Business Analytics versus Data Science

Standard

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@andrew_clegg  more fundamentally: testing hypotheses in controlled experiments (a/b etc) rather than just making changes and reporting results 

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. 

 

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. 

17h

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. 

Applied Data Science Notes

Applied Predictive Modelling

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. 

Advertisements

Business Analytics versus Data Science

Standard

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@andrew_clegg  more fundamentally: testing hypotheses in controlled experiments (a/b etc) rather than just making changes and reporting results 

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. 

 

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. 

17h

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. 

Applied Data Science Notes

Applied Predictive Modelling

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. 

On the Education debate

Standard

Education reform is a politically sensitive issue.
Yet a few articles I came across recently made me think about the issue.

“As descriptions, both arguments—accountability and autonomy—contain a measure of truth. Teachers do lack some of the freedom they need to teach well, and they also lack adequate feedback. But as prescriptions, actual suggestions for how to improve teaching, the arguments fail. Neither change, on its own, will produce better teachers. Basic math makes the problem with accountability clear: Discard the bottom 10 percent and, as Obama said, that’s thirty thousand teachers who will need to be replaced. And that’s just in California. Nationally, the number is more than ten times that. Autonomy, meanwhile, is an experiment that many schools have tried for years, and still seen teachers struggle.”

Another article that talks about these ‘free market reflexes’ is the following http://baselinescenario.com/2013/12/11/free-market-reflexes/#more-10763

“That just doesn’t follow. And anyone who’s worked in an actual company should realize that. Yes, it’s always better to have better workers. One way to get better workers is to hire more effective people and to fire less effective people. But the other way—which, in most industries, is by far more important—is to make your current workforce more effective. You do that in part by figuring out what attributes or processes make people more effective, and in part by training people and implementing processes in ways that improve productivity.”

I think the myth of the ‘naturally born teacher’ leads to such logical absurdities as those above. Talent and skill need cultivation, and we rarely hear the need for such improvements in Education. In software engineering – and I work for a company in that sector – there is feedback from other experts via say ‘code review’. But how much feedback is given to new teachers from their peers. Feedback from exam results is not necessarily correlated with good teaching skill.