3 reasons why data science can fail

The rise of data science in the last decade has been driven by the ease of access to deep data and significant reductions in the costs associated with processing it. These days anyone with a credit card can now setup a cloud-based data warehouse and tracking system within minutes, but achieving a return on this investment is not so straightforward. This is not to say that effective use of data science can’t be very profitable, just that it is not always guaranteed. There are three key reasons why data science projects can potentially fail: 1.Solving the wrong problem Most data science applications are about optimization, i.e. let’s take a product and make it better, faster and easier using data. Ideally you could take the product as a whole and optimize…

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