Why are there so many analytics startups?

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Originally published on Quora: Why are there so many analytics startups?

Question:

Why are there so many analytics startups in the past 2 years?  With Google Analytics getting better every year (for FREE!), what is the value proposition?  I understand the need to augment with some new perspectives such as Clicktale, but I’m not sure I understand the value prop of KissMetrics, SpringMetrics, etc?

Answer:

There are two main reasons:

  1. Features gap between google analytics (free) and google analytics (premium aka 360 now!) — there are a lot companies (esp. with multi-million customers) that want to use premium features but still cant justify the ROI of GA premium. So there are analytics startups out there that try to cater to these “gaps”. Even though GA is improving, there will always be some feature gap(s).
  2. Access to venture capital for these startups — so these startups found a market and they went for it. They also had access to venture capital (easier two years back then it is today!) and it also helped them that “big data” and “data science” was (and still is!) a highly discussed tech topic.

I believe we will see some consolidation in next few years.

“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!

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

 

Doing Data Science at Twitter — A great read!

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Doing Data science at Twitter

Doing Data science at Twitter

Why is “Doing Data Science at Twitter” a great read?

This is an insider’s perspective from someone who is working at a company that I classify as having the highest level of analytics maturity — In other words, Twitter is known to apply knowledge gained from data science into their products and business processes.

It’s also important to recognize that every company is different and the analytics/data-science tools/techniques/processes that would be implemented would also vary based on the analytics maturity — I love that this was one of the key insights shared in this article.

Also, the article talks about two types of data scientists…I thought it was great way to classify them because there’s a lot of confusion in the industry around what a Data scientist does. With that, Here’s the URL:

My two-year journey as a data scientist at twitter

Best,
Paras Doshi

PS: If you like articles like this, don’t forget to sign up for the newsletter!

News from PASS Summit’14 for Business Analytics Professionals: #sqlpass #summit14

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This post is a quick summary for all Business Analytics related updates that I saw at PASS Summit’14:

1. Theme of the Keynote(s)/Session(s) seemed to be around educating the community about the benefits of the NEW(er) tools. I saw demos/material for cloud-based tools like SQL databases, Azure stream analytics, Azure DocumentDB, AzureHDInsight & Azure Machine learning. The core message was pretty clear: A data professional does two things – 1) Guards data OR 2) helps to generate Insights from Data – And they will need to keep up-to-date on the new tools to future-proof their career.

Read more about this here: http://blogs.technet.com/b/dataplatforminsider/archive/2014/11/05/microsoft-announces-major-update-to-azure-sql-database-adds-free-tier-to-azure-machine-learning.aspx

2. Coming soon: Power BI will be able to connect to on-premise SSAS data sources (multi-dim & tabular).

3. Coming soon: A better experience to create Power BI dashboards.

Read more about Power BI updates here: http://www.jenunderwood.com/2014/11/05/pass-summit-2014-bi-news/

4. Azure Machine Learning adds a free-tier! You won’t need a credit-card/subscription to sign up for this.

5. I also saw sessions proposing new way of thinking about an architecture for “Self Service BI” and “Big Data” which might be worth following because since these are newer tools, it’s definitely worth considering an architecture that’s designed to make the most of the investments in these new tools. That’s it & I’ll leave you with a quote from James Phillips from Day 1’s keynote: