by Angela Guess
Isaac Roseboom recently wrote in Dataconomy, “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.”
The first reason, according to Roseboom, is 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 for revenue, but this is not always possible, as it requires taking into account all the elements that can influence revenue, and their relationships with each other, thereby factorially increasing the number of permutations that would need to be tested. This is why optimization problems are usually smaller scale i.e. increase consumption via better recommendations, increase conversions with re-targeting, etc. However, this simpler view can lead to large resource dedicated to solving a problem that may have little impact on the overall revenue.”
Photo credit: Flickr/ hannahkrajewski