HR analytics: Three myths holding HR back

The need to get to grips with HR analytics has been widely reported, but the journey toward metrics-focused people management is often perceived to be far from straightforward. Rupert Morrison dispels three myths that are holding HR back from making the most of HR analytics.

People data is the lifeblood of the organisation. By taking ownership of it and using it effectively via HR analytics activities, HR can build its influence within the organisation. But unlike marketing and finance, HR has been slow to embrace a data-centric approach.

The result is that the function has found it harder to substantiate arguments and demonstrate value to the business. This in turn makes it harder to get the funding required to put people at the top of the business agenda.

A range of HR data myths have emerged over the years that have obscured, blocked and been provided as excuses for not embracing a data-centric approach. Here we look at three of these myths and how HR can overcome them to make the most of people data.

HR analytics myth 1: “Data and technology is scary”

Too often when working with HR people who have traditionally dealt with organisational data I hear the words “I don’t do numbers” or “I don’t do data”. But there is much for HR to gain by overcoming this fear of data and technology, and building relevant skills.

Data can enhance people functions and employee relations in numerous ways. For example, a data-driven approach to areas such as objective setting or competency management can help individuals to develop by identifying specific areas for improvement.

HR professionals working with organisational data need to develop “hard skills” around numerous and data interpretation/manipulation if they are to keep up with the demands of data-driven organisations. Likewise, organisations that do not invest in HR analytics will find themselves missing out on a huge area of potential business value.

HR analytics myth 2: “Data is too hard to process”

People data is naturally complex and messy. It can feel impossible to process because it is often stuck in numerous isolated Excel islands and systems. Data professionals have reported that cleaning and transforming their datasets is an extremely time consuming and boring part of their analytics workflows, often comprising 80% of the work. When dealing with people data this represents a real challenge. But data quality should never be used as an excuse. You have to start somewhere.

It is only by using data that you will be able to understand what data is valuable, what is not, and where improvements are needed. There is no doubt that data is hard work, especially to begin with when building your baseline. Too often the employees handling data get little personal value from the outputs of their work. It is no wonder therefore that excuses are found to avoid working on what is perceived to be a thankless task. Perseverance is essential for HR analytics success.

HR analytics myth 3: “More data means more insight”

People data is a goldmine, much of which has yet to be exploited. But that doesn’t mean that all available people data must be collected. Think of data as a value chain, starting at the systems in which data is stored and ending in actions and behaviours.

There is so much data available that the real challenge is often to specify and focus on what you are trying to achieve and therefore which data is relevant. Too much information is no information. I’ve heard of consultants compiling lists of more than 800 HR metrics in the belief that if you think of everything, then you will be able to answer any question.

data-driven-hr-analytics-bookThis extract from Data-Driven Organization Design by Rupert Morrison is reproduced by permission by Kogan Page.
Personnel Today readers can receive a 20% discount on the book by entering the code HRDODPT when ordering a copy from the publisher.

However, collecting too much data will over-burden people, cripple your data collection and complicate analytical processes, running the risk of nothing getting delivered.

Much better is to collect high priority data and then add to your baseline as you go. Remember, there is nothing stopping you from collecting more and new data further down the line.

Most of these myths are due to a mind-set towards data. By approaching data in the right way you can overcome many of the barriers which have traditionally “stopped” organisational analytics being at the heart of business decisions. Much of the challenge is simply a question of discipline when building up and maintaining a reliable baseline of data to work from.

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