The intersection between HR strategy and HR technology

HRs Correlation to Business

HRs Correlation to Business

Nov 8, 2010

When we talk about the impact of HR activities on our business’s operational production, we don’t usually think that there is a direct correlation.  In fact, some of our activities probably do have a relatively high correlation effect on business outcomes that we might be surprised about.  In defining correlation, we usually think about it on a –1 to +1 scale, with –1 being negatively correlated, 0 being no correlation, and 1 being positively correlated.  From an HR point of view, if we were able to show that there is a positive correlation from our activities to the business outcomes, that would be a pretty big win.

Personally, I don’t have any metrics since I don’t work in your organizations with your data.  However, with modern business intelligence tools and statistical analysis, it’s certainly possible to discover how our HR activities are impacting business outcomes on a day to day basis.

Take a couple examples.  We know that things like high employee engagement leads to increased productivity, but we don’t always have great metrics around it.  Sure, we can go to some industry survey that points to a #% increase for every point that the engagement surveys go up, but that is an industry survey, not our own numbers.  Especially in larger organizations, we should be ale to continue this analysis and localize it to our own companies.  Similarly, we should be able to link succession planning efforts to actual mobility to actual results.  Hopefully we’d be showing that our efforts in promoting executives internally is resulting in better business leadership, but if we showed a negative correlation here, that means that our development activities are lagging the marketplace and we might be better served getting execs from the external market while we redefine our executive development programs.

I’ll take a more concrete example.  Lets say we’re trying to measure manager productivity.  We might simplify an equation that looks something like this:

Manager Unit Productivity = High Talent Development Activity / (Low Recruiting Activity + Low Administrative Burden)

If this is true, we should be able to show a correlation between the amount of time a manager spends on development activities with her employees to increased productivity over time.  Also expressed in the equation, recruiting activity should also be negatively correlated to the manager’s team performance.  If the manager is spending less time recruiting, that means she is keeping employees longer, and spending more time developing those employees – therefore any time spent recruiting is bad for productivity.

I’m not saying that any of these things are the right measures or the right equations.  What I am saying is that we now have the tools to prove our impact on business outcomes, and we should not be wasting these analytical resources on the same old metrics and the newfangled dashboards.  Instead, we should be investing in real business intelligence, proving our case and our value, and understanding what we can do better.

3 comments

  1. There are some good studies on this. As an example have a look at Simon / Martin / Rojo’s IE Business School working paper on Human Capital, Firm Performance and teaching of HR in business Schools:
    http://latienda.ie.edu/working_papers_economia/WP07-05.pdf

    An approach I feel is under-utlized is using data-mining on large HR data sets to find patterns. Typically you look at one measure and build groups that increase the likelihood of the measure being true. It needs interlinked data but the data can be relatively unstructured. An exploratory approach which uses data-mining in partnership with data visualization seems to show promise.

    Sometimes the real value is found not in the numbers theirselves but in the qualitative conversations that should follow any finding to explain why it is happening. So often when I show what is happening the stock response is ‘that is obvious.’ Obvious it may be, but obvious with supporting data is far more valuable than obvious as a conceptual model.

    Ultimately statistics won’t say what will happen, but what is likely to happen. Any future result should contribute to the ongoing development of the model.

  2. Your thinking is flawless, as always. However, I do think there is a role from the industry surveys in providing an initial data point to even use in a calculation. Organizations can plug these in and see what is possible. They can then compare their own data which is ultimately more important. But from industry data that can start doing a simple calculation and from that see how easy it is.
    Thoughts?

  3. I agree Lexy. So much of my work actually starts with an external benchmark – it’s really a great starting point and check where you stand against the industry and where you’d like to be. it’s also a great way to figure out the “low hanging fruit” and where an organization really lags.

    I don’t like to use a survey for pinpoint accuracy, but I do think they are beautiful for initial benchmarking. I know I’m preaching to the choir, but surveys really do rock!

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