Five Key Steps to Keep Data-Driven Decision-Making Simple

Before one can use data to inform and drive decision-making, there must be viable, reliable, and sustainable process that extracts data from the system(s) from which it lives. As the Rubik’s Cube graphic represents, to support informed decision-making, data extracted from the operational environment needs to be organized into useable form to create wisdom and intelligence that feeds a leader's decision-making cycle. The more we enrich data with meaning and context, the more knowledge and insights we get out of it to support better and more informed decisions.

There is a significant amount of published content related to “data driven decision making”.??A concise and well written summary on this topic can be found at the?Northeastern University blog.??The article describes the five key steps to data-driven decision making.??The author asks, “what does it mean to be data-driven?” Perhaps one of the most common buzzwords today is “big data.” But what is “big data,” really? The term is generally used to describe the magnitude and complexity of information. Even a small amount of content could be considered “big data” if a large amount of information has been extracted from it.

Furthermore, 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 is the process of making organizational decisions based on actual data rather than intuition or observation alone.??The process doesn’t need to be complicated.??Keep it simple and follow these five universal steps to make data-driven decisions:

1. Know your “WHY”

A well-rounded data analyst knows the business well and posses sharp organizational acumen. Before you begin collecting data, you should start by identifying the business questions that you want to answer to achieve your organizational goals.?

2. Identify data sources

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

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.?

4. Perform statistical analysis

Once you’ve thoroughly cleaned the data, you can begin to analyze the information using statistical models. Testing different models such as linear regressions, decision trees, random forest modeling, and others can help you determine which method is best suited to your data set.

5. Draw conclusions

The last step in data-driven decision-making is coming to a conclusion. Ask yourself, “What new information did you learn from the collection of statistics?” Despite pressure to discover something entirely new, a great place to start is by asking yourself questions to which you already know—or think you know—the answer.

The majority of the steps listed above do not generate statistics. Rather, this process helps professionals become capable of not only analyzing, but understanding data from a holistic perspective and providing insight based upon the data.??In its simplest form, all of the above results in?the transformation of data into wisdom that informs an organization’s allocation and commitment of its time, money, and workforce.??