As a data scientist, I have helped business analysts write Oracle queries in batch mode run 20 times faster via Perl/Python scripts: instead of waiting for hours for Brio or Toad to return results in a browser (with a crash if the number of rows being returned was above 50,000), it took only minutes on a Unix station: I also spent 30 minutes providing basic Unix training to analysts so they could run and fix my Perl scripts alone. It was a win-win-win: for the business analyst, for executive management, and for myself.

One thing that helped a lot is the fact that my scripts would accept (as input) a SQL query (text file - something business analysts could easily and quickly produce with Brio or Toad and then export to the Unix station) and produced as output the results of the query (tab-separated text file) and another file about the success/failure of the query (with explanations as why the query failed, if it did, to help fix bugs in the SQL code). This made the life of the business analyst very easy. I also taught them about 20 important Unix/FTP commands that could solve all their Unix needs. 

I have been deeply involved (as a data scientist) in helping design efficient dashboards as well as selecting which metrics or KPI's should be tracked in database systems, including data modeling. Currently many people believe this is the job of a business analyst or executive, but don't you think that data scientists should be deeply involved in data modeling, vendor selection and everything related to data harvesting and storing?

What do you think?

Anyway, feel free to download the magic code to boost BI analysts work by a factor 20.

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I completely agree.   There's the IT-side that does what the business tells them and the business-side that does not really know how to translate business into data.  The data scientist / data analyst is the middle-person that understands how to turn data into value and work/communicate on both sides.

I think their relation is like data base architect and network architect. While their work overlaps, they are specialist in their own area and probaly won't enjoy doing everything else the other one is doing.

A Business Analyst is more of a functional role.  Whereas a Data Scientist is more like “The-All-Seeing-Eye”. The Data Scientist will always follow/create/verify the logic that is necessary.  The Data Scientist will clarify and authenticate the requests from the Business Analyst.   Not all Data Scientist will function in this role because this is not their main focus.   The Data Scientist will be more heavily concentrated in the integrity of the structure, processing, speed of delivery. So, this is just my opinion, I think they will be more focused with the system side – machine learning, database- DBAs, data warehousing, and maybe a little help with the application developers.  However, a data scientist is not a application developer or a frontend BI designer.    Data Scientist will be the master mind of answering the question, "How do we put all this data together in a meaningful way so we can examine it”? They create the matrices.


It seems to me that a BA's job is not to get up close with the technology but rather, for example, to identify which operations need to be accelerated or what business problems need to be adressed. Data architects aid in the structuring of data. Isn't data science, at least where it is today, mostly aposteriori? After all, data science has existed for a long time, that is why SAS is so rich. I remember them from before they entered businesses ans were competing with SPSS.

Open to learning,

Whether business people see the merit of ds in their companies is their choice. However, with the intense competition (esp. in this suboptimal economic climate) I don't think the mindset with walk. The future IMO belongs to the business people who are insightful enough to see value in hiring ds in their ranks, while everyone else will be weeded out by natural selection (just like the social media before linkedin and facebook gradually died when the latter appeared on the web).


I was surfing the web for my research on a data problem, happen to found this ...very few of the companies apply data science starting with a true definition of business problem and solving using data science techniques.. this looks  promising ...do check




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