Maximizing the Impact of Data Science Using the Scientific Method

We live in a Big Data world where everything is being quantified. As a result, businesses are trying to make sense of their ever-expanding, diverse, streaming data sources to drive their business forward. If your competitors have access to the same type of data (CRM, ERP, weather, etc.) that you do, how can you keep ahead of them? One way is to get better insights from your data. They can accomplish this task through the use of data science.

Gil Press offers an excellent summary of the field of data science. According to Press, the term, data science, first appears in use in 1974. He concludes that data science is way of extracting insights from data using the powers of computer science and statistics applied to data from a specific field of study. Additionally, in our research, we found that, data science skills can be grouped into three skills areas:

To improve your chances of finding insights from your Big Data projects, you’ll need to apply all three of these skills. But how do these skills contribute to our understanding of the phenomenon we are studying? We turn to the scientific method.

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The scientific method is body of techniques for objectively investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. This method includes the collection of empirical evidence, subject to specific principles of reasoning. The scientific method follows these five general steps:

The application of the scientific method helps us be honest with ourselves and minimizes the chances of us arriving at the wrong conclusion. By following where the data lead us, this method helps us understand how the world really works. Through trial and error, the scientific method helps us uncover the reasons why variables are related to each other and the underlying processes that drive the observed relationships.

When we cross the three data science skills with the five steps of the scientific method (see Figure 1), we see how businesses can harness the power of the three data science skills in the context of the scientific method. I posit that two of the skill areas, business (subject matter expertise) and statistics/math, play a bigger role throughout the scientific process.;

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