Using workforce data to achieve leaner operations

A recent survey from PwC demonstrates the importance of companies using their internal workforce data to provide greater insight into how their organisations operate, as well as demonstrating the value of such data in understanding employees and their work habits.

But it isn’t enough for businesses to know what their data is saying; companies must also be able to ask the right questions of their information to get the most out of it. In a changing world in which business operations are moving towards digitisation and automation, data plays a vital role in helping organisations to create leaner and stronger operations.

Exploring your workforce data

While the idea of corporate reorganisation isn’t new, the use of internal data to determine the most appropriate allocation of resources is changing how we view this process.

Exploring the data available on a company’s workforce is the perfect starting point. This could include staff demographics, home location, pay grade, job titles, job functions, levels of training and the like. But being able to ask the right questions of this information can also make it easier to understand which job functions are the most crucial to an organisation, and where the greatest business value is created.

It is important to note that knowing which job functions provide the most value does not necessarily mean cutting the roles that add ‘less value’. For example, examining overtime spend could help an organisation to reprioritise daily tasks and work orders so everyone is contributing to ‘high value’ work across the board.

Alternatively, overhead costs could be reduced by evaluating the commute time of employees. If the majority of employees are commuting more than an hour, adopting remote working policies and a hot-desk system could allow more employee flexibility, thus reducing infrastructure costs.

Decision-making 2.0

When analysing workforce data with the aim of optimising operations, the first thing that springs to mind is reducing headcount. However, this is not the purpose of analytics. In fact, analytics holds the key to creating a leaner, happier organisation working to its full potential.

Every challenge an organisation (or person) faces can be solved using an approach situated along the problem-solving spectrum. On one end we can place "relying solely on past experience" and on the other "blindly following data".

The problem is, neither of these options provide the best results. If we blindly follow our data, we aren’t engaging with the information we’ve been presented, or making informed decisions with it. And in relying on past experience only, we’re reminded of the adage: "If you always do what you’ve always done, you’ll always get what you’ve always got".

The best way to solve these problems is likely using an approach that falls somewhere in the middle, with a combination of data-driven inputs and human context. In other words, with augmented intelligence, which is the use artificial intelligence to assist human-centric analysis and exploration that allows businesses of all sizes to benefit from better data-driven insights. It takes into account past context within the organisation and how decisions are usually managed, as well as insights gained from data to lead to the best chance of success for organisations.

Ultimately, we need the ability to ask questions and bounce ‘what-if’ statements off the data. We also need to understand if the performance metrics by which both employees and the organisation itself are measured are up to par. Analysing workforce data can help businesses to identify areas of weakness, and where to prioritise efforts in using this information to derive the greatest insights.

Applying these processes can help companies to create leaner operations without losing employees. In-depth data analysis of internal information can lead to effective restructuring that will get the most out of both employees and business operations. However, for this to work it’s essential to know how to read and understand the data in the context of the organisation. Otherwise, CEOs risk having to make decisions without being able to see the whole story.