Data Driven vs Data Centric Organizations: A Point of View

In my discussions with multiple stakeholders the terms ’Data Driven’ and ‘Data Centric’ are used very interchangeably in context of organizations/enterprises who want to move towards a more insight-based decision making.

To Business Executives and even to people in the Information Technology industry the terms may seem synonymous and interchangeable but they are different and define different maturity levels and capability of an organization to collect, process and leverage data assets for business growth .

This classification can broadly be divided into three distinct maturity levels.

On the lower end of the spectrum are organizations which can be termed as Data Aware. These organizations have started to realize the potential of data and are collecting the same with a view to use it for decision making. The staff is aware of where the data is available, how to store and access it. Though a large part of business decision making is still instinctive, and gut based but some dashboard (even excel based) are being used within the organization.

Data Driven is the next step of maturity. A data driven organizations is actively using data to make decisions. There is an enterprise wide or divisional level data strategy in place which articulates vision and roadmap of the company in respect to data. Various intrinsic levels of maturity exist within this broad maturity spectrum, with some organizations higher up on the maturity scale than others than others. However, having a data-lake or a team of data scientists doesn’t automatically make an organization data driven.

On the other end of the maturity spectrum are Data Centric Organizations. Here data is at the core of the business, it is recognized as an asset and governed, managed, and consumed with a view to bring in operational efficiency and future innovation. There are divisions (often headed by CDO/CDAO) whose primary job is curate and make data available with the right context and speed to enable business decision making. New products or customer segments of one, customer 720 etc are based on the internal and external data that the organization can curate, blend, and leverage.

But can this be further extended to increase the data footprint and create a truly universal data ecosystem mimicking the whole business ecosystem of an organization? More on this later!!?