Tableau Reviews

I've used it for multiple purposes, for example, for exploratory analysis or just for dashboards for presentations.

I'd say it brings a centralized place to check day-to-day metrics and KPIs. It helps reduce the duplicated reports or sources of information to get the same data or information. Everyone knows that those dashboards are up to date. They know where to find the answers they're looking for.

I really like the interactivity of the dashboards.

I appreciate the fact that you can have filters and parameters so that users can really customize the view to what they want to see.

Truthfully, this solution offers pretty much everything that I need for my everyday tasks.

It seems that power BI is more targeted for report creation while Tableau is more of just a dashboard. If you need to have something report-like, or downloadable to share outside of the dashboard, that's where Tableau is lacking some features. 

Users would like to be able to export an Excel file when they see a table or something like that. That's not an out-of-the-box feature for Tableau.

I've used the solution for a year and a half so far. It hasn't been that long.

I've had a good experience with the stability. There are no bugs or glitches that I have experienced. It doesn't crash or freeze. It's reliable. 

We did have an issue with our server and it took a while for Tableau support to find a solution. However, that was a one-time thing. That's the only time where we've had issues with our server.

The scalability is pretty good. In our case, we did start small and we are now scaling in for our different departments. It's working great.

We are not a big group, however, I would say that we have around 80 to 100 users and that combines creators, explorers, and viewers - a little bit of everything.

We are getting used to it and using it more and more. We are expecting to increase usage in the future.

I've never been in touch with technical support. I cannot speak to how helpful or responsive they are. 

We did not previously use a different solution prior to adopting this product.

I wasn't around for the initial setup. I cannot speak to what the process was like and couldn't say if it is difficult or straightforward. 

We have some server admins that take care of it and work with Tableau to support it whenever needed. It's a group of people, however, I am unsure about the actual number of personnel that handles it directly. It might be three to five people. 

I've looked into Microsoft BI and downloaded some information about it recently.

I'm just an end-user of the product.

I'm likely using the latest version of the solution. 

Everything was implemented when I started, so I wouldn't know if there were any hiccups or best practices, or lessons learned from the process of setting it up. 

I'd rate the solution at a nine out of ten, from the experience I've had so far. It has helped us tremendously with our everyday reporting and things like that. I can do pretty much everything I want to do and it's been working fine for us.

Tableau has improved my organization in a variety of ways, one of its uses being that of data analysis. It provides a server platform for sharing information. We use it for internal collaboration, as well as other tools for data catalog, for creating the dashboards, for preparing the data in preparation of creating the dashboards, called an ETL extract, and as a tool to transform and load. Tableau is a platform that has several products, perhaps four or five, that average for the fifteen of big data, data evaluation and data collaboration. No specific aspect can be used for this and it can be employed in marketing and finance. It serves the needs of data analysis and providing an algorithm for machine learning. For instance, you can have a logistic regression to analyze whether a specific customer is a good bet or not, such as a bank that is contemplating the loan of money. It allows you to visualize and analyze your data no matter what it may be, though it can be used for an alternate solution.

A feature I have found most valuable is the ease of use and straightforwardness, in addition to the flexibility of Tableau. I like the fact that Tableau can connect to a wide variety of databases, be on cloud or on-premise. Tableau can connect to over 100 database types, including structured and non-structured databases. Tableau can connect to a PDF and extract all the tables you have in that PDF. Suppose you have a one hundred-page PDF containing sixteen tables of data. Tableau can connect to that PDF and extract its data. Tableau can connect to Google Drive, to a host of marketing portals on the internet, to cloud companies such as AWS or Alibaba and to many different types of databases. That's one huge advantage of the tool.

While it can be complex if you need to alter a lot of parameters, it provides simple installation. It is very easy. All you would need to do if you have only one Tableau running server is to employ the maximum connection and install a license column in Adobe Reader. 

An area needing improvement involves the complexity of the product should you need to alter a lot of parameters. 

Definitely speaking, it's straightforward and it's very easy. Implementation problems can be dealt with by the client, in place of the user consultant. Let me give you some examples of things that could take long in a Tableau implementation. Suppose you have five different business areas in your company: marketing, supply chain, finance, HR and procurement. Let us suppose that access to HR salaries is not company-wide but is limited to only a select number of people in HR, such as the manager or the director of the department. Yet, I want people in the supply chain to be able to see and access different data from different areas. While this would not be technically difficult it would be time consuming if the businesses are very particular. There may be many policies involved in access authorization, in data availability and the like.

This can involve a very strict security process using an outside identity provider. Instead of just logging in your username and password, you may have different technologies which are more safe and secure that need different providers to interface in Tableau. Depending on the need, this will be time consuming. For instance, while I don't know how this would be in your country, suppose you have an identity provider, in Brazil, marketing in Tableau. If you go to Asia, you may sometimes have a bio-metric identity that your hand or fingers employ which is going to get back at you. In that circumstance, they are going to send you a number or a code in your cellphone, requiring two steps, one to enter the bank and the other to withdraw your money. So, these things we call an outside identity provider, meaning a different vendor or different companies who manage the servers of managing identities. These would entail an integration with Tableau and these outside companies for security purposes. This would involve them sending me files and me sending them back in order to authenticate the user into the Tableau server.

This can be time-consuming because they involve or require a different partner. Tableau is made for basic needs, such as requiring a user and a password to log in to the server; an unsophisticated architecture; or use of a single instead of a cluster of servers. If you have non-specific data security needs or you just want to analyze and sell data, that can take less than a day. But if you have technical servers, many interfaces, different providers and more serious processes, that will be time consuming. 

While Tableau does integrate with Arc server and Python server, the integration process is slow and the information is integrated in a protracted fashion. Sometimes your data will vary. You may have a vector of data. You may have a matrix of data. For some algorithms we do not use regular data, but a different data structure. Tableau does not work with these different data structures. As such, interfacing with Arc server and Python server, which are still languages that are widely used in machine learning, all happen slowly. It does not happen by a matrix of data and data vector. 

I have been using this solution for five years. 

In the past I worked with Oracle E-Business Suite while working with ERP markets over a thirteen or fifteen year period. Yet for the past five years I've been focusing mainly on artificial intelligence, machine learning, big data and the use of other software, such as  Tableau and Azure for the purpose of developing and building data to create algorithms and visual dashboards to show the data. It's been around five years since I have turned my focus solely to big data and machine learning. 

Definitely speaking, the initial setup was straightforward and very easy. 

Another option I evaluated is Power BI from Microsoft. It's cheaper than other solutions and requires fewer different packages. The major competitor of Tableau is Power BI from Microsoft and Microsoft's much cheaper than Tableau. But Microsoft usually requires me to be on Microsoft cloud Azure. You have to buy other solutions for an integrated solution. At the end your cost will be much higher. So Tableau is more flexible. 

In Tableau, I can have a scatter plot with millions of marks. Suppose I have a graph that plots my value against my process and each dot in the graph is a sale that I've made. So I have 30 million dots in this graph reflecting my 30 million sales. Tableau can run this easily and fast. Power BI cannot. Power BI has a limitation of 13,500 marks, meaning Tableau has more capacity in delivering data than its competitors. 

It’s good for quick visualization and being able to quickly consume unstructured data to play around with. This is good way to show a demonstration/prototypes of dashboards and scenarios for design discussions on reporting requirements or to show what the data is telling us when it comes to features of data integration, OLAP services, data mining and extract, transform, and load (ETL) capabilities.

Good for adhoc visualization of an unstructured dataset which comes from other sources outside of source systems that you can overlay on top of the structured data and you have to get a quick visualization dashboard prototype going. It helps with the Agile design build and can be used in our current operations analytics work to overlay multitude of data sources that we know of. Can always work offline, which is nice. It’s good for organizations with very limited staff to do quick report builds and dashboards that can be put on our SharePoint site for sharing or on reports when responding to data caps. I use it a lot for design discussions so I can communicate the gaps in data sources for data exchanges or to generate a storyboard prototype of how the data is to be used in visualization but where we need to have data exchange/ETLs on.

It is good for its use if ad hoc, offline, or needed for quick turnaround on reports/dashboards. It is not so great when it comes to data exchange/integration, data mining, etc. I rely on what’s available in current versions to see what APIs and plugins that I can use and they have Open Source on GitHub is a plus to share things to re-use.

Room for improvement is more on data integration features that are agnostic to any solution platform but can be plug and play to be able to reuse what was built out Tableau in any other platform of work.

Over five years, and the past three more for integrating the use of a similar family of tools where Tableau is one of a few options in our environment, where these services are considered for quick-hit items as needed, given time, dollars, and analyst skills.

No.

Yes, see other answers. Scalability per user defined elements are okay but not so much for enterprise wide reuse. Per license cost can have some work done to it to make it more affordable on the recurring maintenance end of things. I would like to see more subscription based models.

I don’t have to use it much since I can get much of this through current site materials and social media blogs/videos.

I didn’t switch. It was just a matter of seeing where Tableau makes sense as a service to use in our environment, which is for the simpler, not so complex, but quick turnaround. Worked with other technology stacks that are similar, like Information Builders, SAP, Microsoft, Oracle, SAS, MicroStrategy, IBM, Salesforce, Qlik, etc. I find Tableau the easiest on visualization and its license model straightforward. But when it comes to scaling to other interoperation work, not so great on the wizard template, to do data mining/exchange. It doesn’t have that robust analytics and intelligence self-learning feature that comes with other tools.

It’s straightforward, like any typical software. You have just got to understand what the various versions of API and plugins and what they can do. Though I have noticed that there were situations where it was said it was able to do things, but not until a later version. It needs better communication on that front.

License small scale and run with it to get a business case going on its use. Give the licenses only to those analysts you want to do quick turnaround visualizations and those that know the data sources/data (those that don’t will just have access to tool and that compounds a problem with giving you something nice to look at but no meaning behind it, which I wouldn’t recommend). Look to existing platforms of one’s current BI environment and see if you can have a server license which can reduce the per user licenses.

I wish there was more of a subscription model with the pricing when it comes to Tableau, so you can get all the latest version upgrades/features if you pay monthly/annually, rather than buy straight up licenses that you lock to a baseline version and have to pay for upgrades later on. It limits how many users you can get on the thing, and it's not like you will use it all the time.

Yes, we did an alternatives analysis of all the product line options against our criteria of need in our environment, where recurring cost, time to implement, and other interoperation, security, platform scalability, architecture, etc. factors play a role. The majority were mentioned above.

We always have the latest versions of Tableau (part of the package deal), so we can have the latest in APIs and integration hooks and plugins needed across our platforms of OBIEE, SQL, etc.

Tableau is good for quick visualization once you have the data, but not such a great interoperable tool or getting to multiple sources without a lot of work and know-how. Good for pulling in unstructured data and doing quick reports/prototypes. Does require some stronger business analytical skills rather than your novice user (and technical with regards to use of API and plugins).

If new to the analytics/BI market, use it, as it's good for getting you jumpstarted to understanding your data/data sources and to envision what you can use the data for. It's a good starting tool for that. If more advanced or need it for interoperation, I suggest looking to see how it fits with your current environment and determine where best to use it as it shouldn’t be your only option as the features are not robust enough to scale for everything.

The drag and drop in development, design and usage; the Show Wizard feature.

As a BI and data visualization enthusiast and provider, I have compared Tableau against other BI and data visualization tools. It provides tremendous ease of use and a shorter learning curve when compared to the rest. Provides astonishing visualizations as well.

The user interface and ease of use requires a bit of a learning curve to pick up. No drag and drop functionality at the development stage, unlike its competitors.

The data preparation is quite good but not as powerful as the one I use or would recommend for data manipulation and cleansing as well. Tableau seems to focus on the data visualization end and provides, or has partnered with, some other software for data preparation.

Three years.

None at the moment.

Tableau, as noted above, does not provide much in terms of data preparation. Handling of large volumes of data sometimes does not work well with this all-in-one purpose tool making it less ideal for business users.

The Tableau online community is rich and vibrant and provides quick guidance on getting started with the basic use of Tableau.

None.

The initial setup is pretty straightforward for personal use, except when looking at deploying on local premises. Doing so entails some configuration during the installation process.

Tableau pricing and licensing is on the high side for a small company, but it’s competitive among its peers. They offer a monthly subscription for their cloud service.

As a consultant in the area of Business Intelligence and data analytics, I have personally evaluated BI tools such as QlikView, Qlik Sense, Power BI and MicroStrategy.

Invest in the memory and RAM of the PC or server you intend to use with Tableau. Though most database sources are available for connection, you should still ensure you have all the necessary resources and connectors installed for proprietary databases.

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.

Ease of use, speed to develop and deploy solutions. The ability to quickly develop solutions is invaluable. We are managing an immense amount of data and can more quickly correlate multiple data sources to aid in our decision making. We don’t need a large team of technical staff to develop tactical solutions to support executive decisions makers, while also developing robust work queues to aid staff in prioritizing their work or improving health and outcomes of our patients.

We are much more nimble in our ability to provide tactical data and deploy scalable enterprise dashboards/visualizations to staff within the organization. It has allowed non-technical staff to build solutions to aid us in our decision making or improving clinical care for patients across the enterprise.

- More advanced capabilities to format dashboards
- More advanced data merging from multiple data sources
- More advanced management of data extracts

I have been using it for 5+ years.

I did not encounter any deployment, stability or scalability issues.

I rate the level of technical support at medium. Staff are responsive when there’s a major technical issue, but getting answers to trivial questions are handled via forums and can take time or might not match your environment or configuration.

Qlik and Business Objects were used previously. Tableau was selected based on feedback from staff and other peers that are using the product, as well as all of the success we have experienced within our organization. The benefits and speed to develop were key decision factors. Licensure costs was also a key factor in selecting Tableau.

Initial server setup is very easy and does not take a significant amount of technical expertise.

An in-house team implemented it so we could gain expertise in configuration and how to perform upgrades.

ROI is not easy to quantify at this point, but we have gained efficiency through some initial self-serve data and faster time to discover.

Licensing can be expensive, so it’s best to determine scope and implementation plan.

Utilize the trial version and test out functionality of the product. It’s very easy to get started and as you gain proficiency, you will want to learn more and develop more advanced solutions.

Geo Spatial maps and Time Series animation with Storyboarding. The easy to use interface has really accelerated adoption. 

It lets power users like engineers create visuals on their data without waiting on a longer IT project timeline. The downside is you really need some SQL skills to take full advantage of it.

For Tableau, R is just a script interface. It is missing the R-style Plot area the data scientists want to use. They really want to overlay pieces on the plot and derive new graphs.

They need to provide a Folder hierarchy for organizing content and setting security. Creating 900 sites takes way too much work and limiting. As a result, we have hundreds of Worksheets in one long list—not good.

We had no issues with the deployment.

Performance has issues when you get too many users. The latest upgrade made it worse and had to be backed off.

The above issues brings into question how scalable it really is.

I am most familiar with SAP Business Objects Web Intelligence, but have been to classes on Tableau, Power BI, and Qlikview as part of our internal efforts to help the business choose which is right for their needs.

R and SAS are under our heading of Advanced Analytic tools in the BI space and will be evaluated in phase 2.

Plan how you need to organize and secure content up front. It’s too much work later when it becomes popular. Be sure to plan and budget for more client license bundle purchases as user growth approaches critical mass. Otherwise, you’ll be putting them on a wait list and waiting for management to approve more spending. Managing a lack of licenses is not fun.