Data Analytics in HR: Stop making things complicated for yourself!

Data Analytics in HR: Stop making things complicated for yourself!

HR professionals, it's the end of another year.

Looking back, if you have been spending time and effort fiddling with data at the expense of strategising/implementing action plans for improvement, or even actually interacting with the people in your organisation, then you should stop to review your use of Data Analytics.

There are many compelling justifications for the use of Data Analytics in HR. Management decisions should be data-driven as much as possible, and there are many HR metrics that can and should be tracked to provide references for improvement. Fundamentally, metrics such as FTE, Revenue/Cost per employee, Turnover, Absentism, etc should be data that HR is constantly monitoring and analysing to guide improvements in People practices. As the saying goes, ‘What gets measured gets done.’

There are also many successful adaptations of Big Data in HR across industries, many are even applied to qualitative aspects of HR such as in Employee Engagement. Indeed, the application of data analytics has long been proven to be increasingly critical in enabling HR to become a true strategic partner.

However, those success stories have tempted many organisations to jump onto the Big Data bandwagon without first understanding the implications. Consequently, those organisations spend large budgets on commercial analytic tools but get overwhelmed by the deluge of data generated. Before even going into the scale of handling Big Data, it is prudent to ensure that you understand basic data analytics and have the resources to maintain and utilise it. Seems like a truism, but sometimes the momentum of bureaucracy, the exuberance of over-optimism, or even simple over-confidence can make us overlook the most basic things.

True story:

A large company I worked for previously engages Gallup to conduct their annual employee engagement surveys. Gallup is an established and well-known performance-management consulting company, renowned for their public opinion polls globally, so their reports for employee surveys are extremely comprehensive and detailed.

We received such reports after the conclusion of a survey one year, and the Chief HR Officer (CHRO) instructed the team in charge to summarise the findings for all 20+ departments into a few info-slides to present to the CEO. Curiously, there was no significant change to the data statistically compared to the previous year (all departments had a slight improvement in their engagement scores, but each with variances in the breakdown of the scores), and the most interesting data was the qualitative feedback from the staff.

The issue is that consistent data makes boring presentation, so regardless of how we do up the summary, it did not appear right to the CHRO. When pressed for clarification on her feedback for improvement, her consistent reply was the vague corporate-speak “put yourself in the CEO’s position, what would you then want to see?” The CHRO subsequently spent almost a week discussing with 2 of the senior HR managers, each in turn. I cannot remember if they managed to come up anything satisfactory but the look of frustration on either managers’ face whenever they ended a discussion on the subject was not pretty at all.

Looking back, this incident made me realized how Data Analytics can blind and hinder instead of help.

Data can be messy

Data analysis results are not always neat and clear. While good data interpretation and presentation are crucial in enabling us to make sense of the numbers, being too hung up on fitting the numbers to a convenient presentation framework simply reflects a poor understanding of data handling. More often than not the issue is the management’s vacillation on the degree of detail that they want to see in the executive summary. But it may simply be a case of having too much data that it overwhelms the audience with a bout of info 'indigestion' leading to Analysis Paralysis.

Data analytics is not crystal ball gazing

This is made worse by a lack of clear direction on what the data is used for. Data analysis is most effectively used to validate or support hypotheses/observations. Instead, many tend to mistakenly treat it as a crystal ball that could reveal all the issues. Without having an objective to set the parameters of analysis, it is difficult to find meaningful trends and correlations. Retrospectively, it is possible that my CHRO then had problems conveying her instructions because she did not have any more strategic objective for the data beyond coming up with a nice report for the management. It still puzzles me why more hair was split doing up a ‘perfect’ management report than on action planning to actually improve our employee engagement. 

Data analytics is not a complete solution on its own

Data Analytics is powerful but it is ultimately still a tool and not a complete solution. HR still needs to rely on human talents to do the analyses, come up with relevant strategies, and execute the plans in order to fully enact the desired changes to the organisation. In fact, similar to any adaptation of new technology, data analytics tools bring their own set of issues such as upkeep cost, adaptation period, personal data protection, privacy protection, IT support resources, data analytics competency training etc. There are many consultancy companies out there that offer "end-to-end solutions", but unless you have the right resources and information to advise them on the strategic direction, it will be a case of GIGO (Garbage In, Garbage Out). 

Data analytics reliance can become dysfunctional

As a tool, Data Analytics can exponentially raise HR’s strategic capability but only if we use it as an aid and not as a panacea. The use of advanced analytics tools can be so addictively convenient that it can cause us to over-fixate on the map and neglect the terrain in front of us, so to speak, and it can lead to a rather dysfunctional outcome. We have heard of people instinctively checking the weather app on their phone to find out if it’s raining outside instead of simply looking out the window. Similarly, sometimes we forget that as HR practitioners our expertise should fundamentally be an ability to deal with people interpersonally, part of which is to be adapted at simple and reliable heuristics of how to ‘read’ people. E.g. to find out the employee engagement, the most direct method is to walk the ground, find out how the people are feeling by observing the energy, behaviours, etc at the workplace, or simply speaking with them.

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