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Machine Learning Times
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CONTINUE READING: Access the complete article in Customer Think, where it was originally published.  

6 years ago
Getting More Value from Data: 6 Facts About Data Science


The value of data is measured by what you do with it, and organizations are relying on data scientists to extract that value. I recently conducted a survey of data professionals to better understand what it means to be a data scientist. I discovered a few things in this study that can help organizations optimize the value of their data. While I wrote about these findings in prior posts, I want to summarize the major points here, in a more concise way.

Facts about Data Science

While some of these points below seem rather mundane or obvious, it’s important to note that these ideas are no longer only opinions; they are backed up by empirical data. This is how data science really works.

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
  2. Technology / Programming
  3. Statistics / Math

2. There are different kinds of data scientists. Our study identified four distinct job roles among these data professionals :

  1. Developer (e.g., developer, engineer)
  2. Researcher (e.g., researcher, scientist, statistician)
  3. Creative (e.g., Jack of all trades, artist, hacker)
  4. Business Management (e.g., leader, business person, entrepreneur)

Respondents were asked to select which of the job roles best described their work. They could choose one or any combination of job roles. The correlation across job roles (1 = selected; 0 = not selected) was quite low (average r was -.07; highest r was -.30), suggesting that these four job roles are distinct from each other.

3. Different job roles require different skill sets. Data professionals in different job roles have different skill sets . Not surprisingly, data professionals who identified as Developers reported the highest levels of proficiency in Technology and Programming skills compared to their counterparts. Additionally, Researchers reported the highest levels of proficiency in Statistics and Math while data professionals who identified as Business Management reported the highest levels of proficiency in Business. Finally, data professionals who identified as Creative reported moderate ratings across all skill sets, suggesting they are indeed jack-of-all-trades.

4. Finding a data professional who is proficient in all data science skill areas is extremely difficult. Data professionals rarely possess proficiency in all five skill areas at the level needed to be successful at work. In fact, the chance of finding a data professional with expert skills in all five data science skills is akin to finding a unicorn; they just don’t exist.

5. A team approach is an an effective way of approaching data science projects.

CONTINUE READING: Access the complete article in Customer Think, where it was originally published

Bob E. Hayes, PhD is the Chief Research Officer at AnalyticsWeek and president of Business Over Broadway. He calls himself a scientist, analyst, blogger and author on customer experience management (CEM) and analytics (Beyond the Ultimate Question and Measuring Customer Satisfaction and Loyalty).

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