Tableau Reviews

We use Tableau and Tableau Server for data visualization. We have a Global Tableau Server used by five thousand users on a monthly basis, with fifty thousand email notifications and more than five hundred workbooks for sales, marketing, revenue management, and operations analytics.

The solution has improved the organization by allowing quick access to data visualization and data exploration. Also, it helped to have a global server used as a repository to see all of the reports in the same place.

The features that stand out in this solution are:

• Scheduled extract and the multiple connectors are fantastic.
• Tableau Prep is fantastic.
• Tableau mobile on the tablet is also great for operations and the field users.
• "Ask Data" is good.

The performance could be better. At times, it can take up to one minute or more to open a workbook, which is very frustrating for the users.

Stability is okay, although performance is slow on our side.

Our solution handles five thousand users per month.

Premium customer service and technical support are fantastic. Best in class!

We previously used Cognos and Power BI. We switched because with Tableau, you do not need IT support. Users can create their dashboard really quickly.

The initial setup was a bit complex, more so than expected. I would use a fully managed solution if I had the choice again.

Our in-house IT department implemented this solution.

The initial cost that is set according to CPU cores is expensive. You need to go with at least thirty-two CPU cores for five thousand or more users.

Before choosing this solution we evaluated Qlik, Domo, and Power BI.

The solution is very good. Loved it and the employees also love how quickly they can create a workbook and share it. Overall, a great solution and a great team.

We use it for different groups within the bank. They produce the visualizations they need based on their requirements. It's just rolling out. It's mostly for reports or visualizations - different financial applications. There are also HR applications as well.

For the groups that are advanced, they are pretty happy with the results, and the ones that are just starting the process, the journey, our group is ready to help. There are a lot of learning materials out there to get them trained and try different things out.

In the long run, things should be faster to deployment, to production, than they used to be before.

It's the speed of getting results, based on what the user actually requires. Before it was a very strict, follow the FDLC process. There was a lot of documenting and control that you had to follow before it went to production. We have decreased those a little bit so that they can move to production a lot more quickly than they used to. So time to market, or time to production is a lot faster than it used to be, because of self-service.

It's the ease of use, the quick deployment from developing it and then moving it on to our servers. It's much faster than our Cognos deployment.

It is also a self-service tool so it decreases the burden on having centralized IT-type teams or developers. It has now gone out to the different groups within the bank, and we just have to make sure that they follow certain governance rules so that they don't create crazy queries. It's easier for them. Hopefully, in the long run, they will get their visualization much more quickly. They are closer to the team members that are giving the requirements so they get feedback right away.

I think they have just come out with a tool called Prep, which we just heard about today. It was something that was missing, a little data preparation type of tool. I believe it is an ETL tool but it's not, as far as I know, a robust type tool.

The other thing is a data modeling tool, a little bit more advanced modeling. I would like to see functionality like Cognos has in the Framework Manager.

We are somewhat new at it, so as more and more people onboard to the server, we'll see how that is being managed. Of course, if the performance is slow, we have to find out the reasons, the causes. If it has to do with how they are building certain things, we have to send that information back to them and then they have to correct their models.

We are in the early stages right now. I wouldn't consider ourselves in the middle or advanced stage yet. We're onboarding a number of customers now to our servers.

This is one of the things that we're going to have to address as people start onboarding. It's going to be the challenge, where we have to choose which BI tool to use. Thre is scalability in term of the number of users and in terms of the volume of data. We don't know the volumes of data that we're dealing with. If they're extracting data and putting it onto our server, that all will take up space. Those are things that we're going to discover over time.

Tableau is also improving its product. We're not using the latest version which has some performance improvements. That's because we don't have the hardware to support it. That is something that Tableau will, I'm sure, improve over time as well and catch up with some of the bigger players.

I haven't personally used support but when there is an issue, an internal ticket gets placed and if we can't resolve it ourselves, then we have a platform team. There is a member there who will submit it to Tableau. There have been a few of those.

The switch was driven by the end-user level. We discovered that people were actually getting the self-service tool without us really being aware of it. Once we found out that they were using it, we did some research and looked at the market, saw how popular this tool is and how easy it is to use compared to our existing tool. We said, "Okay, let's not fight the end-users. Let's help the end-users, let's adopt it and help them grow." 

That is how we've moved to this level where we've actually built out of a center practice. We're now a group, not so much of developers, but of people that will help the individual businesses build their own projects.

The most important criteria when selecting a vendor are support, for sure, and their ability to advance in the technology. We have found with Tableau that there is such a community out there. They have a lot of information that is freely available. Those are the main things, support and that they advance their products, that they don't get stale.

I wasn't the one that did the server setup. Definitely, on the desktop, it's very easy to use. And I suspect that the server is also fairly easy. It's pretty straightforward in terms of deploying projects onto the server and promoting it on to production. I haven't heard of any real hiccups yet.

In our case, people were already using Tableau. There are other groups within our company that are using other types of tools like MicroStrategy, and we already had BusinessObjects and Cognos here. But because of the ease of use and the self-service nature of the product we decided, for products in that category, that Tableau was the best.

My advice would be that you should consider Tableau. Certainly, for visualization-type projects, it would definitely be one of the products to look at, and I would recommend it at this time.

Because we are just starting the process, I would definitely give it an eight out of 10. We are getting a lot of good support from the groups using it, but that can only get better as we get more and more groups adopting it, and they are happy. It's really going to be a matter of how happy our users are in building their projects. As that grows, and if their feedback is good, then that will only increase the product's rating.

Tablo has a good community base and we're trying to recreate that community within the bank as well so that different groups of individuals can help each other. That's what we're promoting, and it's working. We have our own intranet site that people can go onto and ask questions and get answers. We also have training and all sorts of different information that's Tableau related.

I work in the hospitality industry and I use Tableau Online and Tableau Bridge with our food ordering company. In our specific uses, I have found that Tableau is very good for extracting data, rather than for working live on the data.

Although the process of transferring data to Tableau isn't the best, once the data is already on Tableau, it works completely fine. I will typically make use of layer aggregation and other operations such as slicing and analyzing it by getting right inside the data in various ways.

Due to the demands of our industry, we always have things that we would like to see more in-depth over different dimensions, such as restaurants, branches, cities, and so on. With Tableau's help, our company can aggregate all the raw data and then analyze by rows, to see, for instance, which restaurant is doing the best by comparing them with one another. It also enables us to easily split areas into zones and use the data to test for not only which restaurants are doing the best, but also where (i.e. in which cities and branches). 

Although Tableau isn't the best for us when it comes to processing and working on live data, it is very good at extracting data for analysis. Once you have extracted the data, the aggregate layers you can create, along with slicing and other operations, are very handy. It allows us to really get inside the data, and it is, in my opinion, better than any other tool I have used with the same pricing model.

Of the best analysis features, multi-aggregation layers come out on top for me, because they let you extract raw details while making multiple aggregations on different time levels and different dimensions, and you still manage to get your work done quickly without having to load a lot of data grouped over different dimensions.

Tableau Bridge is also a very good tool, however I can tell that it does need a few fixes and some maintenance. That said, it's still good for its first few years since release.

Most of the problems in Tableau Online that I have noticed have to do with performance or weird, inexplicable bugs that I can't pin down. For example, you might try unloading some data, and you'll be waiting for a long time without anything happening.

These bugs always seem to happen when we perform big upgrades or do maintenance work, and we have had to send a lot of tickets for unexplained issues during these times. It doesn't seem to be a problem only for us, but also for customers all over the world, such as in Ireland, Western Europe, Eastern Europe, and the US, too.

As for future features, I would like to see major upgrades in Bridge and the Flow Tool, allowing us to do more data engineering work. I think it would give Tableau a big edge in the market to look into how to incorporate more data engineering tools into their product. 

Besides that, I would also like the charts to be more realistic and easier on the eyes.

I have been using Tableau Online for three years now.

The stability is okay. It's not 24/7, but you can say it's stable enough. In the start, it's more stable, especially compared to our OBIEE problems, which have taken two or three days to solve in the past.

It's easy to contact Tableau and ask to increase users or resources. They'll do it in the blink of an eye.

At present, we have 20 users, 12 of which are shift users. The majority of our users in total are board members or high-level managers. 

I wouldn't give their support more than a seven out of ten rating.

I have previously used Power BI, Qlik Sense, and Qlikview. I switched because Tableau was better in working with different sources compared to Power BI which was the only one that was truly on par. Qlik Sense and QlikView were easy to use but didn't have most of the features that Tableau and Power BI offered. Then there's OBIEE which I have used for the past two years, but it is quite difficult for non-technical users.

I also didn't like that Power BI is typically coupled with Microsoft Azure, whereas Tableau works well with AWS and Google which are a lot easier. 

The setup is straightforward. I mean, there's not much setup at all. It's easy for any mid-level user to do it. For example, I just used the documentation they provided and did everything myself. The documentation was sufficient
and the implementation strategy doesn't take more than 20 days.

I implemented Tableau by myself using the documentation they have made available. And for maintenance on one single node, you might need only two to three people involved.

For data extraction and analysis, Tableau is better than any other tool I have used with the same pricing model.

My ultimate advice is that you should know what the tool is capable of first and what your needs are. I think it's better to use the Server edition, and not Cloud, because there are a lot of problems in the Cloud version that don't seem to be present in the Server version. As for myself, I will likely switch to Tableau Server next year after doing a bit more research on how to do the changeover.

I would rate Tableau an eight out of ten.

You can create many different types of visualizations and charts, ranging from simple to highly advanced. And if you're doing advanced analytics, you can leverage Tableau by integrating it with other solutions. You can also do a lot of automation in Tableau, and collaboration is quite good too. 

They currently don't have a great Workday connector. Right now, Tableau can connect to more than 80 different types of databases or data sources, but it's challenging to connect with a few types, like Workday. So if they can come up with a better version or a connector for Workday, it will solve a lot of problems.

I've been using Tableau for around five years.

I can say Tableau is highly scalable. Now that they've introduced Hyper, you can create an extract of more than 5 million rows in minutes and then do your analysis. So that's a very optimized way to analyze a lot of data. That's why many other companies like Amazon use Tableau to create their visualization, reports, and charts, considering that their data volume is very high. 

Tableau technical support is quite good. The Tableau community is also helpful. 

Tableau's pricing structure is unusual. So let's consider all the other competitors in the market. For example, we have products like Tableau, Power BI, Cognos, and QlikView in the data visualization segment. Compared to those, Tableau is quite costly. Their desktop version is expensive, and if you're using their servers, it's even pricier. Of course, they give discounts to bigger organizations. For example, we are premium customers to them, so we are getting it at a different cost, but for an individual, it's costly.

I rate Tableau nine out of 10. I would recommend this to anyone who is coming into the data analytics space. 

Tableau is pretty intuitive. It has a great interface, and you can get multiple visualizations. The best thing I like about Tableau is that you don't have to go for creating; it is calculated free. Unlike Power BI, Tableau has create a calculated column with dimension.

Tableau is quite fast and provides connectivity to 75 plus data connections, which is great.

Also, installation and configuration are pretty fast and seamless in Tableau.

In Tableau, it's just the concept of creating one calculated column and one create calculated free. So, it's pretty simple, and it's pretty easy to locate and work on it.

I have noticed that Tableau is not very compatible with ClickHouse. There's no direct connection to ClickHouse; you have to set up an ODBC connection.

Tableau's performance takes a hit if you have huge data. The stability and scalability could be improved.

I've been working for almost five plus years on Tableau.

Tableau's performance takes a hit if you have huge data. So in terms of stability, I feel that Cognos would be more stable because you can import all the metadata and store it in the Framework Manager. Tableau has scope for improvement regarding stability.

Tableau needs to be more scalable. The performance takes a hit if you have huge data. Even if you take an extract and you publish the extract and schedule it to refresh, if the report has multiple tabs, it can take quite a while to go from one tab to another.

We are going to scale the Tableau server so that it can accommodate more processes and can be more process inclusive.

We have a Center of Excellence team, and anytime we have an issue, we reach out to them. They then raise an incident or a ticket with Tableau technical support. In the case where we had 1 million rows and the Tableau data was failing to refresh, we shared the log with Tableau Center of Excellence. They came up with the findings that it's more of a database issue and not a Tableau server issue.

Installation and configuration are pretty fast and seamless in Tableau.

In general, if someone is new and wants to learn Tableau, it's around $70 per month.

I have experience working with Cognos and Power BI. Compared to Cognos, Tableau and Power BI are pretty fast. Cognos has the concept of Framework Manager where you can build a framework model. Once you build the model, then you have to release the package, and only then is the subset or the package of data available for reporting. Tableau and Power BI eradicate the dependency on a framework model.

With Cognos, installation and configuration wise the setup takes a bit of time. You have to install and configure and then make the data available. After that, you can do reporting. Unlike that, Tableau is very quick; you can just directly connect to Excel or a file on your desktop.

The connectivity, installation, and configuration are pretty fast and seamless in Tableau and Power BI, unlike those in Cognos.

From a license perspective, I think Cognos is the most expensive, then Tableau, and then Power BI.

If I were to rate these solutions on a scale from one to ten, I would rate Power BI at 7 and Cognos at 8.

You can do a lot in Tableau, and on a scale from one to ten, I would rate it at eight.

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. 

The best feature of Tableau is the usability. It allows a business user to prototype something without going through IT and spending a lot of time and money trying to translate requirements. Going back and forth, it allows you to rapidly develop things that you need to analyze the data.

I also think, as I used SAP Business Objects before that and built out the reports, that you can build almost anything you need to, but it's a little more complicated. I would say Tableau is more like Apple, it just looks nice. It formats things. It just gives it to you in one way, as it assumes that is the way that everybody wants it. So it might be frustrating if Tableau doesn't give it to you the way you want. But usually, it does give it in a good enough way. So, it saves you a lot of time.

The thing I don't like is the refresh feature . The users can have the web page open and there's a browser refresh every 10 minutes, or every 30 minutes, whatever IT sets as the refresh rate. You lose everything when you have to refresh the page and it may take 30 seconds. So, that's a bit frustrating too. You expect that when you leave a browser page open, you want it to keep that view there.

I don't know if they've introduced it yet, but there is a connector with Smartsheet. We use Smartsheets a lot for project management and capturing data. We are basically exporting from Smartsheet to Excel, and then creating in Tableau and publishing it. I believe there is a connector now but I haven't actually used it. So that would be one useful feature; more connections to data sources.

I would sometimes get an error message stating “out of system resources”, but when I checked the RAM/CPU utilization in my laptop, it would appear that I had lots of resources.

Cisco has been using it for about seven years. Teams that I work with use it. I've contributed to the requirements of building dashboards with IT. I have been using Tableau for about three years.

There can be a memory hang, or the memory crashes sometimes. I'm not sure, but generally it's stable. I think it's when I build complicated data sets, like too many worksheets or too many reports in my workbook, this can happen, which is annoying.

I don't think it is scalable because of the stability issue. I think it's good for desktop; it's quick and dirty, making things you need, and you can actually build some really nice dashboards. It's scalable when you publish it to the server. Also, you can have as many users as you want accessing it. I have some impressions on how much data you can actually use at a time when you're building your reports.

I think technical support is OK. We have in-house support at Cisco, which is a team that supports Tableau as well, but we can go outside to Tableau support itself if we need help.

I found it easy to get started. I'm more of a technical user with an IT background, so I had no problems setting it up.

Get started using Tableau as it serves the purpose for most people.

There are many use cases and many projects. I have done level cost-based analysis and forecasting. I have also created one dashboard for their risk measurement.

The most valuable feature is the analytics part. You can use simple reporting by using the analytics by businesses and stakeholders, giving insight into that particular information. It also has data cleaning. It saved a lot of time for the application.

Every time, they create a new version of Tableau. We need to update that version and create a new EXE file. Any visualization tool should have one particular application that only needs to be updated rather than creating version one, like 2024.1.2. You can see many versions in Tableau. We are using this number of versions only because of the latest update. Having the latest update in the same application rather than creating multiple versions would be good.

Some features are not enabled in Tableau. We can use the measure or Python to use that. Every company or every person has its own requirement in Tableau. Suppose I am using an in-date format. I'm extracting data from my data source in some other format. At the same time, businesses want to view this information in different formats. I have to customize the data format. If possible, you and your team can work on the date format in the world. There are multiple data formats or data get views available in the report. You can also accommodate in the gate feature.

I have been using Tableau for almost five to six years.

Tableau is scalable software. Almost 500 people within my department are using this solution.

During COVID-19, I had a few issues. Our support team didn't support it. We requested to the Tableau team, and that person helped us. Sometimes, they have a good way. They have excellent knowledge, but sometimes, when you provide your source to a third party, they don't support us technically. 

Neutral

The initial setup of desktop application is straightforward. We can also creat,e athe  project and tha e end user can also create their own project, and later on, if needed, the support team can help us with that.

It takes ten working days minimum to complete creating dashboard and setup server. We are using dev and production environment. Some are also using a testing environment based on their requirement and discussion with the business.

Multiple team is responsible for their activities. At least five to six people are required  for deployment because someone is creating a dashboard and some person has their knowledge at server level. Also, some person needs to have their permission management.

I was not able to connect Tableau with SharePoint online. Microsoft Power BI can be connected with SharePoint easily online. It has many more facilities than the Tableau application.

The daily reporting or data analysis has seen many improvements with Tableau. We can manage the users accordingly while doing the drag-and-drop interface for that application. I can do Python and use Python Tableau language, which creates a lot of differences.

I have never faced any issues with data integration. Before COVID era, I was using SAP HANA. I could use it very frequently and use the data set accordingly.

I have never faced any such issues. It is a very good application. There are many more application which are also very good and very user friendly, but Tableau is also one of them. We have a direct live collection. Even business users share experience while connecting the live. They feel some hindrance while using this application when they refer us or when they filter the data. You can see the delay response in the page.

Tableau is a good application. I created any dashboard for the business, and business want to view their information. They like that application, and they are able to on this side, what purpose they wanted to develop this dashboard.

I recommend Tableau. There are many BI application. Tableau is one of them, as I I use it.

If the user want to view information, it would be go with their line. It is simple to understand, and take decision based on their information. It would be very good to have on comparison heat map or the pie chart.

Overall, I rate the solution a nine out of ten.