Tableau vs Power BI vs Google Data Studio

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In 2019, there are a plethora of options when it comes to Business Intelligence (BI) platforms. There are many factors to consider when selecting the platform that will work best for you and your team, which can quickly become overwhelming. Luckily for you, we have gone through the trouble of evaluating three of the most popular products on the market: TableauMicrosoft Power BI, and Google Data Studio. We evaluated these products across five major feature categories: connecting to data, cleaning data, analyzing data, visualizing data, and sharing data. We also include an overall score at the bottom of this post if you would prefer skipping through the detail.

If you are getting started on your data journey or would just like a little bit of guidance, feel free to reach out to us at info@compassred.com.

Connecting to Data

Probably one of the most important qualities (aside from visualizing) is actually connecting to the data. Here are the highlights from each:

Tableau

  • 75+ built-in connectors

  • Option to extend connectors with ‘web data connector’

  • Supports most file types (csv, json, excel, etc.)

  • Connects to most databases (Microsoft, Amazon, Snowflake, SAP, Spark, Google etc.)

  • Connects directly to some services (Google Analytics, Dropbox, Marketo, Salesforce)

Power BI

  • 90+ built-in connectors

  • Some connectors still in beta, but new ones being added all the time

  • Supports most file types (csv, json, excel, etc.)

  • Connects to most databases (Microsoft, Amazon, Snowflake, SAP, Spark, Google etc.)

  • Connects directly to a variety of services (Google Analytics, Adobe Analytics, Marketo, Salesforce, Facebook, Mailchimp, Mixpanel)

Data Studio

  • 17 built-in connectors

  • Puts the onus of developing connectors on the community

  • Only supports csv file type

  • Only supports MySQL, PostgreSQL, and Google Cloud databases (BigQuery, Cloud SQL, etc.)

  • Only connects directly to Google services (Google Analytics, Google Ads, YouTube, 360 Products, Search Console)

Cleaning Data

We’ve all heard the statistic: “80% of the work is just cleaning the data to make it useful”. Based on our experience — we can’t agree more. Here is how each tool deals with it.

Tableau

  • Supports basic left, right, inner, outer joins of two datasets

  • Tableau Prep — standalone product released in 2018 to compete with Power Query

  • Grouping, binning, and aliasing available once data is loaded

Power BI

  • Most advanced tool on the market

  • Power Query — separate interface from main editor, allows for custom ETL and provides quick options for common tasks

  • Grouping and binning are supported once data is loaded, but not aliasing

Data Studio

Analyzing Data

More of the BI tools are in general becoming good analyzers — and are starting to ramp up their efforts into predictive algorithms.

Tableau

  • Most robust list of supported functions

  • Advanced functions like FIXED that make it simple to perform complex SQL-esque functions.

  • Quick access to statistical tools like quartiles, box-and-whisker, clustering, forecasts, and trend lines.

  • Supports parameters of any type.

Power BI

  • Coming from Excel will help with functions

  • Advanced functions require creating custom data tables

  • Some statistical tools, but most are dependent on visual type. Supports R scripts though!

  • Parameters are limited to numbers

Data Studio

Visualizing Data

Why the BI tools were created all center around visualizations. The biggest watch out is to be careful as to what amount of work needs to go into it to make a visualization beautiful. Tableau has always been the leader in this area — but it can be complex at times.

Tableau

  • The gold standard for what it means to be ‘good at viz’

  • Popularized #MakeoverMonday

  • Has the most granular features for getting your visual to look exactly the way you intend (without knowing how to code)

  • Anything other than a line or bar chart requires advanced knowledge

Power BI

  • Visual interactions are the killer feature

  • Ability to create custom visuals (even low code in Microsoft Visio) or download community visuals

  • Not as many customization features as Tableau, but reaching feature parity with each release

  • Simple and effective approach to creating visuals

Data Studio

  • Makes it easy to create something that looks pleasing without much effort

  • Recently opened up the ability to create community visuals (community is very small right now)

  • Very limited customization (data labels are a nightmare)

  • Very simple approach, but a little too simple

Sharing Data

Visualizations are great — but not if you can’t deploy it to others. Here is where each stands:

Tableau

Power BI

Data Studio

  • Built around easy sharing, but does require a Google account

  • Online portal is fresh and easy for users that are accustomed to other Google products (e.g. Google Drive)

  • Sharing is always free with no limits

  • PDF/printing is limited

Overall

Here’s how we informally net out on each tool set. There are a lot more details to each. Please contact us (info@compassred.com) if your team needs details or assistance on each.

Tableau

  • The Market Leader

  • The go-to choice for the majority of analysts since 2003

  • Recently feeling the heat from two tech behemoths (Microsoft and Google).

  • Can be the most challenging for newbies to learn

  • Has a different approach for visual creation than Power BI and Data Studio, which could be a detriment moving forward

Power BI

  • The Challenger

  • Power BI is becoming a viable option for many organizations that already run on the Microsoft stack

  • With new releases every month, it’s quickly reaching feature parity with Tableau.

  • Like other Microsoft products, it lacks the polish that makes it un-intimidating for newbies

Data Studio

  • The Wild Card

  • A very limited product in its current state, but works for most teams that work with Google data daily and need a simple/free solution

  • New features are sporadically released and often miss the mark

  • Easiest to learn and deploy for teams getting started on their data journey

  • Un-intimidating design like most Google products (made for the masses)

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