Too many tools not enough carpenters
I agree with the article in broad strokes, but I think it focuses a bit too much on the data science portion. I work for a BI consultancy. There are so many enterprises where the measure of success in data analysis projects is being able to perform arithmetic accurately and in a timely manner.<p>Data science is a pipe dream for many companies currently. Having up-to-date sales figures that can be sliced by dimensions as simple as product hierarchy, customer, and region is a realistic goal for many of our clients.<p>I am not trying to discount data science. We have a data science practice that does some cool things, but it&#x27;s so much smaller than the BI practice, because there&#x27;s currently so much more opportunity for doing basic ETL correctly and simple reporting and dashboarding.<p>There are multiple clients who were looking for &quot;real-time access to data&quot;, which was actually synonymous with &quot;automatic weekly refresh would make our lives so much easier. Right now we have to wait on $overworked_analyst to get this out monthly, and depending on workload that can lag by a week or two after the end of the month.&quot; These are not exact quotes, but entirely accurate paraphrases.<p>This is the reality for so many organizations that I think sometimes falls out of context in an HN-like environment.