calculate travel time, and it tells you when you can expect to arrive. This last piece is an analytic. It is specific. It is predictive, and it illustrates one of the main hurdles companies face: they don’t always agree on their destination, let alone the measures and metrics involved in getting there.
Getting people on the same page about analytics is a challenge in itself. It’s about gathering the stakeholders, from business and from HR, in the same room. It means speaking the language of business to agree on the goals they’re trying to track. In an excellent “Focus” roundtable, Workforce Analytics: a Distinct Competitive Advantage, Jay Kuhns, V.P. of Human Resources for All Children’s Hospital speaks of getting business leadership to embrace workforce data “in the same way they might embrace end-of-the-month financial data.” Companies aren’t there yet, but there is some agreement on the basics. Business leaders and HR are beginning to talk the same language.
Getting the complete picture: Contingent, Full-time and Contract Workers
An elephant in the room when it comes to workforce analytics is that it is not easy to even determine who your entire workforce is. This is due in large part to the growth of temporary, contract and contingent labor.
A New York Times article recently observed that 26% of new workers added to the US workforce between 2009 and 2010 were temporary. The August Bureau of Labor Statistics release cites approximately 28 million part-time workers out of the total July 2011 US workforce of 140 million. If you ask around you are likely to hear business leaders speak of anywhere from 5% contingent and contract workers all the way up to 40% in their organizations…or, they may say they don’t know what percent of their workforce is contingent. That’s the elephant.
It’s difficult to effectively manage a workforce when you can’t account for a quarter of your labor. How do you know where your contingent workforce is? How do you assess them, pay them, measure their performance, ensure compliance, and all those things that you do for FTEs? How do you do all these things globally and consistently? Once again, understanding and tracking the contingent workforce takes commitment and agreement from across the company, as well as among associated vendors, on a common process, technology and data.
The good news is that the industry is recognizing this demand for meaningful intelligence around the contingent workforce. Mature solutions, in both technologies and managed services, are giving companies the ability to measure, manage and improve the way they attract, engage and deploy their contingent workforce. How companies use those resources varies, but the trend is growing.
Getting predictive: analysts call it mature, CEOs call it useful
Finally there is the challenge of making workforce analytics work. Last year’s data about employee engagement, turnover, availability of skills in key positions, and productivity may be nice to have, but the goal is to ensure that you have the right person, at the right time and the right cost moving forward. That requires analytics that are predictive.
Predictive analytics require a stable base of relevant data, a clear link between data and business goals, solutions with the horsepower to crunch that data, and the willpower to commit the resources to make it work. Deloitte proposes a practical maturity model in its recent report on the subject. In this scale, the least mature (non-existent analytics) is probably just as uncommon as the most mature (sophisticated predictive modeling capability), with most organizations falling somewhere in between, using available data to support strategic decisions.
Of course, people will naturally want analytics to tell a story…to tell their story. When is workforce analytics information broad enough and detailed enough to give us the ability to make decisions based on what it says, as opposed to using it to support a business case that we make outside of it? The answer to that question may be never. People will always make the ultimate strategic decisions, but analytics are now growing sophisticated enough to reveal the results likely to be achieved by certain decisions, and the detail needed to take the right course.
Moving Forward: To Wade In or to Dive?
Companies are constantly struggling with the economic complexity of globalization, uncertain growth, changing demographics and shrinking pools of critical talent. The visibility that workforce analytics can offer is a competitive advantage in such foggy times. Many organizations are “wading into the pool” when it comes to workforce analytics. They are making incremental advances to solve specific challenges, but they may not have the resources or universal mandate to make it priority. Others may be diving in, creating an integrated long-term strategy for evolving an advanced, predictive capability.
Where is your organization on the analytics curve? Is there data? Is there technology? Is there agreement? No one has everything, but we’re all being pushed in the right direction.