Overlooked and underutilized: Turn your company's data into an asset
July 7, 2017
When I googled the term ‘data’, a mere 5,660,000,000 results appeared… and somehow, I think this is only scratching the surface. Clearly, this is an important topic. Every business runs on data and its manipulation by the firm's employees. But it is evident that businesses are overlooking some critical insights that their own data may reveal to them if utilized in an efficient, thoughtful way.
Here’s a real-life example of leveraging your data to help retain your best employees.
To begin, let's ask a question: Why do good employees leave companies? Anecdotally, we can guess (and there is a host of literature available on the subject) at the usual motivators like more money, a better opportunity, a seemingly uncaring boss, unfair workload, and even a career change. Exit interviews can help fill in some of the other blanks, but it is hard to get distinct patterns from such conversations. We suggest that a solid metrics and analytics function in your HR Technology department can help identify some key correlations to help understand employee motivations. You don’t need to hire a professor in statistics to get started down this road. Some observations will be obvious while others may be gleaned by the insight of an experienced HR Technologist to uncover them.
If you could see patterns where problems were stewing, wouldn’t that change the way you hire, promote, compensate, and retain your key talent? Of course you would. I am suggesting that this is actionable intelligence that will help smooth potential (and sometimes unanticipated) instabilities within your organization by applying attention in advance of any emergency.
What if I were to suggest there is another less understood factor: educational background. You might think that if we take the best students from the best schools, how can we go wrong? Evidence supports the fact that the millennial generation of students-becoming-workers is not expecting to be as connected to a company as Gen Xers. Moreover, they see their first or second jobs after graduation as post-graduate training grounds, not long-term situations. In other words, they are not really committed to their firms and are searching for their own path. This is a different mindset than prior generations who sought, if not nearly lifetime employment at firms, certainly career-type jobs at their initial employers. None of these approaches are wrong, but company managements need to understand and be prepared to engage with their employees' career aspirations.
At major financial institutions, it is common to see that top-rated revenue producers, like investment bankers, experience a turnover rate three times higher than the norm associated with most job families. Are your sales people turning over faster than the norm? Which work groups have a higher turnover than the industry average? Do you need to accept that because it’s ‘just the way it is?” and, if so, at what cost to the firm?
Bad turnover, aka unwanted attrition, is a hugely expensive cost for any company. Just think about the lost productivity (i.e., ramp-up and ramp-down times) when you look at just one year of the "wrong" type of attrition. What if you could change that variable by just a few positive percentage points?
A good Human Capital Management (HCM) database, or data warehouse, can be the source to explore, eventually leading you to the “who”, “when” and “where” in your organization that your unwanted terminations are occurring. But can it tell you “why”? As a first step, your HCM system can be queried for these metrics using any number of widely available visualization tools available in the market.
As an example, let’s take up the idea of educational background. HCM systems can essentially correlate attrition to the name of the school, its location, level of education and the years attended the school. So what is it about the demographics of secondary education institutions that influence premature employee turnover?
Alumni from those respective schools, if even employed at your company, may have some insight, but they may not be truly connected first-hand to the factors causing the turnover so cannot truly be relied based solely on the anecdotal nature of the data collected.
Also, the ‘structured’ data of your HCM is perhaps at its limit. Now is the time to introduce some unstructured data into the equation. This is the first step toward a more complex analysis that extends the data captured in technology and marries it with the more fluid narratives typical in personal interactions. As mentioned above, although limited as a single point of information, narratives can be helpful collected by speaking with these recent graduates or the school counselors. Perhaps create focus groups to gain insight into this phenomena. It’s clear that just reading thru a few of these instances, you are likely to find that they really did consider some companies as a postgraduate experience on their way to pursuing a different career goal. This is, of course, not a bad thing for them, or for the wider economy, but it’s in everyone’s best interest to know this at the onset of the relationship as the firm bears the majority of the expenses that could be used for other more, pressing business issues.
Furthermore, technologically speaking, if we load that information into an unstructured repository, for example Hadoop, and match it to the HCM data warehouse, then we create a tool that could produce long-term, firm-wide benefits. And that is the start of something entirely new and exciting. What else might you find by digesting other unstructured data that you would not have even considered until you applied this type of thinking and technology?
In any event, here is the question that your firm needs to answer:
Are you using all your available data and achieving a return on that ‘data equity’ that you spent so long to earn?
Tom Musante is a 20+ year technology veteran who has worked for major Financial Service companies. During Tom’s tenure, he held positions in which he led the development of the technology strategic roadmap tightly aligned to the goals of the company’s Global HR organization and delivered significant operational efficiencies. Tom also managed global project teams for the PeopleSoft HRMS implementation worldwide in the UK, Japan, Latin America, Canada, Asia Pacific Rim, Australia and Europe.