Gaining Competitive Advantage with Data-Driven Decision Making

5 min.


Minimizing risk and maximizing profit is the goal of every commercial undertaking. Top managers should ensure they make the right decisions in business, and the only way to do that is to use data analysis methods to make these decisions.

There are basically three major ways in which we make decisions:

  1. Based on years of experience/eye for something
  2. Based on data (could be made automatically by computer systems)
  3. Combination of both.

The irony of the present moment is that having so much data to work with, intuitive decision-making is becoming a more important decisive factor, so in real situations the third method of making decisions is probably the most common.

Life experience, intuition and a business sixth sense is not the sphere of ‘traditional’ data analysis and planning, even though it is getting some attention from analysts, such as Gartner. But when it comes to working with data, it’s something that can be discussed into infinity – and with a definite degree of clarity.

DDD stands for Data driven decision making

What Is DDD

DDD is defined as the practice of making decisions based on the analysis of various data, and not just on intuition. To accept DDD as the starting point for making every decision is not enough. Just having the right data isn’t enough either, as you need to be able to work with it and analyze increasingly large numbers to make smart conclusions for the present moment in business.

The benefits of embracing DDD are numerous and include higher productivity, higher return on assets, return on equity, asset utilization, risk minimization and market value.

DDD used to reduce the amount of investment at risk

When launching new projects, such as a new shopping center, the whole area should be scanned to predict potential effectiveness in different regions according to various success criteria, metrics and parameters. The effectiveness of the opening becomes the central part of the business plan. Short term and long term criteria are considered. Data from different sources can predict target audience population, consumer buying capacity, store capacity, number of potential employees, etc. ROI timeframe is also considered: if it takes more than five years for the investment to pay off, the location will be canceled. The initial plan may have included opening 20 shops but as a result of thorough data analysis, only 10 will be opened, but the risks of them underperforming will be much lower.

Data is not always easy to work with for a variety of reasons. Here are five challenges of DDD:

1. Volume

The sheer number of data is so big that in the recent years the term big data is a buzzword that appears in all seminal works on data. An average company deals with hundreds of thousands transactions a day, and this data needs to be stored and analyzed correctly to be of any use.

2. Quality

Not all data that glitters is gold. Data has to be exact, correct and uniform in order to be the yardstick to measure the business potency of this or that decision.

3. Sources

Data sources must be trustworthy. One example that hits home is doing taxes: if incorrect data is used in the process, and it is discovered, problems ensue, and not just in the form of a fine.

4. Skill

Skill is needed to use data to gain competitive advantage. This entails utilizing the correct tools and calling on professional help to harness the power of data.

5. Expiration date

Data is not wine. It doesn’t get better with time. Of course, there are certain cases where old data may be helpful but usually the faster you get data, the better it is for decision-making.

Every industry needs to learn to benefit from effective DDD but in some industries using it becomes more critical than in others.