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Data Science has to be Business First!

The current focus on execution in the field of data science is completely wrong.

One of the big mistakes is to start with execution and then think about how you can make a business out of it.

"Okay here is some data, here is a data scientist and now we want to figure out how to make a business out of this data." - a common way of thinking, but exactly the wrong way. It doesn't work that way.

The core and the first thing has to be the business

The idea is that you look at the business - end to end.

The questions you have to ask yourself are:

  • How does it work?

  • How do I start?

  • What can I deliver?

  • What does the customer want?

  • How do I get to the execution?

  • What is the business?

So you have a business idea and a strategy how to develop your business. And only then you go into the execution. Because then you know how to look and what Data you need. The data scientists can help you in the finding phase.

Sometimes solutions can also come from a group of solutions. That means a new business is created from an existing business. Or a solution for one business case creates another product. That makes absolute sense! Because then you start again from the product side, the business side. You have a solution for something and then you say you can make some more money with this other product and then you do the execution to actually do it. But then it also comes back from the business side.

So it doesn't make sense to hire data scientists and engineers and collect data and build platforms, because they will find the business out of the data.

What are your experiences in this respect? Let me know in the comments!

>> created by Mira Roth


Check out my full video on YouTube!

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This is such an important post- thank-you for sharing. The world of data science is evolving fast and it is being increasingly changed by the needs of businesses for better data and better models. The more the business world understands the value of data, the more business orientated data science will need to become. I don't think this is a bad thing, it is just a change, and it is a change that will actually help ensure data science remains relevant in a shifting landscape. I have another great resource (link here with and I would love recommendations for other similar reads.

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