How Data Scientists Extract Value from Data [Infographic]

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Building a successful data science program in your company relies on a good understanding of the data professionals who extract insights and value from data and how they do it. Toward that goal, we asked over 600 data scientists about their skills, jobs, colleagues and more. We have summarized the results of this ongoing study in several blog posts (see here, here and here for a few popular posts).

Today, I created an infographic that summarizes some of the major findings of that study. If you're not into infographics, I also created a slideshow in which you can see the content of the infographic page by page. They appear at the bottom of this post.The infographic and slideshow present the following four insights about data scientists and the practice of data science:

1. There are a handful of different skills that make up the field of data science. While we measured five distinct skill types, a factor analysis of proficiency ratings of these five skills resulted in three distinct skill types:

1. Business (subject matter expertise)
2. Technology
3. Statistics

2. Different kinds of data scientists possess different data skills. Data scientists fall into four general types: 1) Business Managers (e.g., leaders, business persons and entrepreneurs); 2) Developers (e.g., developers and engineers); 3) Creatives (e.g., Jack of all trades, artists and hackers) and 4) Researchers (e.g., scientists and statisticians). "Researchers" is the most popular data science job role.

Data professionals have different strengths. Business Managers understand business (i.e., subject matter) but not technology, programming or statistics. Developers understand technology and programming but not business and statistics. Creatives possess a basic knowledge in the three skill areas and Researchers are strong in statistics and math but weak in business and programming.

3. Larger, more diverse data science teams perform better. Data professionals who worked with other data professionals who had complementary skills were more satisfied with their work than when they did not work with another data professional. Researchers and Developers report better outcomes of analytics projects when they work with Business experts. Additionally, Business Managers report better outcomes of analytics projects when they work with Statistics experts. Data scientists who work with 4 or more people on their analytics projects report better outcomes compared to their counterparts who work with fewer people.

4. The scientific method is an effective way to get insights from your data. Scientists have been getting insight from data for centuries using the scientific method. Formally defined, the scientific method is a body of techniques for objectively investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. The steps of the scientific method are:

1. Formulate a question or problem statement.
2. Generate a hypothesis that is testable.
3. Gather/Generate data through experimentation or observation.
4. Analyze data/Draw conclusions
   (e.g., test hypothesis).
5. Communicate results /Take action (e.g., change processes) based on the conclusions. Refine hypotheses for further testing.

Each type of data scientist brings her unique skills to bear on the scientific method. The scientific method helps data scientists organize their collective efforts to extract insight from data.

You can see the slideshow and infographic below.