What is “Data Science”?

My original understanding of what data science was “the study and manipulation of data.” I never really took into account the purpose of the study. I also assumed that a data scientist would leverage data analytics to dig up some new information for the business from a previously untapped resource. I have learned that is not quite the case. The goal of a data scientist is to use data to create an application for it to generate more data.

“A data application acquires its value from the data itself, and creates more data as a result. It’s not just an application with data; it’s a data product. Data science enables the creation of data products.” – Mike Loukides

Data science has to add some sort of tangible value to the business. There is a specific business defined focus in the direction of concentration for analytics. Data Science is the practice of fully utilizing data to its potential: to first effectively collect, store, and secure it, then apply tools and techniques to process, analyze and extract value from it, then communicate the meaning of that knowledge for strategic application to future decisions and directions.

This makes sense but I had never thought through it deep enough to define the driving force behind the analytics. Now I understand the role of a Data Scientist is to solve business challenges by applying the appropriate analytic techniques and tools to analyze data. In the end they should be able to tell a compelling story with the data to drive business action.

A great example of this is Thomas Davenport’s brief video on “How Mangers Should Use Data.” I like how he simplified solving problems with analytics into 3 main stages: frame the problem, solve the problem, present results and take action. The steps sound obvious at first but a lot of the time people fail to follow through. I can think of a number of personal examples from work where management has started a project that was driven from statistics and reports they have gathered but failed to execute on the plan. A lot of time we think the hardest part of getting value from data is the analytics but all the analytics in the world will not add value if nothing is done about the findings.

Another good example is of Hans Rosling’s TED Talk, where he showed the power of Analytics. His team was able to take pre-existing data sets and in a 20 minute presentation, change peoples perceive on the “Western World vs Third World” on matters like child mortality, population growth, revenue, growth, etc. He gathered hundreds of data points and showed how they changed over the years. One of the most impressive things I saw was his story telling as the graphs changed year over year. He was able to use an analytical approach to address a read world problem on how people perceive the rest of the world and do it in a compelling way to push support for what his team is doing. I have never seen such an effective message that was so clearly built on raw data. I learned how data can be manipulated and compiled to create knowledge and drive results.

I definitely recommend watching both those videos. Data Science is a relatively new field but it growing fast. I believe all of us technologist’s have a huge opportunity ahead of us in this new world of data science and advanced analytics.



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