How Data Science May Evolve in the Future

How Data Science May Evolve in the Future

Mark Cunningham joined Infostrux CEO, Goran Kimovski (Kima) for a live fireside chat. During the discussion, they covered a wide array of topics including the history of Business Intelligence, the Data Analytics industry, and technology trends that shaped and will continue to shape the industry.

Kima: What’s your view on the term ‘data science’? Do you feel that it’s just a transitionary thing and it will eventually evolve into something else in the future, or is it here to stay? Will it be a valuable set of skills that every company needs to acquire?

Mark: I used to be resistant to the term, but I’ve since changed my stance. These titles used to be called business analysts, then it evolved into data science. The technologies were evolving so the complexity was evolving. There was more sophisticated analysis work being done with more sophisticated technology. So this new name update of ‘science’ came up because it felt like they were doing a lot more than a business analyst was doing. 

The concept of the data engineer and the data scientists can work together. The classic analogy is that data engineers are the plumbers of the system, pulling the data through and building piping in order to get the data. Data engineers are creating more access to the data so that the data scientists can make use of it in their reporting or dashboards. 

From a compensation perspective, these terms matter. It has been really beneficial to people who have positioned themselves effectively in the data science world because the compensation for those roles is significantly higher than that of a business analyst role. But in a lot of cases, when the rubber hits the road, the business analysts and the data scientists are probably doing pretty similar things.

I’m not taking a knock on data scientists at all. I think there are very sophisticated, brilliant, and true data scientists out there. But in the typical enterprise, there is some basic fundamental analytic work that needs to be done that is probably close to what a business analyst can do and maybe what a data scientist is doing.

Kima: Some of the problem is that there is an expectation to have more technical experience with the data scientists. Based on our interviews with customers, data scientists often get asked to do the engineering work, but they don’t have the software engineering or the SQL experience to deal with those tasks. So that is part of the challenge. 

There are externalities to everything, there are trade offs to everything, so the fact that they are being paid more means that there is less budget to hire a data engineer, which means they’re being asked to do data engineering work. A business analyst is typically not being asked to do the engineering work because there’s no expectation for that technical expertise. 

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