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.


Data driven decision making (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.

Example: 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.

Example: DDD used to anticipate product demand
In one famous case, Wal-Mart decided to keep their store open during a hurricane. The execs decided to analyze data on customer behavior from all previous hurricanes to establish unusual local buying patterns. As a result, product demand turned out to be somewhat unusual:  instead of expected water bottles and flashlights, top two products were beer and pop tarts.

In sales data can be used to predict the success of new products and analyze the business’s current position on the market. Sales managers and top execs may look at reasons behind the patterns and trends they detect. Based on data, it is easy to see what effort worked and what failed.

Top management of almost any business can use DDD to build models and define trends, make predictions and look into failures in more detail.

Example: DDD used to avoid product cannibalism
In the sphere of mobile providers, data can be used to gage the potential effectiveness of launching a new product. But once the product is launched and starts working, there is a potential problem of product cannibalism, a process during which the sales of the newly launched product negatively affect the already existing line of products, even causing some of them to become unprofitable. Data can be used to eliminate this possibility. So when planning the launch of new mobile products, data allows to predict how one product can influence the overall profitability of the mobile provider.

The future of the company, especially its profitability and longevity, depends on how skillfully data is processed to make intelligent decisions. Interpreting data correctly transforms it into valuable information that can be used in business. In turn, information, used together with intuition and former experience, becomes knowledge – something that can differentiate one business from the next, and bring it to the forefront of success in its respective niche.