Data analytics vs. Data science vs. Business intelligence: what are the key differences/distinctions?

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They are used interchangeably since all of them involve working with data to find actionable insights. But I like to differentiate them based on the type of the question you’re asking:

  • What:

What are my sales number for this quarter?

What is the profit for this year to date?

What are my sales number over the past 6 months?

What did the sales look like same quarter last year?

All of these questions are used to report on facts and tools that help you build data models and reports can be classified as “Business Intelligence” tools.

  • Why:

Why is my sales number higher for this quarter compared to last quarter?

Why are we seeing increase in sales over the past 6 months?

Why are we seeing decrease in profit over the past 6 months?

Why does the profit this quarter less compared to same quarter last year?

All of these questions try to figure why something happened? A data analyst typically takes a stab at this. He might use existing Business Intelligence platform to pull data and/or also merge other data sets. He/she then applies data analysis techniques on the data to answer the “why” question and help business user get to the actionable insight.

  • What’s next:

What will be my sales forecast for next year?

What will be our profit next year for Scenario A, B & C?

Which customers will cancel/churn next quarter?

Which new customers will convert to a high-value customer?

All of these questions try to “predict” what will happen next (based on historical data/patterns). Sometimes, you don’t know the questions in the first place so there’s a lot of pro-active thinking going on and usually a “data scientist” are doing that. Sometimes you start with a high level business problem and form “hypothesis” to drive your analysis. All of these can be classified under “data science”.

Now, as you can see as we progressed from What -> Why -> What’s next, the level of sophistication needed to do the analysis also increased. So you need a combination of people, process and technology platform in an organization to go from having a Business Intelligence maturity all the way to achieving data science capabilities.

Here’s a related blog post that I wrote on this a while back: Business Analytics Continuum: – Insight Extractor – Blog

Data Science

..And you can check out other stuff I write about here: Insight Extractor – Blog – Paras Doshi’s Blog on Analytics, Data Science & Business Intelligence.

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Where do data scientist hang out online?

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There are few places that I can think of where data scientist hang out online:

  1. Social media: Twitter, Youtube
  2. Q&A: Quora, Reddit (DS threads), Stats.StackExchange
  3. Competition sites: Kaggle, Analytics Vidhya
  4. Blogs: KDNuggets, DataTau
  5. MOOC’s: Coursera, Udacity, Springboard, edx, datacamp

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Are Dashboards dead?… #Analytics

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Let’s think through:

Are Dashboards Dead?

With lot of vendors pushing for democratizing analytics (a.k.a self-service), it may seem that the dashboards would soon be dead!

You need two things to make a org data driven. 1) Push 2) Pull.

“Pull”

…is where most of the analytics vendors are focused right more — it’s set of technologies that the business users want. The big idea here is to enable business users to pull whatever data they want, whenever they want without having to wait for Analytics/IT. Note that the business users are doing the heavy-lifting in analysis (of course you need a data platform to enable this but still it’s the business users using the platform and doing their analysis)

“Push”

…is where there are dashboards which are built by central IT/Analytics and are ready to be consumed by the business users. This should be a governed environment where a lot of effort has been invested by Analytics/IT to make that the metrics are standardized & accurate. This is key to making this work — if the metrics on the dashboard are accurate and metrics are standardized then business users would trust these dashboards more than the self-service dashboards. This would also be their one-place to go view all key performance indicators for their org/department and then if they see something “interesting” (or better yet — get an alert!) then they can dive into the self-service environment and do their thing. You see, “Push” strategy is really great at getting the data to all business users and then “pushing” them to do use the self-service analytics platform.

[BTW: Putting bunch of reports in a grid layout is not what I am talking about here. I am limiting my definition of dashboard which have KPI’s and directs users to where they should be focusing on]

(Again, two things to do here to make sure the push strategy succeeds. 1) Having standardized & accurate data = earn trust! 2) Having KPI’s that align with the strategic plan of the org/dept)

Dashboards Push Pull Analytics Strategy

So now having understood what these strategies are let’s take a minute to put them to use to answer the question:

Are Dashboards Dead Yet?

So let’s imagine a scenario where a org does not a Push Strategy. They have implemented a self-service platform and are focused on evolving that. Now there are two problems that they will run into:

  1. For “casual” users — How do they get them the training they need? OR support that they need?
  2. For “power” users — Once they start creating their own calculated metrics then how do they make sure that they are standardized across what other power users are doing? (also, how do they validate if what they are analyzing is accurate?)

You see both of those problems can be partially (if not completely) solved by having Dashboards:

  1. Dashboards are a great way for casual users to look at their KPI and then they can figure out where they would focus on
  2. Also, Dashboards are a great way to provide standardized & accurate metrics so everyone could trust the number that they are looking at
  3. Note that it shouldn’t require you to start from zero! You should be using the data modeling layer built for your self service platform for the dashboards as well

And that’s why I think Dashboards are not dead yet.

PS: You might see some vendors that are pushing for a different approach where the platform would auto-magically go through the data and get you the “insights” — I think it’s a great approach. Usually they would target dashboards but I would argue that they compete more with “Pull” strategies rather than “Push” because now the business user won’t have to explore so many different variables but the platform could do that heavy-lifting and get them quick insights.

[VIDEO] Microsoft’s vision for “Advanced analytics” (presented at #sqlpass summit 2015)

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Presented at #sqlpass summit 2015.

Data puking and how T-mobile alienated a potential customer:

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I saw this ad on a highway earlier today and my reaction: why would I switch to a network that has just “96%” coverage.

T mobile ad — example of data puking

…instead of converting a potential buyer, this ad actually made me more nervous. You know why? Its a case of what I like to call “data puking” where you throw bunch of numbers/stats/data at someone hoping that they will take action based off of it. So what would have helped in this ad? It would have been great to see it compared against someone else. Something like: we have the largest coverage compared to xyz. My ATT connection is spotty in downtown areas so if it said something like we have 96% coverage compared to ATT’s 80% then I would have been much more likely to make the switch.

I wrote about this adding benchmark in your analysis here

Takeaway from this blog: don’t throw data points at your customers. Give them the context and guide them through the actions that you want them to take.

PASS Business Analytics VC has grown 123% in a year! #sqlpass @passbavc

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It’s been amazing to see the growth of Business Analytics community over the past couple of years as one of the chapter leaders on the PASS Business Analytics Virtual chapter…Here’s a data viz that I put together to analyze effectiveness of our marketing campaigns:

Here’s the chart: 

PASS Business Analytics Virtual Chapter Marketing Effectiveness Chart

It shouldn’t come as a surprise that an “Analytics” virtual chapter is using data-driven marketing techniques! 😉

Calcs:

May’14 = 100 attendees. Jun’15 = 223 attendees. % Diff = 123%

Projections:

With this growth rate, we should have ~500 attendees in our future virtual chapter meeting in Jun 2016. Can’t wait! 🙂

Credits: 

A lot of work by Dan English (current president) and Melissa Demcsak (Immediate past president) went into growing this chapter!

An amazing framework to solve business problems:

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There are two type of things I learned in Graduate school:

1. Useful

2. Not Useful! (useless)

This post is NOT about discussing useless learning’s! So let me share one of most useful things that I learned in my two years at a School of management: How to solve business problems? Sounds cliched but that was, I think, one of the most important skills I picked up there. In particular, I learned about Frameworks used to solve business problems, one of them was called “MECE” which is what I want to share with you in this post.

(Side-note: Most folks learn this at some strategy consulting firm like McKinsey but unlike them, I learned about it in school)

Before we begin, I want to share about why you should care and then I’ll talk about what is it.

Why MECE?

No matter which team you work for, you are solving problems. You wouldn’t have a job if you’re not doing that — so why not get better at it?

If you want to find a root cause of a business problem (& find the solution faster!) then you need to break it down…to break it down, you need to structure it. Now, they are many ways (or Frameworks) to structure a problem — MECE is one of the most effective frameworks out there. So lets learn about that:

(side-note: MECE framework may sound like a simple idea BUT it’s NOT easy to apply!)

What is MECE?

It’s an acronym and it stands for “Mutually Exclusive and Collectively Exhaustive” which means that when you break a problem into sub-items then they should be:

1. Not overlap with each other (mutually exclusive)

2.  If you add up all sub-items then it should represent all possible solutions (collectively exhaustive)

Let’s take an example:

Say that you are asked to analyze “why is Profitability declining?”

Here’s a non-MECE way:

  • Find Top 10% profitable products [does NOT pass the collectively exhaustive test]
  • Out of them find products that are have declining profits
  • Try to find reasons why those products would have declining profits

Here’s a MECE way:

Visual for MECE principle

  • Break it down to Revenue & Cost
  • let’s start with cost, let’s say it’s constant = revenue must be going down for declining profits
  • further break down revenue into 1) Revenue from all non-usa locations 2) USA locations (Note the use of MECE principle here)
  • let’s say that revenue for non-usa locations is increasing, then it must be USA locations that’s the problem! (Note how effectively are able to narrow down and find the root cause faster!)
  • Let’s further break down to product categories for USA locations…Continue breaking down the sub-items in a MECE way till you find the root cause

I hope that gives you a good overview of MECE principle.

Conclusion:

MECE is one of the few effective frameworks that you can use to solve a business problem. If you want to get better at structuring your ideas (to solve business problems), consider practicing MECE as there are ample resources available online that would help you master this!