3 Data Science Skills Needed to Get Value from Your Data


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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.

Putting Science into 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. In our research, we also found confirmed that three pillars (i.e., skills) support the practice of data science: 1) subject matter expertise, 2) technology/programming and 3) statistics/math. You need these three skills to be successful with any analytics project.

The crux of using data to solve business problems is apparent when you consider how scientists use data to solve their problems using the scientific method. The scientific method is body of techniques for objectively investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. The scientific method includes the collection of empirical evidence, subject to specific principles of reasoning. The scientific method follows these general steps:

  1. Formulate a question or problem statement
  2. Generate a hypothesis that is testable
  3. Gather/Generate data to understand the phenomenon in question. Data can be generated through experimentation; when we can’t conduct true experiments, data are obtained through observations and measurements.
  4. Analyze data to test the hypotheses / Draw conclusions
  5. Communicate results to interested parties or take action (e.g., change processes) based on the conclusions. Additionally, the outcome of the scientific method can help us refine our hypotheses for further testing.

The application of the scientific method helps us be honest with ourselves and minimizes the chances of us arriving at the wrong conclusion, helping us to 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.

Team Approach to Applying Data Science

Because not any single data professional will possess proficiency all of the data science skills, businesses’ best bet to successfully applying data science to solving their problems is to organize a team of data professionals who have complementary skills. We know that data professionals work better with other data professionals who have complementary skills. For example, business-savvy data professionals are happier with their work outcomes when they are paired with researchers who are proficient in statistics. Likewise, these researchers are also happier with their work outcomes when they are paired with business-savvy professionals. Different types of data scientists, with their unique and complementary skills, help address the specific steps of the scientific method.

Figure 1. Steps of the scientific method and the data science skills that support each step
Figure 1. Steps of the scientific method and the data science skills that support each step

Summary and Next Steps

To stay ahead of their competitors, companies can optimize the value of their data by leveraging different data science skills to interrogate a phenomenon of interest. Companies need different types of data professionals to adopt a data science approach to ask and answer their important business questions. To do this, businesses can hire a team of data scientists, identify current employees who have the necessary data science skills, or educate/train employees on different data science.

While current employees might not be formally trained in the practice of data science, I believe that many employees can still bring their skills to bear on data heavy projects with proper training. Over the next several blog posts, I will explore each of the three data science skills more fully. Additionally, I will provide various online resources that businesses can use to help improve the skills of their employees to help them start the journey toward becoming a more data-savvy company, giving them the insights to use their data more effectively.


  1. Excellent, Bob. The whole thing about data and big data is that it’s far too easy to draw the wrong conclusions. I see it all the time from companies that we all know who collect data, and shotgun it.

    When you explore relationships among thousands of variables, you will always get random statistically significant results, just by chance.

    The scientific method is there to prevent this problem.

  2. Spot on, Robert. As a colleague told me, “I’m just trying to put the science in data science.” Be skeptical. Think critically.


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