Data-Driven Decision Making: A Primer for Beginners

In the field of data analytics, there are several buzzwords that, while important, are poorly defined because of their complexity. These terms, such as “big data,” “cloud computing” and “data-driven,” can seem obscure to laymen. One key to success in a data analysis career however, is to establish a firm knowledge base by clearly defining these terms early on.

Learning the language of data analysis will enhance your understanding and empower you to use this knowledge to your advantage. Once you understand the definition of the phrase “data-driven,” you can start applying the act to your decision-making and career as a data analyst.

What Does It Mean to be “Data-Driven”?

Then what does it mean to be “data-driven?” This term describes a 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.

Data-driven decision making (or DDDM) is the process of making organizational decisions based on actual data rather than intuition or observation alone.

Every industry today aims to be data-driven. No company, group, or organization says, “Let’s not use the data; our intuition alone will lead to solid decisions.” Most professionals understand that — without data — bias and false assumptions (among other issues) can cloud judgment and lead to poor decision making. And yet, in a recent survey, 58 percent of respondents said that their companies base at least half of their regular business decisions on gut feel or intuition instead of data.

How, then, can you ensure you’re making data-driven decisions that are void of bias and focused on clear questions that empower your organization?

How to Make Data-Driven Decisions

1. Know your mission.

Before you begin collecting data, you should start by identifying the business questions that you want to answer to achieve your organizational goals. By determining the precise questions you need to know to inform your strategy, you’ll be able to streamline the data collection process and avoid wasting resources.

2. Identify data sources.

Coordinating your various sources seems simple, but finding common variables among each data set 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 also be used for additional projects in the future. If so, you should strive to develop a strategy to present the data in a way that’s accessible in other scenarios as well.

3. Clean and organize data.

The term “data cleaning” refers to the process of preparing raw data for analysis by removing or correcting data that is incorrect, incomplete, or irrelevant. To do so, start by building tables to organize and catalog what you’ve found. Create a data dictionary — a table that catalogs each of your variables and translates them into what they mean to you in the context of this particular project. This information could include data type and other processing factors, as well.

4. Perform statistical analysis.

Here, you will also need to decide how to present the information in order to answer the question at hand. There are three different ways to demonstrate 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 your reasoning.

Clarifying how the information will be most effectively presented will help you remain organized when it comes time to interpret the data.

5. Draw conclusions.

Many companies make frequent assumptions about their products or market. For example, they might believe, “A market for this product exists,” or, “This is what our customers want.” But before seeking out new information, first put existing assumptions to the test. Proving these assumptions are correct will give you a foundation to work from. Alternatively, disproving these assumptions will allow you to eliminate any false claims that have, perhaps unknowingly, been negatively impacting your company. Keep in mind that an exceptional data-driven decision usually generates more questions than answers.

The conclusions drawn from your analysis will ultimately help your organization make more informed decisions and drive strategy moving forward. It is important to remember, though, that these findings can be virtually useless if they are not presented effectively. Thus, data analysts must become skilled in the art of data storytelling to communicate their findings with key stakeholders as effectively as possible.

Example :

Consider Netflix. The 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. Moreover, without that decision, the company would not have flourished at the rate or in the direction it did.

Amazon is another poignant 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. What drove them to make such enormous decisions? Data. It’s no surprise that such major (and successful) rebranding moves were made based on data collection and the inferences made as a result.

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 methods out of business. The world is becoming data-driven, and to not make data-driven decisions would be foolish.

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