“4W” framework for assessing your Analytics Maturity:

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Most organizations could benefit from Analytics but before you set the Analytics road-map for your organization, it’s important to figure out your current stage and then build the road-map to achieve your vision. So how do we figure out the analytics maturity of an organization? Let me share a framework to think about this:

I have blogged about “Business Analytics Continuum” before — it’s a great framework to think about Analytics maturity in an organization — BUT the issue is that it’s harder for business people to remember the stages: Descriptive -> Diagnostics -> Predictive -> Prescriptive — And so there’s a simpler (but equally effective) framework that I have been using over past few months (What -> Why -> What’s next aka “3W” framework). And recently at a Microsoft Analytics conference, I saw this framework with an extra “W” which makes total sense that I liked a lot! So i thought I will share that with you all. So here you go — 4W framework:

Stage 1: What Happened?

Stage 2: Why did it happen?

Stage 3: What will happen?

Stage 4: What should I do?

Analytics Framework What Why Whats Next HOW

Credit: Microsoft Data Insights Summit

I hope the framework as you think about your organization’s analytics vision/road-map and stages that you need to go through to help your org succeed with data!

Recommendations:
Building data driven companies — 3 P’s framework.

Amazon ML vs Microsoft Azure ML vs Databricks Cloud — my guest post comparing cloud Machine Learning platforms on BigDataCloud.com

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I wrote a post on BigDataCloud.com community comparing cloud based Machine Learning platforms: Amazon ML vs Microsoft Azure ML vs Databricks Cloud 

My goal for the post were to: 1) share a framework to compare cloud-based Machine Learning platforms 2) Apply the framework to three platforms to see how they stack up.

Here’s the framework:

Auzre ML vs Amazon ML

Please read the rest of the post on BigDataCloud.com. Azure ML vs Amazon ML vs Databricks Cloud.

As a prospective Data Analyst intern, how do I answer the most challenging data analysis I have done so far?

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My answer on Quora for: As a prospective Data Analyst intern, how do I answer the most challenging data analysis I have done so far?

https://www.quora.com/As-a-prospective-Data-Analyst-intern-how-do-I-answer-the-most-challenging-data-analysis-I-have-done-so-far/answer/Paras-Doshi?srid=uWIN

When I hire for Data Analyst (Jr. or Intern) positions, I look for three things:

1) Analytical mindset:

I would do this by sharing a hypothetical case study and seeing how you go about solving this. I would look for things like: a) Approach: How do you break down the problem? b) Effectiveness: How effectively can go about solving the case. I am NOT looking for the “Right” answer but just want to see how you go about solving the case.

(Search for “Management consulting case studies” — I usually pick a simple case)

2) Communication skills:

This is pretty standard across many roles but it’s important for data analysts to be able to communicate their recommendations/findings to stakeholders.

3) Basic hard/tech skills + Willingness to learn new tech skills:

I would ask you basic tech questions around SQL, Excel OR other “tech skills” that you might have mentioned in your resume. I am not looking for expert-level knowledge but just want to make sure you know things that you have listed on your resume or things that you studied. Also, I would ask you questions that would help me figure out whether you are open to learning new tech skills.

So now that I have shared the framework with you, let me try and answer your question: How do I answer the most challenging data analysis project that I have done?

a. If you had a good approach for your project then It would mean that you know how to break down data analysis problems and solve them. So solving a basic case study shouldn’t be difficult for you and I could check box #1!

b. If you can communicate the “complexity” of the project effectively then I think I would check the box #2: communication skills!

c. Since you solved a challenging project, I assume that you picked up some tech skills (Bonus points if you picked up new tech skills while solving this problem). Just let me know what tech you used to solve the problem so that I can ask questions around that — if you are able to answer them then I would check box #3!

It’s NOT about the challenging project but your learning/takeaways from that project that will be help you the most!

Now, assuming that the interview team think you are a good “culture fit” plus you came out on top compared to other candidates then you will get an offer to join the team as a Data Analyst!

Hope that helps and may the force be with you!

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.

Webinar: Learn how to build a Machine Learning model to predict customer churn

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UPDATE: WEBINAR HAS ENDED. RECORDING CAN BE FOUND HERE:

https://www.youtube.com/user/PASSBAVC


Next week, on Mar 15th, 2016, We at Business Analytics VC are hosting a webinar to help you dive a little bit deeper with azure machine learning and learn about building a model to predict customer churn. Even if you don’t use Azure, I think you can still benefit from learning about the use-cases and the framework to solve this problem. You can register using this URL:

http://bit.ly/PASSBAVC031516

see you there!

PS: if you like to learn about how to build a recommend-er system in Azure ML then you can see last month’s presentation here: http://bavc.sqlpass.org/Home.aspx?EventID=4514

Completed Marketing Analytics Course from Coursera:

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I just successfully completed the Marketing Analytics course from coursera. The certificate was issued by coursera and university of virginia — it was great to brush up some of my existing skills and then build upon it by learning some new techniques/frameworks.

The course covered:

  1. Marketing Resource Allocation
  2. Metrics for Measuring Brand Assets
  3. Customer Lifetime Value
  4. Regression Basics
  5. Marketing Experiments

If you haven’t checked out courses on coursera yet then I would recommend to check those out! There’s a ton out there for data professionals!

Coursera Marketing Analytics Certificate

 

Machine Learning Algorithm Cheat Sheet:

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If you’re getting started with Data Science & Machine Learning then I think this would be a great resource for you. This “cheat sheet” helps you select the “algorithm” to test depending on the problem you are trying to solve and the data-set that you have.

Download link: http://aka.ms/MLCheatSheet (Courtesy: Azure Machine Learning)

Also, even though the cheat sheet was created to help you with “Azure Machine learning” product, it’s still valid if you use other machine learning tools.

Azure Machine Learning Algorithm Cheat Sheet