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HR as a Data Leader: How It Can Help Solidify Your Strategic Role
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Readers of my blogs and articles know that my focus has often been on explaining Big Data.
This Big Data phenomenon is quickly spreading throughout the business world. Those that understand it well will be able to garner new insights that will give them a competitive advantage in their business activities. This is why I’ve focused on demystifying it and talking about how HR can start adopting this new technology to drive business results.
For this post, though, I want to change focus a bit. Instead, I want to focus on a related and equally important idea about utilizing Big Data – developing and growing your role as a “Data Leader.”
I’ve been asking myself, “What are the most important concepts HR professionals should always keep in mind as they embrace their utilization of Big Data into their portfolio of data tools?” The answer to that question is quite clear: “Don’t let the excitement about Big Data cloud your judgment.”
Big or small, the same rules of data analysis still apply. And, being able to demonstrate your data intelligence is just as critical a skill as is knowing when and how to bring data into the decision making process.
With this in mind, based on my years of experience, here are the three (3) most important concepts to always keep in mind:
As both a producer and consumer of data analysis, it is paramount that HR professionals be able to differentiate between a neutral, unbiased interpretation of data and one that uses the data to tell a desired story. Darryl Huff’s famous work, How to Lie with Statistics, is full of timeless cautions on this topic. He reminds us that despite its mathematical base, statistics is as much an art as it is a science.
Today, the HR marketplace is replete with vendors and services all portraying themselves as the answer to every business problem and need. With the ever increasing messaging on the need for HR to be “evidence-based” in their decision making, it is now nearly impossible to find a vendor or service provider who isn’t trying to use data to base their claim that their solution is what is needed.
Let me offer a simple rule to keep in mind – “Even if you can’t see any demonstrable bias in the data, always allow yourself a reasonable sense of skepticism; especially when the source of the data being provided is from someone who has a point to prove or a product to sell.
Correlation does not imply causation. This is one of the most basic tenets of statistical analysis.
The idea is straightforward – “just because there is a connection between two variables, does not necessarily mean that one causes the other.” This is very important in the world of Human Resources.
HR has long pursued being able to tie its activities to positive business results. However, that pursuit will be fruitless until HR can understand which variables have that type of impact and can then subsequently statistically demonstrate the causal connection between HR activity and profitability.
Interestingly enough, though, HR can also capitalize on simply knowing there is a correlation between data points. The trick is to know when correlation alone will suffice. Again, simple rules govern when correlation is sufficient. When the intended outcome is either:
When it is not necessary to know why a relationship exists and you only need to be concerned with the fact that a relationship does exist, then you can leverage that insight to drive desired business outcomes.
This one is actually embedded in the two previously outlined concepts. However, because of its critical importance to this discussion, I am calling it out on its own.
The right sample size is an important feature when the goal is to use data to make decisions, evaluate results, or to determine either correlation or causation. Sample size is critical, both in terms of the size of a population or data-set, as well as to how the sample size is determined or created.
As noted, marketing collateral’s purpose is to story-sell. The data-savvy consumer of this type of information should always be on the alert for data that is purposely cherry-picked to convince the buyer of the vendor’s stated value proposition.
Not coincidentally, HR and their data analytic teams must abide by this same concept. When data will be used to support decision making, to create policy, to make recommendations, or to purchase products, they must present an objective analysis that is based on a statistically significant sample size.
I felt compelled to write this article largely because I’m noticing an increase in the amount data and analytics-oriented marketing messages being presented to the HR community. I’m concerned about the HR professional who is not yet accustomed to examining the claims made by others that are “supported” by their own reported data.
It is important that HR professionals not blindly accept information at face-value simply because it is presented as fact supported by graphs and charts. With so many “Big Data” and analytic oriented solutions now coming to market, HR must know how to take a critical eye in evaluating these claims.
This same critical eye is also what HR must bring to their own data analytic work. Objective storytelling based on adequate sample sizes, with appropriate conclusions as to correlation or causation are the core traits of a seasoned data veteran.
I remember back to the start of my career in Human Resources. There I was, a Generalist in an explosive growth company called PeopleSoft. Back then, Dr. Lyle Spencer’s 1995 book titled Reengineering Human Resources was like the bible to us.
Dr. Spencer’s definition of a strategic HR function was one that was able to switch its focus from spending 60 percent of its time on administration work to instead focusing 60 percent of its time on planning and the development of HR programs. Today, many HR departments have moved well past that milestone.
The stature the HR profession has achieved is clearly evidenced by the fact that 79 percent of CEO’s have their senior HR leader reporting directly to them – primarily to play a key role in defining the direction and overseeing the performance of the company. HR has graduated from its role as a “data steward” to that of a business leader.
HR now must regularly evaluate data and information as it guides and leads the businesses it operates in. And because of Big Data’s statistical analysis underpinnings, it is even more critical that HR take its data-savviness to entirely new heights.
Phil Simon, in his book Too Big to Ignore, the business case for big data” talks about the popular perception of HR folks tending to almost exclusively rely on gut instincts versus making decisions based on data. I’m convinced that as the profession continues to embrace the use of data and analytics into its practices, that it will also continue to solidify its role within the C-Suite.
This was originally published on eQuests’ Floating Point blog.