What Is Data-Driven Decision Making (DDDM) and What Does It Mean?

Making decisions based on hard data instead of basing decisions solely on instinct or observation is the primary objective of data-driven decision-making (DDDM). The data-driven decision-making process has become a much more fundamental part of all industries, including important fields like medicine, transportation, and equipment manufacturing, as business technology has advanced rapidly in recent years.

The terms “data-driven decision making,” “data-driven decision management,” and “data-directed decision making” are already used interchangeably to describe the practice.

Using data-driven decision-making, decisions should be extended from data sets that show their projected effectiveness and how they might work out. It’s common for businesses to use a variety of enterprise tools to gather and present this data. Until recent times, commercial venture decision-making was primarily based on observation or informed guesswork, which is a stark contrast to today’s more complex decision-making process.

Decision support software can support in deciding how a product will work in the market, what a customer will think of a slogan, or where to allocate business resources. As a result, the need for data-driven decision-making solutions has grown significantly. MIT’s Center for Digital Business conducted a study that found 4 percent higher productivity and 6 percent higher profit for companies that use data-driven decision-making, as reported by TechTarget.

Companies have developed self-service data analytics products to meet this burgeoning demand, with the hope that these products will lead to more equitable data collection and transfer. The bottom line is that without self-serve tools, executives and others outside of the IT department can’t do their own analysis and present their own decisions backed up by the data in question, while with self-serve decision support tools, they can.