Looker vs Tableau: How would you compare them in terms of price & capabilities?

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Someone asked this on quora so here’s my response: This is a great question — one that I figured it out when I led Analytics at Kiva[.]Org last year so I am happy to add my perspective here on Looker vs Tableau:

Looker vs Tableau

Let’s talk about capabilities first and then price.

Capabilities — Looker vs Tableau

Even though both of these tools are classified under Business Intelligence, they have some pretty clear product differentiation so in this section, I will share that. I will share the three main components of Business Intelligence platforms and then map it back to core strengths of each product.

Business Intelligence platforms typically has three main components:

  1. Data Collection, Storage & Access
  2. Data Modeling
  3. Data Visualization

#1) Data Collection, Storage & Access: Both of these tools don’t do data collection & storage. You will need infrastructure to collect data and store it — typically it is stored in databases. And you can access data from databases using SQL. You will need to connect to these data sources from either of these tools and access data — Note that: On the surface, it might look like Tableau supports more data sources than Looker but there might be workaround to get your data into one of the data sources supported by Looker and take it from there and so I am not awarding extra points to Tableau for this. Also, I am personally a big proponent of using Analytic databases like redshift, vertica, bigquery & Azure DW for Analytics applications which Looker & Tableau both support so calling it a tie here!

#2) Data Modeling: This is Looker’s core strength by a wide margin! Why? This is because of their LookML which is their data modeling layer and I am super impressed by this after using it for a while now! So let’s chat about what data modeling layer means and why you should care.

Data modeling (in this context) means creating data models that take your raw data as input and then it’s cleaned, combined, curated & converted and made ready for data analysis.

Why is this important? Not everyone can clean, curate, combine & convert raw data into analysis-friendly data assets. That’s what data analysts are trained and specialize in. May in the future we will have tools that do that OR maybe we will see plug-and-play (aka turnkey) solutions for few key analysis needs but for now, you need data analysts that can create these data models.

Now there are two ways to create data models:

You can create them on-the-fly (ad-hoc) OR you can publish all of these data models on a platform (like Looker).

There are all sorts of issues with doing it on-the-fly — it works for small teams (<20–30 people) but more than that you need to have some process in place. For instance: You can’t automate data models that you need often so that’s wasted time, Also, you can’t share these models easily with others, creates a single point of failure and if the analyst person is sick or on vacation then no-one gets “insights” from data — the world stops spinning. Yada Yada Yada…So self-service is good after you have few business users who want to consume data.

So what does a self-service platform bring to the table? They help data analyst build these wonderful data-analysis friendly models and publish them so everyone who cares in an org can access it. So the consumer can focus on analysis part and not worry about doing the not-so-good part of making it ready for analysis. Also, this ensures all sorts of other benefits: standardized metric definitions, trusted data sources, better collaboration among analysts, speedier model-delivery process, get out of excel hell and what not!

Think of this way: If you have all key data model available on your self-service platform then your data analyst can focus on 1) advance stuff = more $$$ 2) building more data models (and so eventually they can do more advanced stuff later and more $$$!)

Looker vs Tableau

This is where Looker fits in. Looker is great at this data modeling thing — it’s platform is amazing for anyone looking to solve this problem. You can also do data visualization on top and build dashboards.

Alright, moving on:

#3) Data Visualization: This is Tableau’s forte! No one does data viz better than Tableau, at least right now. There are vendors that are investing significant resources on this and they are close but still Tableau is a leader in this space.

Having said that, let’s map it back to how it help business users & analysts:

Business users and self-service environments:

Tableau is not great at data modeling thing. Yes, you can do basic clean, combine, curate & convert thingy but it doesn’t work well with intermediate to advanced needs. So if you have a self-service data modeling layer already in place that Tableau can connect to and you are looking for a data visualization layer then go for Tableau! You would be able to create some amazing visuals, dashboards and stories that will WOW your business users! But to make sure this scales you need to seriously think about 1) how to overcome the limitations in tableau’s data modeling layer OR 2) use some other tool to build this data modeling layer and connect Tableau to it.

Pro Tip: I highly recommend trying out trials of these products and seeing what works best!

Analysts:

Tableau shines at data discovery! While this certainly helps business users, it’s best leveraged by analyst because whenever they are working on ad-hoc data analysis (one-time, strategic in nature) projects they can be much more effective and discover the underlying trends and patterns in their data by visualizing it using Tableau.

So with that context you might be wondering, What tool did I champion & Implement at Kiva?

This is public knowledge that Kiva is a Looker customer because it’s Logo is on their website so I can share this.

After evaluating about 30+ tools (including Tableau), I ended up championing and eventually leading the initial implementation sprints to implement Looker at Kiva because the goals & vision that we had for Kiva’s data & analytics platform aligned better with having the data modeling layer that met Kiva’s needs. So you need to figure out your goals and vision and then choose the tools with that framework.

Pro Tip #1: It’s insanely hard to figure out what your goals and vision for analytics in an org. To figure this out, you might want to chat with organizations in the same industry at the same size & stage and see what they use. Ask them about what they use and whether it worked for them. Ask them about their Return on investment. This is a great way to get external feedback but you still need to figure out internal needs and prioritize them.

Pro Tip #2: Both of these tools have amazing reviews! You will see them highly ranked in analyst reports too — this is great but it’s important that ever before to clearly define what your organization needs and then map it back to the core strengths of these products (or any other tool for that matter) and go from there!

[I am happy to help evaluate the right tool for you needs, feel free to contact me: Let’s Connect! – Insight Extractor – Blog ]

Pricing — Looker vs Tableau

I can’t talk about Looker’s pricing because it’s not public, I apologize! You need to contact them to get the quote.

However, you can anchor that with Tableau’s pricing which is public: Buy Tableau | Tableau Webstore

Your analyst and power users will need Tableau Desktop/Professional which is $1K and $2K respectively (one-time thing) and then depending on your deployment model: cloud or self-hosted — the price varies:

Looker Tableau Pricing

*Note that Tableau online is a subscription model so you can definitely start small. Let’s say 5 business users in a department and take it from there. If you grow then you can later look at other tools like Looker. (If you are rapidly growing, account for the non-trivial time needed to migrate from one platform to another and so it might be worth it to pick the right tool from the get-go)

Pro Tip: I will encourage you to think about building a ROI model too. You know use some analytics for your analytics projects 😉 — I apologize, couldn’t resist! Anyhow, the point is that instead of just thinking about the “cost”, think about the value-add and anchor your investment figure to that. There’s a reason some analytics tool are priced at let’s say $1000 vs some tools priced at $100,000 — both of them have different value proposition and if you know how to extract value of the tool and can project it then you can get better ROI!

Hope that helps! If I can be of any further help, email me or comment here! Let’s Connect! – Insight Extractor – Blog

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How can I start learning and exploring the field of Big Data Algorithms?

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Someone asked this on Quora about how to learn & explore the field of Big Data Algorithms? Also, mentioned having some background in python already and wanted ideas to work on a good project so with that context, here is my reply:

There are two broad roles available in Data/Big-Data world:

  1. Engineering-oriented: Date engineers, Data Warehousing specialists, Big Data engineer, Business Intelligence engineer— all of these roles are focused on building that data pipeline using code/tools to get the data in some centralized location
  2. Business-oriented: Data Analyst, Data scientist — all of these roles involve using data (from those centralized sources) and helping business leaders make better decisions. *

*smaller companies (or startups) tend to have roles where small teams(or just one person) do it all so the distinction is not that apparent.

big data

Now given your background in python and programming, you might be a great fit for “Data engineer” roles and I would recommend learning about Apache spark (since you can use python code) and start building data pipelines. As you work with a little bit more than you can learn about how to build and deploy end-to-end machine learning projects with python & Apache spark. If you acquire these skills and keep learning — then I am sure you will end up with a good project.

Hope that helped and good luck!

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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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What analytics data gives you the most actionable advice to improve your blog?

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Someone asked on Quora: What analytics data gives you the most actionable advice to improve your blog? so here’s my answer:

I have been blogging about Analytics for past few years and this question is at the intersection of both so let me give it a shot:

It depends on two things: 1) Your goal for running the blog 2) Age of the blog

#1: Your goal

why blog analytics

First let’s talk about your goal for running the blog. It’s important to define this as this would help set the metrics that you will monitor and take actions to improve it.

Let’s say that the goal of your blog is to earn is to monetize using ads. So your key performance indicator (KPI) will be monthly ad revenue. In that case you can improve by one of the three things: Number of People visiting the blog x % of visitors clicking on ads x average revenue per ad click. You can work on marketing your blog to increase number of people visiting the blog. Then you can work on ad placement on your blog to increase % of visitors clicking the ad and then you can work on trying different ad networks to see which one pays you the most per click.

let’s take one more example. Like me if your goal is to use your blog for “exposure” which helps me build credibility in the field that I work in. In this case, the KPI i look at is Monthly New Visitors. I drill down further to see which marketing channels are driving that change. That helps me identify channels that I can double down on and reduce investments in other areas. For example: I found that Social is not performing that great but Search has been working great — I started investing time in following SEO principles and spent less time on posting on social.

So first step: Define your goal and your KPI needs to align with that.

#2: Age of your blog:

  • Early: Now at this stage, you will need to explore whether you can achieve what you set out to using blogging. So let’s say you wanted to earn money online. In first few weeks/months, you need to figure out if it’s possible. Can you get enough traffic to earn what you wanted? yes? Great! If not, blogging might not be the answer and eventually all your energy is being wasted. Figure this out sooner rather than later — and take first few weeks/months to make sure blogging helps you achieve your goal.
  • Mid: By this stage, you should know how blogging is helping you achieve your goal. So it’s time to pick one metric that matters! So if your goal was to earn money using ads then go for Monthly ad revenue and set up systems to track this. Google Analytics will be a great starting point. Also, at this stage, you should be asking for qualitative feedback. Ask your friends, ask on social, get comments, do guest blogging on popular platforms and see if you get engagement — basically focus on qualitative feedback since you won’t have enough visitors that you can analyze quantitative data.
  • Late: In this stage, you have the data and the blog is starting to get momentum. Don’t stop qualitative feedback loops but now start looking at quantitative data too. Figure out the underlying driving forces that move the needle on your KPI. Focus on improving those!

TL;DR: Define your “why” and then pick a metric— then use combination of qualitative and quantitative data to improve the underlying driving factors to improve the metric.

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How Marketable is R programming?

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Someone asked this on Quora: How Marketable is R programming?

Answer:

Let’s step back!

Why do you want to learn R? OR why do people learn R?

To solve problems that R can address. Right?

What problems do you have? OR what problems does your COMPANY have? OR what PROBLEMS your Dream company that you want to join have?

<< LIST THEM DOWN HERE>>

example:

  • I want to predict customers that are going to churn next quarter.
  • I want to identify Marketing channel that drove the revenue growth last quarter.
  • etc..

What’s Next?

NOW, take all of these problems and find ways to solve them.

R may or may not help.

You could just do it in Excel. Then do that.

OR R helps you a little bit in the process but you need something else.

In some case, R is a perfect solution! Like building a model to predict customer churn!

So, What?

you see, learning R is important and you might get a job by showing that you have “R” chops but that will not be enough for career growth. You should be focused on learning to solve business problems using data. use R sometimes. use Excel sometimes. use Python sometimes. use SQL. use Tableau. use << INSERT A TOOL HERE>>. Learn them. Apply them. Figure out their strengths and weakness. BUT learn to use all of these technology platforms to solve problems! Solve problems that are thorny. Solve problems that move the business needle. Solve problems that get your bosses boss promoted.

If you do that, marketing your skills wouldn’t be a concern anymore.

It’s NOT easy. And it WILL take time.

TL;DR: Go for it! Learn R! But more importantly, learn to solve problems with data.

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How do you generally detect a fraud using analytics?

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There are two broad range of algorithms that can help you detect fraud: 1) classification (supervised) 2) clustering (unsupervised)

Fraud Analytics Anomaly Data Science

Now it’s a fair assumption that fraud is pretty rare and it’s an outlier in your data. In other words, it’s a anomaly and the process of identifying them is called Anomaly Detection.

So under classification, there are algorithms out there specialize in “anomaly” detection like one-class SVM and PCA based anomaly detection. Try them out on your dataset and see if it’s able to capture “anomalies” in your dataset. While you are at it, don’t discount traditional classification algorithms either, they may be useful as well. You will have to train these algorithms and that’s why they are called “supervised”.

There an alternate approach. Which is to use unsupervised algorithms called “clustering” techniques. You could try something as simple as K-means or something more sophisticated. I haven’t used clustering much for solving fraud problems and have usually deferred to anomaly detection algorithms for this. But I am throwing this out there for making sure you know all the options! I can see these algorithms being applied to exploratory analysis where you are just exploring your data to find outliers to study them.

Hope that helps!

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Book Giveaway: Head First Data Analysis — Ends 07/22/2016

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<< THIS GIVEAWAY IS CLOSED NOW! Thanks for Participating! >>

Head First Data Analysis

Book Giveaway: Head First Data Analysis — A learner’s guide to big numbers, statistics and good decisions!

I love Head First series — if you haven’t read one of these books, you should — it’s great! So when I learned that they had a Data Analysis one, I had to read it. So I bought one and skimmed through it.

Now, Instead of letting it sit on my shelf, I think it might better serve its life purpose if more people read it so I have decided to do this little experiment.

Rules:

  1. You need to have an US-based address so that I can ship it to you (no cost to you!)
  2. You need to comment on this blog post on or before 07/22/2016 — just put your name & email. I’ll contact you if you win*

*Random selection!

Go!