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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Introduction to Goal Seek & Solver capabilities in Excel:

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What-if Analysis is a pretty common analysis done by decision makers. Often, they would just create simple excel tables and adjust their variables manually until they get an answer that works. But instead of doing it manually there are features available in excel that will make your life much easier and analysis much more accurate. So, the goal of this blog post is to introduce you to the Goal Seek and Solver feature to help you do what-if analysis in Excel.

#1. Goal Seek:

Let’s say you are a CEO of an e-commerce startup and wondering about what factors you need to focus on to increase revenue. Here’s what the data (*assume per month) looks like when you start out:

excel-goal-seek-1And you want to increase the Revenue to $150K from $125K. The three levers you can pull are website visitors, conversion and revenue per customer.

Now you could manually tweak the values for this variables till you get to $150K but as I promised earlier, there’s a better way!

Let’s start with Goal Seek.

You need to set two variables for Goal Seek.

a. Your goal — which in this case is 150K

b. The variable that needs to be changed to achieve that goal — note that you can specify just one variable to do so. So you need to choose out of the three above what you would like to focus on. Let’s say you want to focus on conversion rate.

So once you have these two things — from the Data Tab in Excel, Go To What-if Analysis, Goal Seek:

excel-goal-seek-2Now, specify the values. For this example, we want to figure out what should be the new conversion rate so that our revenue will be $150K. So here’s an example of how that would look on Goal-seek:

excel-goal-seek-3After entering the values, you will see the status — you can click “OK” to keep the solution and cancel to go back to what you had:

excel-goal-seek-4Perfect! So you need to increase the conversion rate from 1.25% to 1.5% to get to the goal that you had set!

#2: Solver add-in

So, you worked on improving the conversion rate for next month or two and you & your team found out that it’s getting really hard to increase it above 1.35% — And also you found that with the less effort you can move the needle on other variables (website visitors & revenue per customer). Now Goal Seek allows you just set one variable so if you more variables than it doesn’t serve the purpose that well! That is where Solver add-in helps.

Think of Solver as advanced Goal seek where you can set more than one cell that can change. You can also set constraints on what the values could be for all the variables that can change.

Now, for our scenario, the conversion rate is at 1.35% but you want to see the possible changes that you can make for website visitors and revenue per customer to reach $150K.

You also know that you can’t above 1,100,000 Website visitors per month and also need to have less than $11 as revenue per customer.

You will need to enable the Solver add-in in Excel and once you do that you will see that in the Data Tab.

Once you have it, open it and fill up the information needed in the dialog box:

a,. Set objective to Total Revenue with value of 150000

b. By changing cells to: Website Visitors and Revenue per Customer

c. Constraints. Website Visitors <= 1,100,000 and Revenue Per Customer < $11

solver-excel-1After that click on Solve.

if it found a solution, it would show you that on Excel and also give you additional options to whether you want to keep the solver solution or restore it to original values:

For our scenario, it suggesting that with website visitors to 1,010,101 and revenue per customer to $11, we should hit our goal.

solver-excel-2Click on OK when you’re done.

Conclusion:

In this post, we saw how you can use Goal Seek and Solver add-in using an e-commerce scenario but you these techniques can be applied to wide variety of data analysis problems that can be solved using “what-if” techniques.

Hope this was helpful and I would love to hear from you about how will you use this in your work? Or if you use it already then what do you use it for?

Can I be a data analyst at a tech company without a degree in computer science?

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Yes — it’s not a must have to work as a Data Analyst. In fact, a lot of people come from a non-CS background and succeed in this role!

Let’s look at the pros and cons of having a computer science (CS) degree and this should help you evaluate where you fall:

Data Analyst computer science degree

Pros of having a CS-degree:

  • If the data analyst position requires you to have this degree in CS then you qualify! Fortunately this is not that common and usually it says bachelor’s required in cs, business administration or related field so as long as you have bachelors for positions that require it then you should be fine
  • you might already have the basic tech skills that are needed for data analysis jobs and the CS degree might be used to validate that.
  • you can pick up new tech concepts and tools fast(er) — with the cs background, it’s easier to pick up new concepts & tools — and you need to continuously do that to stay relevant.

Cons of having a CS-degree:

  • Not enough business problem solving experience and/or lack depth in business knowledge — so if you have a degree in business then you come ahead! Especially if your background aligns with the role. For example: if you focused on Marketing in your bachelors and the role is focused around marketing analytics then you might have an edge
  • I have a CS degree and then I followed it up with a masters from a “business school” — so this is just based on my experience but few CS students (without real world experience) are inclined to focus on “automation” and “bleeding-edge” instead of focusing on what the problem needs. Lot of data analysis doesn’t need to be automated or shouldn’t be automated and not every company needs <<insert the latest tech trend here: big data, deep learning>> — but CS students tend to do that. That’s what they feel most comfortable with so while that doesn’t stop from getting the job, this would impede their growth as a data analyst within the org.

Conclusion:

So as you can see even if you don’t have a CS degree, you can still find roles that align with your other skills and in fact, you might be able to come out ahead if you can prove that you have basic quantitative and tech skills needed to get the job done.

Related: Paras Doshi’s answer to How do I prepare myself for a career in Data Analysis?

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What are the reasons why developing a data dictionary is so important?

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data dictionary

Let me first define “Data Dictionary” — It’s a document that lists data fields/metrics and their standardized definition to be used across the org.

The key here is: Standardized.

Imagine this:

Imagine that a management team meeting is going on and you have CEO, VP of Sales, VP of Marketing, CFO, COO among others in the room.

Meeting Agenda: why they didn’t hit the $100M profit goal in the first quarter. So each of them start with the reports they had access to.

VP of Sales says they missed it by $5M

CFO says that they missed it by $9M

COO says that they missed it by $7M

VP of Marketing has three different versions on her report and she is confused!

No ONE talks about the “Why” they missed the goal but instead spends next hour reconciling the numbers!

It was a hypothetical scenario but these things happen all the time! Of course it could be any team meeting and the metric could be something else or it could just that someone is working on something on their own and end up spending a lot of time digging through all the metric definitions and trying to makes sense of it all. This is where data dictionary could help! Let’s take this a step further:

What’s one of the most important characteristic of a good data analysis/science?

It needs to be Actionable.

It needs to help business decision makers take action based on the insights that they found or were shared with them. And before they take that decision, business decision makers need the data they can TRUST!

For data to be trusted, it needs to be understood. It needs to have a definition that everyone agrees upon.

This is what data dictionary is for. It lists data fields/metrics and their standardized definition so that everyone in the org understands what the field/metric means and don’t have to worry about aligning their meaning. They could focus on Analyzing and extracting insights that would change the business and the world!

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What are the differences between big data developer and data analyst?

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It depends on how the Analytics & Data Science team is structured in an org but usually you will see following trend:

  1. “Big Data Developer” usually rolls up under the Engineering org. They are responsible for building the data pipelines that feed data to the “data platform” — they use things like Hadoop, Spark, Custom Code, ETL tools, etc to build data pipelines and are responsible developing and maintaining the data platform. And to succeed in this role you need to have deep technical chops. Other titles for this role: Data engineer, Software engineer, etc.
  2. “Data Analyst” usually rolls up under some “business” team like strategy, operations, growth, product, marketing, sales, etc. Data Analyst are the link between the “data platform” and the “business” — these guys are primary consumer of the “data platform” (sometimes you might see shared ownership of data platform between engineering and analytics). They help solve business problems using data and pull data from the “data platform”. These guys need to have a good balance between business and technical skills to be successful in this role.

View the question on Quora.

What is Descriptive Analysis?

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I classify analytics into four kinds:

  1. Descriptive — Reporting “What” Happened
  2. Diagnostic — “Why” is Happened
  3. Predictive — What’s going to happen next?
  4. Prescriptive — How can I use all these things to take business decisions/actions.

With that overview, let’s look at Descriptive analysis a little bit more. This is usually the first step for any organization to start getting value out of all their data. They should be able to answer questions like: What were my sales last quarter? How about same quarter last year? Then compare them to see if they made progress. They can also report on sales (Actual vs goals) for last n months and see if they are trending in the right direction. Things like this! Once you have a good process and platform to get this right then the organization is ready to advance to next step which is diagnostic and this is where you start analyzing the key drivers and underlying reasons to figure “why” it’s happening. But you need start at Descriptive!

Hope that helps!

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Does data analysis and machine learning go hand-in-hand or are they mutually exclusive activities?

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Originally published on Quora. Link Here

“Machine Learning” is a subset of “Data Analysis” — it’s just one of the activities that you could apply to solve a data analysis problem, you just need to find a problem that can use machine learning wizardry! What kind of activities?, you say — well, to answer that we will need to step back and categorize what problems could be solved by Data Analysis. There are broadly three kinds of problems:

  1. “What” Problems. Few example: What are my sales number for last quarter? Can we compare it to same quarter last year? Now, can we break it down by Regions and Product Categories? — you see all these questions could be answered by a querying your data stores or by your Business Intelligence platform. Yo do NOT need machine learning for this. Moving on…
  2. “Why” Problems: Few example: Why did the customer cancel their contract? Why is the profit in region A declining Quarter over Quarter? You see this is little bit more challenging than “what” questions — you will need to structure the problem and pull data from multiple sources. Why did customer cancel? You may want to look at internal (e.g. customer complaints) and external (e.g. bankruptcy) data. Usually you won’t need to apply Machine Learning here — you might benefit in some cases where you “cluster” all churned customers and see if you can find some patterns but again Machine learning is not you primary tool here. Moving on…
  3. “What’s next” problems: This what you have been waiting for — this is where Machine learning could be applied. Example: Which customer accounts will cancel their account this fiscal year? — This is where you train a machine learning algorithm to predict which customers will churn this year. Note that the work you did for “why” problems where you identified some characteristics of churned customers will still be applicable here — and that brings me to: Most organizations don’t usually jump from “What” to “What’s next” stage — every organization is at a different stage depending on their maturity and you can’t apply machine learning to every data analysis problem. Also, with more and more companies using “data” to gain competitive edge, if you are not using machine learning then chances are high that your competitor is and they may out-compete you and that’s why it’s important to continuously invest and reach the highest level — more and more companies and executives are realizing this and it’s a great thing for the data community!

To conclude: Depending on the analytics maturity of your organization and the business problem at hand, you might have to use Machine learning to solve a data analyis problem…And it never hurts to pick up Machine learning basics along with other data analysis skills that you might have.

Hope that helps.