Yariv asks, "What are the differences between data analytics, data science, business analytics, and business intelligence?"
It depends on who you ask. Thanks to consulting and marketing, these terms all have varying degrees of overlaps, and companies which make their money on clarifying terms like this - consulting shops - often have conflicting definitions. You'll get a different answer, and sometimes an opposing answer, if you ask a McKinsey consultant and then ask a Deloitte consultant.
Based on a roundup of existing literature, we classify these terms based on whether they are past or future looking, and how technical or non-technical the various tools and processes are. To be clear, a business needs all four; this isn't an aspirational contest to reach one magic corner of the diagram.
Watch the video for the full explanation and nuances among the different categories.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
On today's you ask I answer your reef. I hope that's how you pronounce your name asks, What are the differences between data analytics data science, business analytics and business intelligence?
Well, depends on who you ask.
Thanks to marketing,
sorry, and consulting. These terms all have varying degrees of overlap. And
companies that make their money on
on perspectives and positions on terms like these mostly consulting shops often have conflicting or oppositional answers, you'll get a different answer. For example, She asked McKinsey consultant for their definitions, and then you go ask a Deloitte Consulting or Forrester consultant, you're going to get totally different answers. Even
within those organizations, depending on which
division you're talking to, you may get very different answers. So
based on a roundup of existing literature, you can classify these four terms, data analytics, business, analytics, data, science, business intelligence, based on whether they're their past, or future looking and how technical or non technical they are. So let's, let's look at a diagram of these, put this up here, and,
and walk through this. So
on the bottom of the diagram below, we see the types of analytics we're doing the types of analysis, are we doing descriptive, which is what happened, what's, what's in the data? What does the data tell us and diagnostic to the extent that you can, why did something happen? And then on
as you move further to the right on the diagram, you see predictive
and prescriptive analytics. This is
you build a model the forecast the future
and can you build a model that tells you what to do on the vertical axis, we have less or more technical, so less technical tools and processes for business users, people who are not analysts or data scientists by profession, they just your average executive or line of business user, and they want to log into a friendly interface and get some answers.
And then as you go up
the the chart, it is
more technical users. So analysts, statisticians, data scientists, programmers, developers,
machine learning and AI specialists. So this is how we classify the terms business intelligence tools that are descriptive and diagnostic and nature they're less technical. Really good example of this would be something like IBM Watson Analytics or Tablo software that lets you just go in and and very quickly just organize your data and go Okay, what am I looking at what happened in here on the predictive and prescriptive side, this is where you see sort of like some of the things like big Watson, being able to just kind of come up with an answer is for you, or modeling software modeling tools, nine alter x companies like that, that allow you to assemble drag and drop workflows with your data to try and make the day to do stuff that visualization tool by itself would not do
the more technical side, data analytics, descriptive and diagnostics, hardcore statistics, software, SAS, SPSS would be a good examples of these are. And then in the data science realm, that's where you, that's where you see schools like our like Python, TensorFlow Charisse, all those the data science, machine learning and AI tools. So when we're trying to figure out
organizationally, what capabilities we have and what capabilities we need, this is a way to, to explore these four areas.
Now, it's really important, I think it's worth mentioning that
with these with
this diagram, it
a lot of times a two by two matrix, these people say, like I, I just need to go from here or here, all the way up into the upper right hand corner, or that depending on which consulting firm you're talking just the upper left hand corner,
that's not the case,
with this sort of data
you need all four,
you need the ability to understand the past,
descriptive and diagnostic. And you need the ability to predict the future and to
to prescribe based on what your your data says, You also need the ability to have these capabilities available to business users, people who just
want to get their jobs done,
and to the hardcore, technical statistical math folks who need the tools to build
Now, what tends to happen organizationally,
is that the sore top row here of the hardcore stuff is the back end or the fuel for what eventually becomes the business users. So the ability to take those those key findings at a tactical level, and distill them down into the the business level that said, the business level then sort of feeds back and informs
the technical stuff to help refine models, or to construct a new software or new templates, or whatever the case may be, in order to, to tune the findings to be more effective. So that's this sort of spectrum of these four terms. Data Analytics, data science, business intelligence, business analytics. And again, it's super confusing
because of marketing. I mean,
you would think that data analytics and,
and business analytics would be in the same category of like, descriptive diagnostic, but they're actually different in the sense that what, you know, data analytics looks tends to look back at words at past data and business analytics, at least in a lot of the way organizations describe it
is very predictive and forward looking.
Again, this is all marketing and, and people trying to mistake a thought leadership position by
whatever you call these in your organization, and you may end up developing your own lexicon, you just want to be able to do all four things you want people with technical looking back abilities to will, technical looking forward abilities, business users, giving them the ability to look backwards easily, and the ability to look forwards easily. That's what you want within your organization. If you can do that, you create those capabilities, then whatever you call, it doesn't matter. You just need to have all four. Like I said, it's not a contest, you're not trying to go from here, you know, trying to all get into the magic corner, you need to have a good balance of all four capabilities
to really make the most of it. So great question. You're you've
if you google this, you're going to get 400 different answers from like 200 people.
And even if you were to ask
a person on any any given day, what their answer is, the answer may change. So just know that it's going to be confusing. Focus on the capabilities as opposed to the brand labels that go on them. As always, please subscribe to the YouTube channel into the newsletter and I'll talk to you soon. Take care
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