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In a recent post about HR analytics, my colleague Kouros Behzad asked the question, Does HR Really Fit the Definition of Big Data? It’s a valid question and one he answers in the positive by describing the myriad varieties of workforce-related data that companies must manage; the need to connect the right data in a way that makes sense to identify trends, risks and opportunities; and some of the tools that are available to help HR organizations with these initiatives.  I’ll go even further and think about who is sitting in front of the laptop or iPad, working with the big data (I was once an HR practitioner, after all).  If it’s the right person, with the right skills, it may be the most encouraging development we’ve seen in finally making the HR profession more strategic.

In a recent study of 250 HR executives by Oxford Economics, the respondents agreed that the benefits of improved HR analytics are substantial (“improved” meaning improved impact on the business at large, not just HR).  The Oxford study noted that within three years, 70% of those surveyed expect to have the proper tools to support the business with analytics.  Clearly there is a need and an appetite for HR analytics – not just operational HR metrics such as time to hire, or cost of hire, but true HR analytics which combine workforce-related data with other organizational data to achieve strategic impact and business change.

However, the Oxford study also pointed out that only 50% of the respondents considered HR to be a profit driver (a dismal 23% in North America).  So another important theme is emerging, and that is the readiness of the HR organization to support HR analytics.  It’s a skill set, many say, which HR organizations do not have today, and need to nurture, develop or acquire.  With the right skill set leading to more sophisticated use of HR analytics, I think those numbers can easily improve.

What are the skills needed to be an HR Analyst?  According to Peter Howes, Vice President, Workforce Planning and Analytics at SuccessFactors, it’s a combination of business acumen and understanding of models, along with analytical skills to interpret the information.  I would add it’s an ability to envision what might be, with the ability to build scenarios and queries to investigate that hypothesis, and to communicate this to company leadership in a language that makes sense to them – i.e. business language, not “HR” language.

So does HR have a big data problem?  I agree with Kouros, that yes, it does.  HR analytics technology is a big part of the solution, but as Peter Howes noted in a recent webinar, “Quality data and tools are necessary, but not sufficient.  Analytical and conceptual capability gets you over the wall to true business impact.” Having the right combination of technology and skills may be the key to finally making the HR department strategic: HR analytics, in the right hands, will help the HR profession articulate the value of HR initiatives and actual impact on the business – and take deserved credit for it.

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  1. Mashhour Bedaiwi

    Perhaps an important point to note, is the dependency of Big Data definition on the size of the organization, and volume of data which flows within a certain period of time. Is this measurable by any means, and does it affect HR data as being a part of Big Data?

    Moreover, by convention , the term Big Data always triggers the thought of complex analysis of natural phenomena , such as geological aspects of an oil field , or simulations regarding physical phenomena, Can HR data be that complex ?

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    1. Ray Rivera

      HR data is actually quite complex, but for different reasons than natural
      phenomena. Unobserved variables are a major problem with HR data, and are
      sometimes dealt with using proxies or linear combinations of predictors.
      Another big problem is selection bias, which is often remedied using
      instrumental variables. Large datasets can provide a rich set of proxies and
      instrumental variables outside typical HR measures, yet can help correct these
      shortcomings using well-established statistical procedures.

      HR phenomena also tend to be multi-level (individual, business
      unit, division), which require very large datasets to in order to get good
      models. Multi-level social phenomenal have been studied for decades (for
      example, school effectiveness) using national databases. HR has up to now
      lacked data of this size, and the first academic studies applying multi-level
      modeling to HR are starting to be seen. This could provide major insights to
      the HR profession in the coming years.

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