This article was originally published in DNA India.
In my earlier article, I mentioned that People Analytics was here to stay. That’s all fine and dandy, but how do organisations get started with people analytics? Do they first prepare the groundwork by ensuring that all the systems for data collection are in place and that they are all integrated seamlessly before one can get started with using analytics? Or, do they work piecemeal by looking for chunks to automate and analyse? Moreover, what does analytics even mean? Does it mean one looks for reporting or dashboards or are we talking predictive analytics here?
The first thing to be clear about is that analytics is a continuum. At the lower end of the continuum, are questions that deal with “what happened?” and are generally represented by reporting tools and dashboards. The next step in this continuum is attempting to gain some insights into “why did this happen?” and this involves some amount of statistical analysis to identify patterns in the data. One level up from that is the answer to the question “what’s happening now?” and involves monitoring of information and real-time tracking of data. The final level in this continuum is one where predictions rule, which helps in answering the “what will happen in the future?” question.
Moreover, analytics is also a journey with varying degrees of usage across organisations. As Patricia Saporito details in “Applied Insurance Analytics” and which holds true for analytics across domains, analytics laggards use gut-based decision making, have no processes for gathering and working with data, typically use excel-based systems and have very poor quality data. Aspirers and novices have some awareness of analytics and use it to validate their gut instincts. There are processes in place for running static reports and there is some basic data quality awareness. Practitioners use analytics to support strategic decision making. They have processes in place for data exploration and visualisation as well as fully integrated data. Finally, savants use analytics to drive business strategy. These are organisations which have analytics and data-driven decision making embedded in their DNA. These are also the organisations that probably have a chief analytics/data officer as an integral member of the executive team. They look at data as a strategic asset of the organisation and use tools such as predictive analytics, text mining, and natural language processing as well as machine learning approaches.
Where an organisation wants to go depends entirely on where it is starting from. For the laggards and novices, while it is important to put processes and data stewardship in place, that is rarely a good place to start, simply because it is typically difficult to obtain internal buy-in based on a rosy future. It is far better to start with a specific question in mind that requires solving, which, at the same time, is amenable to a quick resolution. The classic, “low-hanging fruit” in corporates. Taking such a problem, using available or easily available data with some fairly basic and easily accessible statistical techniques to provide direction can help illustrate the benefits of a more comprehensive data and analytics policy to the powers that be.
Let me illustrate the above with a quick example. All organisations look to cut costs and since HR is typically seen as a cost centre, let me start with seeing where HR can cut costs. We all know that hiring costs are particularly high when one accounts for the entire hiring process including agency fees, signing bonuses and so on. While some of those might not be under an organisation’s control, the hiring process itself is and actually contributes a major chunk to these costs. Taking a closer look at the hiring process and optimising a favourite HR metric, time-to-hire (TTH) can actually be an easy first step in the analytics journey. Organisations can identify the time taken for every step in the hiring process and the capacity utilisation of the people involved in this process without too much difficulty. Identifying one or two steps for optimisation and cutting down the TTH by even a few days can lead to huge cost savings that HR leaders can then place in front of the executive committee as an example of what data mining, even in its preliminary stages can do. And who can argue with that?