How Big Is Too Big? Making Sense of Data Analytics to Drive Business Value

HR is a field that has always generated large volumes of data. For instance, candidate profiles, recruitment data, career progression data, personal development and review data, appraisal data, employee engagement data, etc. With the rapid advent of technology, it is even possible to analyze and leverage data from social media profiles, emails and so on too (apart from the traditional means) to understand employee sentiment and gather other such insights.

Earlier, businesses and HR teams would not leverage such data to gain deep insights to refine/ add value to their decision-making. Today, there is a growing acceptance and shift towards evidence-based HR and HR analytics.  

Different organizations and sectors are deriving different kinds of benefits from using HR analytics. A majority of businesses are still using big data and analytics to understand administrative and standard KPIs such as absenteeism, employee satisfaction, etc. as they are easier to measure or that they are used by everyone else. However, HR analytics is not limited to these standard KPIs and definitely not to the HR function.

Invaluable and unique HR insights and metrics that can be unearthed from HR data will enable better business decisions and enhanced performance. Why? HR deals with employees. So, better HR decisions, effective and efficient HR processes, nuanced understanding of employees and aligning their goals to the common business goals, improving the leadership/ management decisions with regard to employee-related matters, improving employee well-being, etc. will directly impact how the business performs in the long-run.

So, evidence-based HR is necessary for driving business value and this is something everyone agrees upon. But, is it sufficient even if all our insights are actionable? Should we focus only on data and leave no space for human intelligence and intuition in HR decisions? Are we becoming too fixated on data-driven decision-making? Will focusing on data-enabled decision-making, where people make the decisions ultimately, be a better solution in HR and otherwise?

Let us try to address these questions.

Making sense of data analytics to drive business value

Data by itself adds no value: Data has come to occupy a very important position in today’s day and age, but as such, it is only information and adds no value to a business.

For instance, does knowing that you walked 10,000 steps in a day add any value to your life or health? It is just a piece of information. Then, will knowing the number of steps you took in 10 days be of value? No, not just as pieces of information or even in visual forms such as graphs or pie charts. You must have more insights as to why you took more or fewer steps, if there were special events/ activities that prompted you to do so, if you can incorporate some of these positive habits on a regular basis to make yourself healthy and so on.

So, if data and data analytics must add value to a business, it must be used in context and actionable insights must be obtained.

How big should big data be? There is a widely held belief that increasing the volumes of data and creating data lakes will benefit the business. The irony with big data is that data has no size, it’s a category of classification and the base unit for information. It is the quality and veracity of data as well as the level of understanding you have about the context of and relationship with other data that matter. There could be biases in the way we collect data that skews the insights in that direction. There is also a possibility of outliers distorting the insights if we are not careful. Simply adding more data to the data lake without understanding the data itself and without exercising prudence would only lead you to drown!

The data strategy, instead of focusing on volume, must focus on leveraging quality data to address business problems and make strategic decisions that will lead to operational and transformative outcomes. It must be applied in a multi-dimensional, cross-sectional manner. The end goal of data, data analytics, HR analytics and so on must be business outcomes and driving business value and not data or analytics alone. Do not narrowly focus on mechanics and technicalities.

The depth of analytics is important: Once the focus and outcomes of analytics is fixed, you must look at the depth and kind of analytics to engage in. For instance, a sentiment analysis may be necessary to understand how employees perceive certain business initiatives and accordingly, streamline or improve the initiatives based on the insights derived rather than focusing on numeric feedback alone. Descriptive analytics may not suffice in certain areas such as turnover and prescriptive analytics may be necessary.

Actionable insights: As mentioned earlier, your HR data must provide you with actionable insights if you are to enrich your business decisions and solve business problems. Whether data-driven or data-enabled, the insights must be actionable so that it empowers different actors in the business to perform their jobs better and improve the quality of business decisions, thereby, driving value.

Lack of action-oriented insights and business focus is the biggest drawback of the traditional data-first approach. Companies typically want to use data, so they end up doing some very basic or dirty analytics, which do not yield actionable insights which means the business results do not change for the better.

Nothing can replace human intelligence and intuition: While data-driven decision-making has several benefits, it also has certain drawbacks. In this approach, data is the central and mostly, only input in business decision-making. So, if your top metrics up and your revenue is up as well, then you assume that all is well and continue on with that strategy.

However, in data-enabled or data-informed decision-making, data is one of the inputs among other variables. In this approach, you use the insights derived from the data to get a deeper understanding of what creates business value. So, even if all your key metrics and revenue are up, you will still try to understand the counter metrics, maybe engage in sentiment analysis and so on before formulating your strategy. This way you are finding a way around the blind spots of the data-driven approach and leveraging human intelligence, expertise, intuition, non-linear approach and creative thinking. All these and the subjectivity they bring to business decisions are crucial in HR which deals with human beings.

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