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Strong collaboration between data scientists and subject-matter experts
(SMEs) on data is essential for building an infrastructure of capturing
data for rapid, accurate decisions. It’s important to understand the
role of a data scientist in order to build your data science “dream
team.”, continues Forbes.
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| Abstract Digital network communication Photo: Getty |
Mark Twain once said, “Data is like
garbage. You’d better know what you are going to do with it before you
collect it.” This gives data science teams food for thought.
The go-to method in data collection for many teams has been to “collect it all” and sort it out later, though this data strategy brings up several issues for managing, qualifying, and processing data later on.
The solution? Strong collaboration between data scientists and subject-matter experts (SMEs) on the data is essential for building an infrastructure of capturing data for rapid, accurate decisions. But where to start? First, it’s important to understand the role of a data scientist in order to determine the best way to build your data science “dream team.”
What does a data scientist do?
There is much discussion, and often confusion, around the term “data scientist.” In short, the definition of data science is the process of asking questions and getting answers from data. By defining the different roles of data scientists and breaking them into four distinct categories, it may better clarify the different uses of the term data scientist, each with its own focus...
Giving Data Scientists the Tools to Succeed
Keeping these guidelines in mind will help you assemble a team of data scientists to take on your biggest data challenges. And just as racing teams pair highly skilled drivers with the latest technological innovations to achieve increasingly faster results, you should empower data scientists with tools that will help them do their groundbreaking work faster.
Read more...
Source: Forbes
