Three "Laws" of Workforce Analytics

(These are personal views and do not reflect the views, programs or policies of any organization)

I’ve distilled what I’ve learnt as the head of Workforce Intelligence at McKesson for the last three years into three principles. I’ve presented these principles at a number of workforce planning and analytics conferences across the U.S. in the last year and the audience’s positive reception emboldens me to call them “laws.” 

However, since human capital analytics is a young field, I’ll leave the quotation marks in for now and await the evidence of your experiences to see if we can one day dispense with them.

First “Law” – The demand for workforce analytics grows exponentially

Whenever my team delivers interesting business insights through workforce analytics, we typically leave the meeting with requests for additional analyses. This is the case whether people are new to workforce analytics or have had previous exposure to analytics. In the first instance, the response is “Wow, who knew? Show us more!” and in the second, it is a more targeted request, reflecting a sophistication about what specifically to look for next.

In either situation, it is not a request for marginally more information or analyses; it is a request for a whole lot more – an exponential increase, relative to what was shared initially. I can’t help but make the connection to Moore’s Law, posited by Gordon Moore, founder of Intel – 50 years ago to the day – which, simply stated in its current form, says that computing power doubles every 18 months*. The law has withstood the test of time, hence the absence of quotation marks.

One of the main implications of the first “law” is that you have to factor in the exponentially increasing demand to build a right-sized supporting infrastructure. You must ensure that you grow the analytics team at the right pace – don’t forget to make the technology and headcount budget requests early and often!

Second “Law” – The consumption of workforce analytics requires effort

If you believe all the hype around workforce analytics, you can be forgiven for assuming that once workforce analytics are delivered – via a fabulous report, marvelous dashboard, dot-connecting “story” or some such device – all your problems will be solved. The wonderful “insight” that everyone talks about (the “aha” moment!) will be magically revealed and you will immediately “add value” to “the business.”

The incredible effort needed to develop data assets, tools and analytics notwithstanding, the delivery of the analytics is when the hard work truly begins. One of the secrets to success in analytics is that you need to have a prior expectation of what the number(s) ought to be – that’s the first piece of effort before any analytics are performed – so that you can be an intelligent consumer. Then you have to cast the net wider for additional information, drill deeper to understand drivers, make connections to other insights, adjust hypotheses based on new information, etc.

The implication of the second “law” is that consumers need to have the time to make use of the analytics. This is related to the “energy balance" implication of the First Law of Thermodynamics. If HR leaders and business partners need to leverage analytics as strategic advisers to the business, they will have to spend less time doing something else. It won’t all just come together without having to lift a finger; there’s no such thing as a free lunch (which someone needs to bank as an Iron Law of Economics).

Third “Law” – Workforce analytics trumps workforce planning – in most circumstances

This is the only one of my “laws” that has a qualifier. I need to insert the qualifier because I’ve seen the “promised land” where workforce planning** works – for the finance and investment banking divisions of a major Wall Street firm in the late 90s and, more recently, for a specialty health care business of a major drug distributor – and truly believe that with the right conditions it can be a game changer for an organization.

What are the right conditions? Most importantly, visionary and strong HR and Finance leaders willing to let down their guard and collaborate so that each can do their job better and jointly improve business outcomes as a result – forecast talent need, manage workforce cost and minimize business strategy execution risk. Other conditions include an existing strategic planning foundation, functional cost- and managerial hierarchy management, an affinity for data and measurement, a talent management system that tracks roles and skills, a disciplined process orientation within HR, and the patience to invest for the long term.

The sad truth is that these conditions are seldom met and we are stuck in the land of the second best. However, that’s where analytics comes to the rescue. In the last two years, I have grown increasingly convinced that you can achieve the outcomes of workforce planning (see the definition below) through workforce analytics without all the pain and pretense that goes with it. This is a variation on modern economists’ recognition that the “invisible hand” of the free market is much better at resource allocation than a planning-based command economy.

The trick is to provide relevant information and insights to human capital decision makers – whether HR leaders, HR business partners, business leaders or people managers – who are closest to the action. This means people in business units who understand the business economics and have the local context, rather than “central planners” at Corporate. As long as the decision makers are incentivized appropriately through reward systems aligned with business and talent strategies, their analytics-based decisions on people and teams will ensure that the organization has the right person with the right skills in the right place with the right terms at the right time.

* Moore’s original observation on April 19, 1965 was that the number of transistors per square inch on integrated circuits had doubled every year since the invention of the integrated circuit. He predicted that this trend would continue for the foreseeable future. The pace declined in later years such that data density has doubled approximately every 18 months. This is the current definition of Moore's Law, blessed by Gordon Moore himself.

** At McKesson we define workforce planning as a business process that applies the rigor of financial planning and analysis to optimize the workforce on three fronts – capacity (headcount size and cost); mix (worker type and location); and capability (skills and experience) – to execute the organization’s business strategy.