Tableau Desktop was one of the early players in the self-service business intelligence (BI) space.
It's this maturity that makes it one of our three Editors' Choice winners for the category.
New competitors, along with the rise of Big Data and Internet of Things (IoT), have put the pressure on Tableau to constantly improve over the years.
Other Editors' Choice winners, such as IBM Watson and Microsoft Power BI offer intuitive, semantic language interfaces.
With a terrific user interface (UI) of its own, along with a free starter price tag, Tableau Desktop remains a strong contender in the BI market.
This is likely part of the reason why Tableau Desktop moved to a subscription model in 2017, now starting at $35 per user per month for the Desktop version and $42 per user per month for the Online version.
Yes, subscription models are the trend in recent years, but that represents a significant slash from Tableau Desktop's $999-per-user-per-year cost—a price cut the company felt no need to make in years past.
The reduced cost makes it easier for individuals and companies alike to opt for Tableau Desktop, and those that do will have little cause to complain.
It's a mature product and very stable, and being able to add the phrase "Tableau-proficient" on your resume can be a big plus with many employers.
However, that's not to say that Tableau Desktop is resting on its laurels because, faced with the advanced capabilities of its new competition, it can't.
If it does, then the company may not hold on to its market perception (hence, the price cut).
If performance is a concern for your business, then Tableau has attempted to answer with Hyper.
Introduced in January 2018, Hyper is a new data engine that Tableau claims will provide its customers with up to 5x faster querying speed over previous iterations and up to 3x faster extract creation speed.
It also added Tableau Server on Linux and embedded tooltip data visualizations.
The Learning Curve
Tableau Desktop—like Chartio (Visit Site at Chartio) —still assumes too high a level of sophistication in its users if it hopes to progress further in a market that's swiftly moving towards general users rather than data specialists.
Tableau easily found footholds to sprint to the top earlier because experienced business and data analysts were desperately seeking better tools and a way around IT bottlenecks.
But that market is now largely saturated.
The challenge today is to grow the market through distributed BI and data democratization—meaning, tools must appeal to and be usable by nearly anyone in a given organization.
This is why IBM Watson Analytics (360.00 Per User Per Year for the Plus Edition at Software Advice) and Microsoft Power BI (Visit Site at Microsoft Power BI) are such serious threats to Tableau Desktop.
IBM Watson Analytics, for example, has found a stronghold in healthcare where doctors, nurses, and other medical professionals understand data but not the language of data science.
The highly intuitive, semantic language in the UI enables them to work with data with little hassle or learning curve.
Ditto for Microsoft Power BI, which has found a stronghold in organizations that tend to have few data-trained people yet significant need for data analysis and a familiarity with everything Microsoft.
Still, Tableau Desktop is a great product with a feature set that easily rivals that of either of the competitors just mentioned.
If customers are willing to eat its learning curve, then Tableau Desktop can almost certainly fulfill any data analytics need.
And if the company evolves its UI in the future, then there's every chance it might regain its solo position as king of self-serve BI.
Getting Started
The UI aside, loading and extracting data in Tableau Desktop is a breeze—arguably the easiest of the systems I tested.
It has plenty of connectors, and users can choose to work with the data live or extract and load it to Tableau Desktop.
It's just a matter of clicks, starting at Data Source and then choosing your setup by clicking the boxes appropriate to your needs or preferences.
When I connected to CSV files, it instantly connected to all of them in the same group rather than waiting on me to select each file.
That was much faster and easier than in much of the competition, even Google Analytics or IBM Watson.
Color me impressed, as establishing data connectivity is the part of data analysis I find most annoying.
Once you've established a connection to your data sources, however, there's the data preparation task, which means cleaning the data and making nonconforming entries adhere to your established fields.
This is a little trickier as you must hunt your way around the page until you find the right pull-down menus and/or spilt commands for sorting and data manipulation.
Even so, I moved through the entire process in a matter of minutes once I got the hang of it.
The Discovery Process
To an experienced data analyst, using Tableau Desktop is fairly straightforward or, at least, relatively easy to figure out.
But the absence of prompts, pop-up messages, and quick Help links in the presence of esoteric terms and configurations means that many new users will require substantial training before the tool proves its full worth.
In short, Tableau Desktop is not a tool that inexperienced or low-skilled users can poke around and easily figure out.
This means it will present some hurdles to organizations that are looking to become fully data democratized.
From the Data Source page, I merely clicked on the Sheet 1 tab to go to a worksheet.
My data's dimensions were automatically displayed and I needed only to drag and drop the relevant data sets and then choose a visualization with which to explore my results.
I clicked the Show Me button to find visualization and other options.
Tableau Desktop presented me with a wealth of visualization options but, again, a user with little experience or understanding of data science concepts isn't likely to know what to drag to where, much less how to form a sophisticated query.
For experienced data analysts, it's easy to pause for automatic data refreshes, sort records, save, share, and other functions by clicking on familiar icons at the top of the screen.
I quickly moved through the data and built a dashboard to share in a matter of minutes.
Tableau Desktop works so fast and so flawlessly that a user can be forgiven for thinking the tasks simplistic.
But they aren't; it's just that the processing engine and analytics are that efficient and powerful.
That is to say that, while Tableau Desktop needs to further simplify its UI to fully capitalize on the distributed BI movement, I fully appreciate how far Tableau has come in reshaping the BI industry to date.
Data Visualizations
Tableau Desktop offers a variety of visualizations including the old familiar standbys.
It also offers guidance on how to best use each visualization when you click on them, which is particularly helpful when the analysis kicks out a weird-looking visual you want to fix.
The potential downside to Tableau Desktop is that users need some training on it to get the full benefits of all the functionality built into this tool.
The danger is that users may try to skip the training and just feel their way around the UI.
That will work for more basic queries but it will eventually hold the organization back—not because the tool can't do the work but because users don't know how to make it work at harder or more nuanced tasks.
Pros
Enormous collection of data connectors and visualizations.
User-friendly design.
Impressive processing engine.
Mature product with a large community of users.
View More
The Bottom Line
Tableau Desktop is one of the most mature offerings on the market and that shows in its feature set.
While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.
Tableau Desktop was one of the early players in the self-service business intelligence (BI) space.
It's this maturity that makes it one of our three Editors' Choice winners for the category.
New competitors, along with the rise of Big Data and Internet of Things (IoT), have put the pressure on Tableau to constantly improve over the years.
Other Editors' Choice winners, such as IBM Watson and Microsoft Power BI offer intuitive, semantic language interfaces.
With a terrific user interface (UI) of its own, along with a free starter price tag, Tableau Desktop remains a strong contender in the BI market.
This is likely part of the reason why Tableau Desktop moved to a subscription model in 2017, now starting at $35 per user per month for the Desktop version and $42 per user per month for the Online version.
Yes, subscription models are the trend in recent years, but that represents a significant slash from Tableau Desktop's $999-per-user-per-year cost—a price cut the company felt no need to make in years past.
The reduced cost makes it easier for individuals and companies alike to opt for Tableau Desktop, and those that do will have little cause to complain.
It's a mature product and very stable, and being able to add the phrase "Tableau-proficient" on your resume can be a big plus with many employers.
However, that's not to say that Tableau Desktop is resting on its laurels because, faced with the advanced capabilities of its new competition, it can't.
If it does, then the company may not hold on to its market perception (hence, the price cut).
If performance is a concern for your business, then Tableau has attempted to answer with Hyper.
Introduced in January 2018, Hyper is a new data engine that Tableau claims will provide its customers with up to 5x faster querying speed over previous iterations and up to 3x faster extract creation speed.
It also added Tableau Server on Linux and embedded tooltip data visualizations.
The Learning Curve
Tableau Desktop—like Chartio (Visit Site at Chartio) —still assumes too high a level of sophistication in its users if it hopes to progress further in a market that's swiftly moving towards general users rather than data specialists.
Tableau easily found footholds to sprint to the top earlier because experienced business and data analysts were desperately seeking better tools and a way around IT bottlenecks.
But that market is now largely saturated.
The challenge today is to grow the market through distributed BI and data democratization—meaning, tools must appeal to and be usable by nearly anyone in a given organization.
This is why IBM Watson Analytics (360.00 Per User Per Year for the Plus Edition at Software Advice) and Microsoft Power BI (Visit Site at Microsoft Power BI) are such serious threats to Tableau Desktop.
IBM Watson Analytics, for example, has found a stronghold in healthcare where doctors, nurses, and other medical professionals understand data but not the language of data science.
The highly intuitive, semantic language in the UI enables them to work with data with little hassle or learning curve.
Ditto for Microsoft Power BI, which has found a stronghold in organizations that tend to have few data-trained people yet significant need for data analysis and a familiarity with everything Microsoft.
Still, Tableau Desktop is a great product with a feature set that easily rivals that of either of the competitors just mentioned.
If customers are willing to eat its learning curve, then Tableau Desktop can almost certainly fulfill any data analytics need.
And if the company evolves its UI in the future, then there's every chance it might regain its solo position as king of self-serve BI.
Getting Started
The UI aside, loading and extracting data in Tableau Desktop is a breeze—arguably the easiest of the systems I tested.
It has plenty of connectors, and users can choose to work with the data live or extract and load it to Tableau Desktop.
It's just a matter of clicks, starting at Data Source and then choosing your setup by clicking the boxes appropriate to your needs or preferences.
When I connected to CSV files, it instantly connected to all of them in the same group rather than waiting on me to select each file.
That was much faster and easier than in much of the competition, even Google Analytics or IBM Watson.
Color me impressed, as establishing data connectivity is the part of data analysis I find most annoying.
Once you've established a connection to your data sources, however, there's the data preparation task, which means cleaning the data and making nonconforming entries adhere to your established fields.
This is a little trickier as you must hunt your way around the page until you find the right pull-down menus and/or spilt commands for sorting and data manipulation.
Even so, I moved through the entire process in a matter of minutes once I got the hang of it.
The Discovery Process
To an experienced data analyst, using Tableau Desktop is fairly straightforward or, at least, relatively easy to figure out.
But the absence of prompts, pop-up messages, and quick Help links in the presence of esoteric terms and configurations means that many new users will require substantial training before the tool proves its full worth.
In short, Tableau Desktop is not a tool that inexperienced or low-skilled users can poke around and easily figure out.
This means it will present some hurdles to organizations that are looking to become fully data democratized.
From the Data Source page, I merely clicked on the Sheet 1 tab to go to a worksheet.
My data's dimensions were automatically displayed and I needed only to drag and drop the relevant data sets and then choose a visualization with which to explore my results.
I clicked the Show Me button to find visualization and other options.
Tableau Desktop presented me with a wealth of visualization options but, again, a user with little experience or understanding of data science concepts isn't likely to know what to drag to where, much less how to form a sophisticated query.
For experienced data analysts, it's easy to pause for automatic data refreshes, sort records, save, share, and other functions by clicking on familiar icons at the top of the screen.
I quickly moved through the data and built a dashboard to share in a matter of minutes.
Tableau Desktop works so fast and so flawlessly that a user can be forgiven for thinking the tasks simplistic.
But they aren't; it's just that the processing engine and analytics are that efficient and powerful.
That is to say that, while Tableau Desktop needs to further simplify its UI to fully capitalize on the distributed BI movement, I fully appreciate how far Tableau has come in reshaping the BI industry to date.
Data Visualizations
Tableau Desktop offers a variety of visualizations including the old familiar standbys.
It also offers guidance on how to best use each visualization when you click on them, which is particularly helpful when the analysis kicks out a weird-looking visual you want to fix.
The potential downside to Tableau Desktop is that users need some training on it to get the full benefits of all the functionality built into this tool.
The danger is that users may try to skip the training and just feel their way around the UI.
That will work for more basic queries but it will eventually hold the organization back—not because the tool can't do the work but because users don't know how to make it work at harder or more nuanced tasks.
Pros
Enormous collection of data connectors and visualizations.
User-friendly design.
Impressive processing engine.
Mature product with a large community of users.
View More
The Bottom Line
Tableau Desktop is one of the most mature offerings on the market and that shows in its feature set.
While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.
