Tableau Reviews

Tableau is easy to use. It provides us with faster development and deployment. It also produces impressive graphs and charts.

We make heavy use of supply chain analytics to analyze information on orders, bookings, landed costs freight analytics, partner scores, etc.

We would like to see more advanced data science capabilities like predictive analysis and advanced linear regression.

Tableau also lacks native financial functions. Also, we would like to see integration with native Hadoop and some standard reporting capabilities beyond the dashboard.

We have been using Tableau 9.3 for three years, and are currently beta testing 10.0.

We have not experienced any issues with deployment.

We have not experienced any issues with stability.

We have not experienced any issues with scalability.

I rate the technical support as excellent.

We previously used Enterprise standard.

The initial setup is simple and straightforward.

We built the implementation in house.

Pricing is costly when compared to products like Microsoft Power BI. Also, the licensing should be more general.

Go ahead and use Tableau.

Tableau excels at data visualization and exploration. Terminologies such as a "sheet" are similar to the concept of a worksheet in Excel. Connection to most databases are supported out of the box. These factors keeps the learning curve short for business users. Charts in Tableau are rendered using visualization grammar called VizQL. This enables creating unique and out-of-box charts such as lollipop, sankey, sunburst, etc. The charts you can build in Tableau is only limited by your creativity. In addition, it is relatively to easy build interactivity and pass parameters in your dashboards so you can drill in and progressively reveal more details as the user interacts with data.

Tableau has empowered the business users to get answers to their data questions without relying on IT teams. Rather than following build, wait, and iterate cycle, business teams are able to analyze the data much more quickly and identify opportunities for efficiency and revenue generation. Prior to the adoption of Tableau, data was present in multiple spreadsheet; thus, there was a lack of "single source of truth". This problem was resolved by using "Published Data Sources" residing on Tableau Server.

Many areas for improvement are in Enterprise features. Some of which are:

1. A private folder on the server for a user to store his/her files.

2. A built-in tool for deployment and migration between multiple Tableau Server environments.

3. Ability to share a "database connection" between multiple Tableau data sources so that one change to connection information will be reflected in multiple data sources which share that connection.

4. Email distribution: Delivering analytics via emails on enterprise scale is difficult.

Areas for improvement in Visual Analytics:

1. Blending improvements: Slow performance when two sources are blended together on a high cardinality dimension. Blending does not support full outer join and does not allow using non-additive measures ( e.g. count distinct, average) from secondary data sources.

Update for Tableau 10.1: Some of the above are getting addressed in version or in future version as per Tableau road-map declared at Tableau Conference 2016 in Austin, TX. Specifically, there may be features for private folder in future. Tableau migration pains are somewhat alleviated with TabMigrate, their open source tool ( no support) and email distribution is getting better with conditional subscriptions ( 10.1).

Four years. Eleven years in BI.

In my experiences, Tableau deployment has been straightforward when deploying self contained dashboards (called packaged workbooks). However, you need to follow multiple steps to deploy a dashboard to your production server from your development environments.

Tableau (both Desktop + Server) is one of most stable software I have worked with.

Scalability in Tableau depends on many factors such as server configuration, networking, and workbook design. In my experience, the single biggest factor that affects scalability is the dashboard design. Excessive use of quick filters, multiple data sources blended together, long conditional expressions impact scalability. Reading "Designing efficient workbooks" whitepaper is extremely helpful. If your dashboards follow the best practices, it is possible to accomplish near-linear scalability by scaling your Tableau Server horizontally or vertically for large number of concurrent users.

Tableau customer service is very helpful and responsive. Both the customer service and technical support are integrated into their customer portal.

Tableau technical support is very professional and responsive. Many questions are answered in 1-2 business days depending on the severity. However, specific questions such as improving performance fall outside their scope. Thankfully, the Tableau community is one of the most engaged, knowledgeable, and helpful community. If you have a question that is not already answered by the community (very rare), you are guaranteed to receive a response within a few hours.

Yes. I have prior experience with SAP Business Objects, Lumira, Sisense, as well as javascript charting tools such as Highcharts, D3JS, etc. The biggest reason for switching are: ease of use, agility to deliver insightful answers from your data, and large number of chart types.

Initial setup was very straightforward involving running the installer program. I did not run into any issues with multiple versions of Tableau Desktop or Tableau Server.

All of my implementation have been in-house working closely with IT team.

Tableau pricing is competitive with the other options available in the market.

Yes. We evaluated Microstrategy v10, Sisense, SAP Business Objects.

Before committing to Tableau, it is helpful to list the top problems you are trying to resolve. If most of your needs are in operational business intelligence, you might want to evaluate other tools in addition to Tableau. If you want to analyze data, discover insights, communicate your story with data, or impress your customers with great visualizations, Tableau is by far the best tool.

For a successful Tableau adoption it is very important to have strong business user support and understanding the importance of data-driven decisions. Without the business user engagement, Tableau is just another tool.

The most valuable feature in Tableau Desktop developer version is the drag and drop feature of dimensions & measures in the view. Parameters and action filters are also great.

Me and my team work extensively on geo-spatial data, wherein we are supposed to deal with shape files and state/city/pincode boundaries. Tableau also provides a feature to make custom geographic shapes, which helped us in delivering a wide range of solutions. 

I would love to see dynamic parameter values & radial distance recognition on other WMS maps.

I have been using Tableau Desktop and Tableau Server for four years now.

I have not encountered any deployment issues. Deployment is really easy and simple until and unless you get your hands on the Server-Worker Configuration setup. That is a bit tricky.

It is super stable. No issues whatsoever.

I have not encountered any scalability issues.

I used QlikView but switched; the UI was not that great.

Initial setup was straightforward.

I implemented Tableau Desktop & Server all by myself in my organisation.

ROI shows when you hear 'Wow' from your clients every time you deliver a dashboard or a report. They always come back with new projects and greater expectations.

The price is perfect, as the ROI is superb.

Before choosing this product, I did not evaluate other options.

The ability to connect multiple data sources, build data extracts, and display said data in an automated, graphical, format is the key driving factor insuring I continue using Tableau. While there are plenty of other tools on the market, such as Report Builder or Crystal Reports, Tableau is the first I have used that allows for such wide spread data integration and presentation in a visibly pleasing format. Moreover, the data extract function of Tableau has hooked me into using their tool over any other. Specifically, the ease of extracting and automating report generation from these extracts.

Tableau has allowed us to build automated weekly dashboards that explain our organisation's pipeline. Moreover, these dashboards have resolved long standing time sinks which have freed up resources to focus on ever larger and more interesting BI projects. Additionally, we have used Tableau to increase the range and width of reports we are able to generate on a weekly basis.

Tableau development has an increasingly large learning curve. It is marketed as an upper management tool that anyone can dive into. However, trying to develop even simple tables within Tableau is an exercise in frustration and patience. The end results cannot be denied, but the path to getting there is not for the lighthearted.

I have used Tableau now for 2 years.

Tableau customer service has been incredible. Not only do they have an active community forum, but their account managers have been very pleasant to work with.

Out of five, I would rate technical support a solid 4.

I have used Report Builder and Crystal Reports. Switching wasn't really my decision, as the other tools used were at different organisations. Since then, I have tried to move back to other reporting tools, but found them lacking in functions I hadn't even realised I had come to rely on!

Setup of Tableau was the easiest of any reporting environment I have used.

We implemented Tableau through a vendor team who were helpful in getting us off the ground. That being said, any reporting tool is dependent upon the data set it connects to. As such, real value has only appeared from Tableau after long usage and learning of how to use its myriad of functions.

Stick with the development process, try a report over and over, and use Google to search out answers to those questions you think have no answer. Take it from me, if you can build it in Excel, then you can build it in Tableau. It just takes time and effort. Also, it may not look EXACTLY the same, but you can get it pretty close. In some cases even better.

• Reports on demand based on dynamic data
• A single dashboard for understanding the learning performance in various geographic areas
• Easy-to-use navigation
• Responsive UI
• Dashboard was superb and good enough to be used in corporate presentations for managers
• Exporting the graphics to other Microsoft Office tools

• Assisted in driving data-driven decisions and changing strategy accordingly
• Able to compare data from different organizations and collate a model to understand the learning behavior within regions

• Not browser independent
• Sometimes, data retrieval was slow

I had a bad experience viewing the reports with Microsoft IE or Chrome. The version well supported the Mozilla Firefox version. The browser showed no response when it was fetching the data or executing a report based on dynamic data.

This is just an observation. I predominantly used Chrome browser for viewing dashboards using Tableau.

I have been using it for two years.

We chose the solution because it was stable and scalable.

Customer service and technical support are excellent.

We used Microsoft Excel.

A data and analytics team was set up to implement metric analysis and reporting.

Implementation was done in-house. We have a team that is passionate about enabling data reporting for strategic business improvement.

ROI = 100%

For a global organization, pricing is affordable.

It helps me deliver a tool to my clients that can be easily customized according to their business needs. Also, it provides great insights and analytics on huge volumes of data coming from disparate sources, which has helped my clients and managers make sound business decisions.

The most valuable features of the product are:

• Most importantly, flexibility
• Visual features
• Filters
• Actions
• Parameters
• Interactive dashboard building

It helped my organization by winning more client work, as clients love interactive visualization.

Tableau has also helped our managers make better decisions and, more so, quick decisions. With Tableau's quick querying and ad-hoc visualization features, many important processes have been automated and thus Tableau has provided a quick turnaround time for product/project delivery.

Example: My company's auditors historically audited financial statements and transactions from the customer's ERP system via sampling methodology, which did not encompass the entire transactional data and thus did not provide 100% assurance to our clients. With Tableau's advanced data analytics features, we were able to build a tool that is tailor-made to the auditor's needs and thus provides 100% assurance over the entire data set.

The most important feature that Tableau must introduce is to provide 'reference hints' for every dimension and measure. For example: When working with numerous dimensions and measures, it's difficult to debug and back track to determine whether a certain dimension was used in a visualization, tool tip, filter, parameter, or action. It would be great to debug and back track already developed Tableau dashboards.

I’d also like to see the following features introduced or improved:

• Auto-Save or Backup feature. There have been quite a few instances when Tableau crashed after substantial dashboard development and I had to start over from the scratch because the work was not saved. The best workaround for this is to be diligent and create manual backups and save as work is progressing.
• Performance with 32-bit computers. There have been numerous instances when Tableau threw an out-of-memory error when working on a 32-bit system.
• Performance in general. Tableau works best with light data sets. Although extract optimization is a good way to improve performance, it can be better.
• I feel the user interface for adding actions is complicated. It can be made simple and intuitive.

I have used it for two years.

I like the online community.

I have previously used Excel and SSRS. Tableau provides more flexibility than these tools and is very intuitive to learn.

Initial setup was very simple.

It is best suited for in-house development, as it provides on-demand customization.

A bit costly, but worth investing.

It totally depends on the requirement of the client/user. One should research the following tools before investing - Spotfire, Tableau, SSRS, Excel, SAS, SAP Lumira, etc.

Ease of use, polished visualizations, all data elements are treated as discrete, the story-telling feature, self-service, excellent for visually exploring data with unknown questions.

Tableau is used in our Platform-as-a-Service offering for data visualization/exploration, seeking to retire a larger BI platform using it, good customer adoption/feedback.

Metadata/reuse, performance/scalability at high data volumes + high user concurrency, disparity between desktop vs. web versions – need to address enterprise requirements in general.

I have used Tableau for two years.

No issues with deployments/stability.

It does not perform well when you cross into TBs+ of data and thousands of users.

Technical support is very good.

For our PaaS data discovery needs at the time, we reviewed MicroStrategy 9.4 Visual Insight, which was not up to the capabilities/ease of use that Tableau had at the time.

Initial setup was straightforward, it is not a complex system to install.

We implemented it in-house, limited to no need for external assistance.

The ROI is unknown.

Licensing costs continue to increase in Tableau and in QlikView, which we also use, forcing us at some point to consider consolidating to one and/or turning some existing/owned MicroStrategy licensing inward to replace, if the upward pricing trend continues.

Pick the right tool for the job/consumers of the products. There is not a single product that can cover all personas/use cases well or there would be only one product out there commanding the world – and there’s simply not just one. Tableau is great if the targeted users want highly polished visualizations of the data and from an exploration use case, the question(s) for the data are unknown.

There are so many smart features baked into this product, it's hard to even rank them. I think what makes Tableau stand out over other software I've used is that it doesn't start with a visualization, then pump numbers into it: It starts with numbers, and then represents them as shapes on a canvas. The result is more akin to an artist painting on a canvas using numbers as the brushes and colors. As a result, it is the flexibility of the mark types, and how they interact with data types, that make this product stand out. Someone who wants to create a visualization need only imagine the intended output, then use the numbers to create the marks in that output.

A non-trivial example is that a number can be considered continuous or discrete, depending on the context. In some cases, you need to use the same number both ways in the same visualization (histogram, anyone?). The flexibility to specify how a number is interpreted in terms of how an axis/mark will be generated is visualization at a more fundamental level. It is a completely different experience than pointing Excel at a highly manipulated table to generate an inflexibly structured chart type.

The use case has been different from one organization to another. In most cases, the initial buy-in and value-add is at the analyst level. The freedom to calculate, then derive, then iterate - that never-ending cycle every analyst out there knows well - to do that, do it quickly, and in a way that is remarkably beautiful, is every analyst's wet dream. Even if an analyst never shares visualizations they create during the course of a project, the tool makes them better and faster at deriving insights of their own by virtue of everything data visualization is meant to do for humans - improve understanding.

For organizations that have the capital to take it further, beginning to push out the interactivity, reports, and resulting insights up the ladders, or out the branches of the organization, with buy-in to the more-expensive server options - well, those organizations wield a much greater force. The ability for decision makers whose decisions affect many lives, higher in an organization, or the net effect of decision makers who make thousands of small decisions every day - Tableau, used well, makes it easier to make higher quality decisions faster. The same can be said of any technology used well, to be fair, but Tableau’s beauty and speed to insight is unmatched.

In my case, I have also used Tableau as a report prototyping platform. Designing and implementing is so fast in Tableau, when working with business stakeholders who may not know the specifics of what they want, and will ultimately be using whatever reporting platform their corporate standard is on - Tableau is great for iterating through version after version, change after change, until a report is built that is exactly what the business needs. Then, specific requirements can be written for a report builder using that corporate technology - which is invariably slower to iterate on. By moving the prototyping and proof of concept to Tableau, the development hours and agile lifecycle can be decimated.

Connectivity seems to be a sticky point, but it's also a hard nut to crack at the level that I would love to see. Tableau is fast, razor sharp on the whole - WHEN you use an extract. The problem is querying the data to fill the extract is only as fast as the source system. The result is that when you start working with large volumes of data, you often must start finding creative ways to improve the performance of your query. Here's where it gets tricky. I almost exclusively use SQL Server as a source. When I want to create custom data for an analysis (or some ongoing report with complexity), I have the latitude to write custom SQL. The problem is that in order for Tableau to retrieve metadata, the query sent to the server arrives as a subquery like:

SELECT * from ([your custom SQL here]) x

Because of this, I can't use a host of very useful T-SQL techniques that improve query performance or clarity. No CTEs, no temp tables, etc. In some organizations, the argument is that if you want that kind of complexity, wrap it up in a stored procedure and call the procedure (which, yes, you can call sprocs in Tableau), but that comes with its own disadvantages, which I won't get into too much here. But that is not to mention not all report writers or analysts have the privileges to create or alter sprocs on a server within their organization.

In any case, depending on how much control you have over your database as a Tableau user, as well as the nature of the data you are pulling, you may find yourself having to be very creative just to get data TO tableau to create larger extracts.

I started using Tableau in version 7, around 2012.

Desktop deployment is a cinch. Server, I had no involvement with setting up.

The user community is extremely vibrant and engaged, especially for how (relatively) young the product is, so I have only had two occasions to call support. In both cases, they were resoundingly helpful and responsive.

Back in 2012, we evaluated it against QlikView and MicroStrategy. MicroStrategy was more complex than our organization needed, and Tableau won out on ease of use and feature set, but I don't really remember the grittier details.

Initial setup was simple and fast for both Desktop and Server, but, as I mentioned, I was not involved in Server setup.

In 2012, we used an in-house team. I was not deeply involved at the time, so I don't have much implementation advice. For a larger organization, Tableau has created a consulting arm specifically for implementation, and having read a few of the white papers put out by them, I would highly recommend using them for a large implementation.

ROI can only be attained by use, so the ROI will be a function of adoption, not features of the product. Make sure you have the culture and an execution strategy to get people engaged and using it. Compared to other BI software, it's very easy to use, but not everyone will start using it just because there is a new icon on their desktop. Figure out adoption. Focus on it.

If people are using it, the ROI will be there, but if you spend 6-7 figures on an enterprise feature set and nobody uses it, it will have been wasted.

For the prototyping situation described earlier - one complex dashboard built from scratch then implemented in SSRS - the saved development time alone paid for the Desktop license.

This is a visualization software. Make sure you are looking at total cost of ownership in the context of other BI infrastructure that is still needed to get good ROI. Management of data at an enterprise level is more than just visualization, and if all of those things are in place, this product shines. If you have dirty data, slow resources, governance problems, etc., this software is not designed to solve those problems, and those problems will stunt the usefulness you get out of Tableau.