Data Science Foundations: Defining Primary Data Types

Data Science is more than a buzzword, it’s a profitable practice that needs to be a part of every company’s retention strategy.

So much data is collected nowadays and if you aren’t analyzing and operationalizing it, you’re missing out on a goldmine. I’ve worked with hundreds of companies and you’d be surprised just how clear the pattern for success can be once you properly use your data. This blog post is the start of a broader series that will walk you through the latests ways to think about foundational Data Science concepts. You’ll get a blend of advanced topics, tactical best practices, and a few pop culture references here and there. To start, I’m taking you on a journey through the data universe to explore primary sources of data.

What is Data Science?

Data Science helps companies distill meaning from their data and operationalize it so they can build better processes. Modern companies use so many technology solutions, data becomes disparate and difficult to compile together to create a hyperdimensional view. At Gainsight, we call this hyperdimensional view a 360-degree view of the customer. Vast data sets like this are aptly called “big data.” It takes an enormous amount of effort to derive insights from them—that’s where Data Science comes in. Data Scientists use statistical tools, algorithms, and machine-learning models to organize and understand big data. Data Science is one of the most powerful tools a company can leverage and directly affects their ability to be predictive and proactive.

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I will be looking to share my insights learned after working with over 120+ companies in the last 5 years. If you would like to have me blog about, Data Science, Career Planning, Business Analytics, Project Management, Customer Success or anything else let me know in my inbox. Feel free to reach out anytime.

Best,

Sam