Big Data and Pay

We all have our favorite theories about what engages and motivates people at work but what if we could validate our assumptions with actual data? Compensation is such a large percentage of overall operating expenses I predict we’ll start seeing more deep analysis around its effectiveness at driving engagement, retention and performance.

In fact, it’s already happening, according to a recent WSJ article Big Data Upends the Way Workers Are Paid.  The problem with the big data approach is that it’s aggregated across multiple companies so its applicability to a specific company may be limited. On the upside, big data can offer a broad view of what has worked - and not worked - across thousands of organisations.

What is Big Data? It basically means lots of data. Complexity is implied as well.

Today companies use a variety of external information to decide on compensation, including market data, best practices, etc. Unfortunately, that kind of information only gets you so far because it fails to measure the impact of specific strategies within your own organisation.

For example, imagine your company is losing a critical mass of talent. What challenges would that create, besides losing experienced people and having to replace them with inexperienced people?

• Recruiting expenses might be above the benchmark for your industry;
• Morale might also be an issue, impacting customer service and productivity; 
• Continuity might be a problem for ongoing projects as tribal knowledge flees the organisation.

You get the idea: high turnover not good. The typical reaction is to ignore it or throw money at it.

But what if you could build a model of your own organization and track things like salary, career progression, turnover, absenteeism, variable compensation, etc., and use this information to build a predictive model of why people leave? The model would tell you how much impact individual levers such as compensation have on retention so you could make better decisions about how to address the retention problem.

For example, when confronted by a top performer who has been absent a lot recently and just exercised all their vested options, you could evaluate whether a salary increase is likely to retain them based on your model. If the model tells you that a promotion would be more effective than a raise or that the manager they work for has an unusually high attrition rate, these are useful data points to inform your decision.

Best of all you could more precisely manage how much of an increase to give and stop at the point where giving more loses effectiveness.

*The caveat here is that this will only work for organisations with a statistically significant sample population and sufficient historical data.

So, now you’re thinking, ‘Yeah, right, as if we have any extra money to give people!’ I’m going to go out on a limb here - actually, I live on this limb so I’ll just stay put - and suggest that the reason you don’t have budget for increased rewards may be that your data is insufficient to make a business case. You may know a top performer is paid below market but you don’t know if they’ll leave the company or whether it even matters if they leave the company.

In other words, you have what is known as a ‘hunch.’ Hunches can be useful for pointing you in the right direction - and giving CFOs a rare chuckle - but you still have homework to do.

Bottom line: You need more precise data to get more money from the people whose job it is not to give you more money.

There are many things besides compensation that motivate employees and few companies are good at understanding and managing them. However, I expect we’ll see a change in this area as management consultants figure out how to monetise big data.

In the meantime, hire a graduate student specialising in statistics to build a predictive rewards model.  If they do a good job, you can use it to fend off the management consultants who want to hire them away for more money.

Laura Schroeder is a global talent specialist at Workday, headquartered in Pleasanton, CA.  She has nearly fifteen years of experience envisioning, designing, developing, implementing and evangelizing global Human Capital Management (HCM) solutions and holds a certificate in Strategic Human Resources Practices from Cornell University.  Her articles and interviews on HCM topics have been published in the US, Europe and Asia.  She lives in Munich, Germany and enjoys cooking, reading, writing, kick boxing (well, kicking things) and spending time with friends and family. If you want to read more from Laura, check out her talent management blog Working Girl or follow her on Twitter @WorkGal.

Picture courtesy of Mediaplexblog.com.

Comments

[Jacque Vilet]

Great article Laura! I agree with you. Compensation professionals need to start rolling up their sleeves and start doing more analysis than just survey result analysis. I think that we will see a much more diverse use of total rewards in the future. Money motivates some people almost to the extreme (Wall Street brokers) and others want more recognition, work challenge, etc. The answer is understanding what motivates your workforce --- both in the aggregate and by jobs, by functions, by business units, etc. The result will likely be, again, more diverse ways to deliver total rewards.

[Jacque Vilet]

Forgot to add ---- yet one more reason following "best practices" is worthless. Not that it ever was worth doing. Smart people know that "best practices" are "one size fits all" --- just re-packaged with a different name.

[Laura Schroeder]

Thank you Jacque - I agree we'll start seeing a shift from 'best' practices to tailored practices as companies invest in more precise data.

[John A Bushfield]

Pricing jobs/functions in the rapidly changing business environment is more art than science, and getting relevant data points can be a challenge. Jacque is spot on with 'best practices'; most of them are yesterday's news. High turnover, bad management, and misdirected incentive programs have been around for a long time, and pay rates have been an easy donkey to pin the tail on. Analysis is critical in order to understand what's happening within an organization, but we make it hard on ourselves by the complexity we seem to love in designing and managing compensation systems. Yes, money is important, but only up to a point. Motivating employees involves a number of variables, and the key is to understand, quantify, and communicate those variables. Easier said than done.