Organizations need to think long and hard about their data analytics use cases. Source: Shutterstock

Organizations need to think long and hard about their data analytics use cases. Source: Shutterstock

Why success with data analytics requires an emphasis on the last mile

TECHNOLOGY has made it possible for companies to collect data from a variety of sources — which is why most businesses have significant data repositories filled with rich customer data.

To translate that data into insights, however, organizations must undertake data analytics projects that span the entire organization and tie together various data bundles.

Although organizations are investing heavily in collecting, storing, and analyzing data, it seems as though business leaders are struggling to realize the promised returns from the exercise.

Experts that work with businesses in the data analytics space often believe that the issue that most leaders face is that they start the project with the data repository in mind instead of the end-use and the manager. This is often referred to as the last-mile disconnect.

Consultants who work for think tanks such as BCG and McKinsey and help companies across industries understand how they can leverage data to transform their organization often believe that the best way to generate insights that are meaningful and reliable is to start with the manager and understand their needs first.

The risk, however, is that working backward might seem challenging because, at the end of the mapping process, the team might realize that they don’t have the data they need to deliver on the manager’s expectations.

Truth be told, that’s actually a good thing — because in today’s world, with sensors becoming affordable and the internet of things (IoT) and 5G taking off, no data is out of reach.

When companies map out their data needs for an analytics project and realize that they don’t have the data they need, they can simply embark on a data collection exercise and create a pathway to collect the right data.

This is often the best approach because it really takes control of the insights that are being generated, and delivers on the expectations of the manager who will be using the data.

Another benefit of starting with expectations and mapping out data in the initial phase of an analytics project is that the company is able to list down how each project can benefit the organization, quantify the value of the project, and then prioritize the project based on the organization’s needs.

When the company starts with data repositories, however, quantifying the value of benefits that each project will deliver isn’t practical.

Ultimately, organizations need to understand that data is critical to thriving in the digital era and making the right decisions.

Using data analytics with the user and their use case in mind is definitely the best way forward, especially for those organizations that are playing catch-up and need to accelerate their journey to digital by prioritizing the benefits that warrant the investment of the required resources.