Today’s Business Analytics – The Essentials of Data-Driven Decision Making

In the world of business analytics, a fundament tool for data-driven decision making requires deep learning, but people-related issues are often addressed informally with a strong reliance on instinct. Analytics instead relies not just on objective data but also on deep analysis to remove subjectivity from decisions.

Data-driven decision making is an approach to business governance that values decisions that can be backed up with verifiable data and analyzed data.

The success of data-driven approach is reliant upon the quality of the data gathered and the effectiveness of its analysis and interpretation. Such success is possible because the quality of data gathered is ensured. It used to be a long and difficult process to collect, extract, format and analyze the data, thereby requiring full-time experts which of course impacts the time required to take action and the quality of these decisions.

In recent times, the development and democratization of  business intelligence software encourages anyone without a heavy technical background to analyze and gain insights from their data, enabling lower support from the IT department to produce the reports that will later need to be analyzed, and also accelerating the decision process.

When it comes to the use of data to drive businesses, organizations, such as Google or Facebook, it’s usually iconic. Although we can draw out a lot of discussions about their practices with regards to topics such as privacy, ownership, and governance, there are no doubts that they have been pioneering the field both in the technical approach as well as culture.

The first step taken in this journey is to acknowledge how effective the data-driven decision making can be, also identify the right infrastructure needed to be in place that enables the adjustment in the culture of the organization.

Quality of the data– The maintenance of data quality is a difficult but necessary task. In order to keep achieving reliable and consistent customer data, businesses must constantly manage data quality so that they can trust their data and enable quicker and more knowledgeable decisions.

By Odey Pauline.