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Our Industry Terminogy is Degenerated


Hello.

I'm writing an article about our industry terminology and would like the input of you guys.

A very brief introduction just so you know where I'm coming from: I am an electronics engineer and started career in AI/DS in 2009 where I spent ~3 years developing CAPTCHA solvers mainly with LeCun's '98 paper, from there I went to the analytics team of a management consulting firm for 4 years, and now I have with 2 partners my own analytics/AI professional services firm.

So, let me just list a few terms related to our industry:
  • analytics
  • advanced analytics
  • data analytics
  • data science
  • data mining
  • KDD - knowledge discovery in databases
  • big data
  • business analytics
  • business intelligence
  • artificial intelligence
  • machine learning
  • data engineering
  • operational research
  • management sciences
  • statistics
  • applied mathematics
So. What is the difference between data science and analytics? Is business analytics the same as data analytics? Analytics is a short for business analytics or data analytics (I've seen coming from both directions) ? What of business intelligence? How does it differ from analytics? Artificial intelligence and machine learning are the same thing? Does a data scientist have to know operational research (not all OR problems have data, just an objective and some restrictions)? What should I call the corporate department? Analytics Team, AI Team, Data Science Team? The guys that produces the vision algorithms for an autonomous car or AlphaGo, are the same type of guys that develops marketing budget optimization or stock price predictions?

My point is: our industry (call it whatever you like for now, our industry name - you know what it is - is part of the discussion) have way too many intersected terms with too weak conceptual definitions - we math/engineering guys should be the frontline in the defense of linguistic precision! Math is not ambiguous or loosely defined, how come our area came to be ? Blame the marketing and consulting guys! Just kidding. I believe that are 2 main reasons how it came to be that way:
  • The field developed in the academia and industry coming from different standpoints, e.g.: mathematicians/statisticians x computer scientists/engineers; tech guys x MBA guys - and everybody wants their term to survive!
  • Hype, buzzwords and book selling: every time a new term come to market be sure that follows an avalanche of books, webinars, consultants, etc, e.g.: write "agile" or "lean" in a book cover to see the effect - you can put "lean" in front of anything these days: lean cooking, lean showering, lean coaching (the last one actually exists, but is bested by quantum coaching).
I've read at least 10 articles that defines differences between say, AI x ML, and got at least 11 different opinions. We have more conflicting ideas and ambiguity than religion!

Why does this matter? It matters to me, personally, since I need to be very specific and precise in the use of language else I can't think, and matters industry wide, since we need clear and simple definitions so we can communicate effectively with each other - it is the foundation of language, common understanding and definitions, we are degenerating it, soon we well be all communicating like chimps and gorillas.

So, before I elaborate too much on the topic (let's be agilean - agile + lean, I am patenting it), I would like to know the community interest and response to that. Being positive, I would like to propose a group exercise so we can get to the bottom of it, and share my reflections on the terms (hint: I would - and do - kill most of them).

Regards.

1 reply

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Hello.

I'm writing an article about our industry terminology and would like the input of you guys.

A very brief introduction just so you know where I'm coming from: I am an electronics engineer and started career in AI/DS in 2009 where I spent ~3 years developing CAPTCHA solvers mainly with LeCun's '98 paper, from there I went to the analytics team of a management consulting firm for 4 years, and now I have with 2 partners my own analytics/AI professional services firm.

So, let me just list a few terms related to our industry:
  • analytics
  • advanced analytics
  • data analytics
  • data science
  • data mining
  • KDD - knowledge discovery in databases
  • big data
  • business analytics
  • business intelligence
  • artificial intelligence
  • machine learning
  • data engineering
  • operational research
  • management sciences
  • statistics
  • applied mathematics
So. What is the difference between data science and analytics? Is business analytics the same as data analytics? Analytics is a short for business analytics or data analytics (I've seen coming from both directions) ? What of business intelligence? How does it differ from analytics? Artificial intelligence and machine learning are the same thing? Does a data scientist have to know operational research (not all OR problems have data, just an objective and some restrictions)? What should I call the corporate department? Analytics Team, AI Team, Data Science Team? The guys that produces the vision algorithms for an autonomous car or AlphaGo, are the same type of guys that develops marketing budget optimization or stock price predictions?

My point is: our industry (call it whatever you like for now, our industry name - you know what it is - is part of the discussion) have way too many intersected terms with too weak conceptual definitions - we math/engineering guys should be the frontline in the defense of linguistic precision! Math is not ambiguous or loosely defined, how come our area came to be ? Blame the marketing and consulting guys! Just kidding. I believe that are 2 main reasons how it came to be that way:
  • The field developed in the academia and industry coming from different standpoints, e.g.: mathematicians/statisticians x computer scientists/engineers; tech guys x MBA guys - and everybody wants their term to survive!
  • Hype, buzzwords and book selling: every time a new term come to market be sure that follows an avalanche of books, webinars, consultants, etc, e.g.: write "agile" or "lean" in a book cover to see the effect - you can put "lean" in front of anything these days: lean cooking, lean showering, lean coaching (the last one actually exists, but is bested by quantum coaching).
I've read at least 10 articles that defines differences between say, AI x ML, and got at least 11 different opinions. We have more conflicting ideas and ambiguity than religion!

Why does this matter? It matters to me, personally, since I need to be very specific and precise in the use of language else I can't think, and matters industry wide, since we need clear and simple definitions so we can communicate effectively with each other - it is the foundation of language, common understanding and definitions, we are degenerating it, soon we well be all communicating like chimps and gorillas.

So, before I elaborate too much on the topic (let's be agilean - agile + lean, I am patenting it), I would like to know the community interest and response to that. Being positive, I would like to propose a group exercise so we can get to the bottom of it, and share my reflections on the terms (hint: I would - and do - kill most of them).

Regards.

How will you go about this cause all this stuff have thier root in maths..
I would love to follow up on this Thank you

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