When you work in the IT industry you often realize that a big challenge of getting to grip with the industry is learning the memes and buzzwords. One of the difficulties I came across recently was understanding the difference between Business Analytics and Data Science.
I won’t define data science here – primarily because it has no formal definition. Yet I will describe some of the differences that came up when I reached out to some luminaries on Social Media.
I asked the question what is the difference between Business Analytics and Data Science, these are some of the answers I got.
@andrew_clegg @Springcoil more fundamentally: testing hypotheses in controlled experiments (a/b etc) rather than just making changes and reporting results @Springcoil reporting vs predicting/recommending/explaining maybe?
Andrew Clegg is a Director of Data Science at Pearson Education, and has academic experience in Natural Language Processing and a strong Engineering background. He clearly still thinks somewhat like a scientist, and this is obvious from his own blog and his own presentations. I think sometimes though people bash ‘reporting’ because it has become such a bane of our professional existence. I live in the Financial Centre that is Luxembourg, and regularly hear my friends complain about the ‘reports’ they have to produce for regulatory compliance. This can be very descriptive work – but I still think that a good ‘data product’ can be in report form.
@Springcoil context :-) age and industry of practitioner, start-up vs. enterprise, etc. most of the time it’s a semantic difference
John loves to cut through the bullshit – he has experience as an Analytics Consultant and now leads a team at Mailchimp. If I were willing to move to Atlanta I would probably pester him for a job interview :p I think he has a good point here about the semantic difference, at Amazon I met and worked with business analysts who were effectively doing data science, building predictive models of the changes in price elasticity of certain goods. That is not to say that sometimes they had to build reports and that a lot of their day to day work was ad-hoc analysis – which often means ad-hoc scripting to count something.
@Springcoil very similar. Typically biz analytics don’t build models. But they should. Too often biz analytics is report & slice/dice data
And the wonderful JD Long – who I interviewed for this blog before @ Data Artisan interview – gives a good point about the fact that Biz Analytics should often build models rather than just report and slice/dice data. I think this is personally where the scientific aspect comes in – and this is a corollary of one of Andrew Cleggs comments above.
I am often surprised by how decisions are made in business – one of the best parts of being a consultant is learning how important culture is. I hear sometimes the job title ‘data strategy consultant’ which strikes me as a data scientist working with companies to create a data-driven culture. Culture is a hard thing to change in an organization even if you are a member of the C-class. I will probably comment on that when I am old and wise enough to understand it.
So what do you do if you want to work on your predictive modelling skills?
Well I can recommend one good lecture notes or pdf and one good book.
Both are good hands-on introductions to the applied part of Data Science.
Which is thankfully more exciting than mere reporting.
P.s Although sometimes data scientists have to build the infrastructure themselves – I think that a data scientist should be capable of building a data mart for themselves, since one often doesn’t have the software engineering support to do that.