Data-Driven Decision Making: A Primer for Beginners

Industry Advice Analytics Computing and IT

In data science today, there are several buzzwords that carry tremendous weight but, because of their complexity, often end up being poorly defined. These terms, such as “big data,” “cloud computing,” and “data-driven,” can feel lofty to novices. One key to success in a data analysis career, however, is to set a firm foundation by defining these terms early on.

Understanding the language of data science will allow you build upon it and begin using it to your advantage. Once you’ve mastered the definition of “data-driven,” you can start applying the act to your decision-making and career as a data scientist.

So, what is “big data?” This term is used to describe the magnitude and/or complexity of information. Even a small amount of content could be considered “big data” if a large amount of information has been collected on that content.


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Then what does it mean to be “data-driven?” This term basically describes the decision-making process, which involves collecting data, extracting patterns and facts from that data, and utilizing those facts to make inferences that influence decision-making.

Every industry today aims to be data-driven. No company, group, or organization actually says, “Let’s not use the data; our intuition alone will lead to solid decisions.” Intuition without data can be clouded by bias or false assumptions and can lead to poor decision making. How, then, do we ensure that we are making data-driven decisions that are void of bias and focused on clear questions that empower organizations?

Data-Driven Decision Making

Figure 1. A timeline for making data-driven decisions.

In order to effectively utilize data, you need to be able to do a few things:

1) Have Organizational Acumen

A well-rounded data analyst knows the business well. Ask yourself what the problems are in the market with which you are working. Identify and understand them thoroughly. This will equip you to make better inferences with your data later on.

2) Identify Data Sources

Put together the sources from which you’ll be extracting your data. You’ll be coordinating information from different databases, web-driven feedback forms, and social media.

Coordinating your various sources seems simple, but finding common variables between each dataset can present a tremendously difficult problem. It can be easy to settle for the immediate goal of utilizing the data for your current purpose alone, but it’s wise to determine whether or not this data could be used for other projects in the future. If so, should you develop a strategy to present the data in a way that’s accessible in other scenarios?

3) Clean Data

Surprisingly, this is actually 85 percent of what you do with your data. Start by building tables to organize and catalog what you’ve found. Create a data dictionary: A table that catalogs each of the variables you are working with and essentially translates them into what they mean to you in the context of this particular project. This includes data types and other processing factors as well.

4) Statistics!

There are three different ways to present your findings:

  • Descriptive Information: Just the facts
  • Inferential Information: The facts, plus an interpretation of what those facts indicate in the context of a particular project
  • Predictive Information: An inference based upon facts, and advice for further action based on the inference

5) Draw Conclusions

What new information did you learn from the collection of statistics? Despite pressure to discover something entirely new, a great place to start is actually asking yourself questions to which you already know—or think you know—the answer.

Many companies find themselves upon assumptions: “A market for this product exists.” Or, “This is what our customers want.” So before finding anything brand new, put these assumptions to the test. Proving these assumptions are correct gives you solid groundwork to move from, and disproving these assumptions will allow you to eliminate any false claims that have, perhaps unknowingly, been negatively impacting your company. Keep in mind that a good data-driven decision actually generates more questions than answers.

Incidentally, the majority of the steps listed above are not actually generating statistics. The majority of these steps to effectively utilize data encourage novice data scientists to become well-rounded—capable of not only analyzing, but understanding the data from a holistic perspective, and providing insight based upon the data as well. This holistic aspect of the analysis is the future.

It is worth asking yourself, “Who isn’t data-driven?” The factor that differentiates data-driven and non-data-driven companies is often success. Companies that are data-driven tend to succeed, and companies that aren’t tend to fail.

Take Netflix, for example. This company started as a mail-based DVD sharing business and, based on a data-driven decision, grew to internet streaming—becoming one of the most successful companies today. Without data, Netflix would not have had the basis to make such an immense and impactful decision. And without that decision, the company would not have flourished at the rate or in the direction it did.

Amazon is another great example. What started as an online bookstore has blossomed into a massive online hub for just about any product a person could want or need. (Amazon has just recently made moves to begin offering food products.) What drove them to make such huge decisions? Data! It’s no surprise that such huge and successful re-branding moves were made based on data collection and inference that indicated this is the direction in which our world is headed.

Without the data-driven approach to decision making, Netflix would still be mailing you an outdated mode of movie content and Amazon would be a simple online bookstore. The bottom line is that this data-driven approach is putting all other approaches out of business. The world is becoming data-driven, and to not make data-driven decisions would be foolish.


This post was written by Joel Schwartz, an instructor for Northeastern’s bootcamp program Level, and Richelle Joseph, a staff writer at Biomedical Data Analytics, Schwozny Inc. 


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