Tableau Reviews

We are consultants and implement this solution for many of our clients. We had a project for a telecommunications company, where we extracted the transaction data and helped them to find some trends and improve analytics. They were able to gain knowledge from the data and to see some KPI indicators and build some dashboards for commercial, risk and financial purposes. We help clients connect their raw data, prepare and clean, and generally carry out the cycle. We then help them to extract insights, trends and work on forecasting so they can visualize their indicators in dashboards or in some ad hoc analysis. I'm the general manager and we are partners with Tableau.

The solution has several platforms or tools to visualize data and to build dashboards. The Tableau Prep tool is great for data preparation and this is the most valuable tool for preparing data, cleaning and building data models or data warehouses. The main issue, and most companies have the same problem, is updating data, which they can do with Tableau Server, where you can synchronize data to automatically refresh daily, weekly or monthly. It means your dashboards and KPIs will be updated. Most people know Tableau because you can build beautiful dashboards, but the main beneficial features are behind the scenes.

The product could be improved with more features in data analytics. Tableau is not currently a good database for handling built-in models for data science in order to test, train and run the models. It's not currently an AI tool or a tool for machine learning. Right now it's more for non-expert users. If they could improve their analytical capabilities for data science tasks, it would be a better product. In order to carry out data science tasks now, we have to use Vertica for big data projects to discover and run machine learning models. It would be very good if they had their own machine learning capabilities built in. I'd like to see more features in data analytics, AI and machine learning capabilities.

I've been using this solution for the past 12 years.

The stability is good, we haven't had many bugs. They provide many updates every week and we don't have problems with Tableau in general.

The solution is scalable, we have around 15% of our clients that are large scale businesses with the majority being small companies. We provide support for our customers.

Two or three years ago, technical support was very good. I think that now there are many more users of Tableau, the technical support is not as good as it used to be, particularly in terms of the depth of analysis. It's more general these days. You can buy their professional services in order to get better support.

Most companies find it very easy to implement Tableau and to make an impact with their data because it's very easy to install, to learn and to start using. For larger companies we combine Tableau with other solutions, such as Vertica or Alteryx or Hadoop or Python. That's a big project but most companies first need to solve their self-service BI. They need to find insights into their business and with Tableau it's very easy to do that. In minutes, you can gain many insights and discover knowledge without being an expert of business intelligence, let's say. Deployment takes an average of two months, it depends on the size of the company.

We are always evaluating this solution in relation to Microsoft Power BI and QlikView. Power BI requires knowledge of numerous other Microsoft products in order to get results from your implementation. You need an expert DBA that can handle it in cloud and many specialists to implement the Microsoft solution. People think that buying or using Power BI is all that they need to do, but that's not the case, Power BI is just the last step of the implementation. A lot needs to be done before implementation. It's the same when it comes to automatizing the data refresh. Tableau has just three products and you don't need much time to learn and to finish a project and be up and running. QlikView has less tools and less features for data preparation. Vertica is another database that handles built-in models for data science and for the data scientist, this is a good choice in order to run, test and train the models.

It's important to understand your needs because if you only need to build dashboards, Tableau is not essential. But if you need a deeper business intelligence project, and you have higher expectations, Tableau would be the solution. If you only need to build some dashboards, you can use Power BI, it's a very good tool and it's cheaper. If your project is more ambitious then go for Tableau. Tableau has a lot of experience and can solve all the typical problems. I rate this solution a nine out of 10.

We use it for consulting and teaching purposes. We teach data science, and we use a data science tool. Most of the time, we use open-source tools, but we have also started to use some proprietary tools, such as Tableau. We will also explore other data science tools such as SPSS from IBM and SAS. 

We are using Tableau to teach students. We create a story on the data and teach them what kind of visualizations are more appealing to clients. The focus is on storytelling and what kind of visualizations are relevant for a specific business situation. 

We are using the latest version of Tableau, and it is deployed on my desktop.

From the data science point of view, we use it for model building purposes. For example, if we are using it for a bank and we want to understand how much loan the bank can provide, we can use visualization to show the educational qualification, salary, gender, and city of a customer, and by using this information, we can arrive at the loan amount that this person is eligible for. I can also use it to view all prospective customers, so essentially, this is going to help me in model building as well as in understanding and segmenting customers and doing forecasting and predictive analytics. 

We use model widgets, and we can create thousands of visualizations, such as motion charts and bubble charts. We can also create animated versions of the graphs and view the data from multiple dimensions. These are the features that we typically use and like.

I have used Power BI as well as Tableau. There are a couple of interesting features that I like in Power BI, but they are not present in Tableau. For example, in Power BI, if I am looking at country-wise population, I can type and ask for the country that has the maximum population, and it will automatically give an answer and address that query. This kind of feature is not there in Tableau.

Similarly, in Power BI, for integrating with the latest ML algorithms, we have decision trees and primarily multiple machine learning algorithms. The decision tree essentially visualizes the patterns in the data. We don't have such a feature in Tableau. If Tableau can integrate with the machine learning algorithms and help us to do visualizations, it would be a wonderful combination. Most of the people are going for Tableau primarily for visualization purposes. However, in the data science industry, users want to do model building as well as tell a story. As of now, Tableau is fulfilling the requirements for visualization purposes. If they can bring it up to a level where I can use it for machine learning purposes as well as for visualization, it would be very helpful. Many people who want to do data science don't want to write a code. Tableau is anyway a drag and drop tool, and if they can provide those options as well, it will be a powerful combination.

I have been using this solution for four to five years.

If I'm going to upload data of more than 10,000 records, then it might be unstable. With 10,000 rows and more than 100 columns, it really becomes shaky. However, this could also be because of the local infrastructure. For example, if I am using Tableau on my local machine with 4GB RAM, it might not be suitable.

There has been no need for scaling. We are actually connecting to an Oracle Database or SQL Server database, and we can take whatever data we require. We have 40 people who are using this solution in our organization.

We never took help from their technical support. We have experience in data science, and we know what kind of configurations are typically helpful. 

Its initial setup is very simple. We get the Tableau license code, and with a couple of clicks, we can set it up. It doesn't take more than two minutes to install it on our machine.

We implemented it ourselves. It doesn't require any maintenance for the purposes for which we use it. We use it for consulting and teaching purposes.

Its price is reasonable. Everything is included in the license.

Tableau is a wonderful tool, but you should know the proper methodology of using it and the specific situations for which it is helpful. This is very important. For example, we can use a knife to cut vegetables, but it can also cut my hand.

One should be able to understand the visualization that you are constructing in less than 30 seconds. Otherwise, the visualization doesn't meet the purpose. This is the benchmark that I have set myself.

I would rate Tableau a nine out of ten.

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.

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.

We use the most recent version. 

We use the solution to engage the field teams and we integrate that with the data warehouse data and build the dashboards for them.

It is helpful that the solution provides access to one's own data. It allows a person to get insights out of the data provided by his tool, based upon the KPIs that the person wishes to look at. It all depends upon different use cases. We have dashboards for marketing people, field teams and executives. It all depends upon which insights a person wants, in which case he can prep the data accordingly. This is good. 

The most valuable features are the visualizations, the way they show the combination charts. This allows a person to jointly put in different measures in different axes and greatly facilitates the user in understanding the data better.

There should be a focus on memory data, which is the concept of Tableau. This is where they squeeze the data into their memory. Because of that, we see performance issues on the dashboards. The architecture should be improved in such a way that the data can be better handled, like we see in the market tools, such as Domo, in which everything is cloud-based. We did a POC in which we compared Tableau with Domo and performance-wise the latter is much better.  

As such, the architecture should be improved to better handle the data.

We are seeing a shift from Tableau to Power BI, towards which most users are gravitating. This owes itself to the ease of use and their mindset of making use of Excel. Power BI offers greater ease of use. 

For the most part, when comparing all the BI tools, one sees that they work in the same format. But, if a single one must be chosen, one sees that his data can be integrated at a better place. Take real time data, for example. I know that they have the live connection, but, still, they can improve that data modeling space better.

We have been working with Tableau for almost seven years.

The solution has pretty good stability. It's a robust tool, even though it has a steep learning curve. But, still, I feel that from the stability perspective, it's a leading BI tool in the market. It's pretty stable.

I personally don't like any BI tool to have that scalability. What we usually do is integrate scalability into our warehouse layer. We know how to scale up and down and we handle it there. We don't rely much on the BI tools to do that.

I am talking about the scalability of a program in general, be it in its relation with users or as it concerns dashboards. 

We recently started working with Tableau online and that particular solution is scalable. It ingests the hardware, the server capacity by itself. So, if users go from, let's say... 100 to 500, we don't see a dip in performance. It still behaves the same. Because of this new integration technology with the cloud, they are scalable in that regard.

We are in contact with technical support. One service we have is Tableau online. If we see a dip in performance, we raise a ticket to the Tableau support team, work with them and make certain they address our issues. I would rate my experience with them as three out of five. 

We used Tableau from the get go. 

While I was not directly involved in the setup, I know that it's not that easy. There is a need for a proper administrator who has experience in that field.

We used an integrator from Tableau when implementing.

Our experience was good and we were assisted with our implementation requirements. They were able to make notes to match our use case and answer all of our questions, including those concerning the number of users we have and how to set up the server.

I'm not part of the administrative group which handles the setup. I am mostly a consumer and responsible for building the desktop. I use the desktop version to build the dashboards and am not responsible for the server health check or maintenance. As such, I am not in a position to provide information about the staff required for maintenance, updates and checkups. There are a couple of people who are responsible for this, one from the customer side and another from our team. Both parties are in sync when undertaking these activities. 

I have no knowledge concerning the licensing costs of Tableau. 

The solution is mostly deployed on-premises, although we have also done cloud-based deployment. 

We have around 500-plus users making use of the solution and mostly 90 percent are viewers. We have very few creators or explorers. Creators comprise seven percent and explorers three percent. 

My advice to others would vary depending on their use cases, what they're looking for and the level of competency they have within their organization to use it. Tableau has a steep learning curve. So, it depends upon one's use case, the reason the person is going with that specific BI tool. The procurement department would need to evaluate the use cases very carefully, because there are so many BI tools available in the market. One's focus should be more on a centralized tool when bringing a new one to his organization. It should address all the answers to one's users, like what they're looking for. Definitely Tableau is good in the data discovery part and it can handle large data sets. So, all of these things should matter when one is trying to evaluate a tool.

I rate Tableau as a seven out of ten. This is because we are using it and it has a steep learning curve. It's not user-friendly. One must build a competency in creating the visualization and then support it. All of these things matter when one is evaluating a tool. That's why a shift is going towards Power BI.

We primarily use the solution for HR and energy dashboards. We have made and a few other dashboards for opportunities and accounts. 

Tableau has really good and interactive visualization and interactivity. 

The action feature which Tableau has is very useful for us. If we click on one visualization, it will pass the value to another visualization. That interactivity within different visualizations is the most valuable feature of Tableau.

Tableau would be really good if we could have predefined templates. I was doing a POC another newer tool, Einstein Analytics. They have predefined templates already set up. These predefined templates do the heavy lifting for the initial dashboards. We don't have to build them from scratch. Our dashboards look really good and 20 to 30% of the look and feel of the dashboard completes with the predefined templates. If Tableau works on the predefined templates, that would be so helpful to a lot of companies. It would save time for the developers.

The pricing is a bit higher than the competition. They'll need to lower it to stay competitive.

They need to move more into machine learning AI. Right now, in a POC that I'm doing with Einstein Analytics, they are more into machine learning and AI. Tableau is lagging as of now. If they want to have a long run in the market, they need to integrate machine learning and AI. It has to be very robust.

I've been using the solution for two years now.

I can say that this solution is quite scalable. I'd rate it eight out of ten. It integrates with many solutions. I haven't used our code in everything, however, I have used it for our HR integration and I find the code is quite scalable. 

In the last project I managed, there were 110 regular users of the solution with about 20 suer-users that were able to edit reports and dashboards and tasks of that nature.

I'd give technical support a nine out of ten. There were time zone differences. However, I got a timely reply and call from them, so it was very good. The support is very good. It's both responsive and helpful. I'm quite satisfied with them.

I've used BusinessObjects and MicroStrategy as well. I used to use Power BI for a few months.

The main competitor to this product is Power BI, which I also use. This license is a bit costly compared to Power BI. Not more, but a bit costly from what I've seen.

There's not much of a difference between Power BI and Tableau. They have the same kind of interface and the features. The main difference between the BusinessObjects and the others is that its an enterprise tool. The licensing cost of the BusinessObjects is very, very expensive. The visualizations and objects etc., all have separate licensing for dashboarding. On top of that, the chart's not that interactive. If you click on one chart, it will automatically change the data of the several other charts related to that. That is not very interactive compared to Tableau or Power BI.

The initial setup was not exactly straightforward. It was complex, or, to be fair, had a medium amount of complexity to it. The use cases were complex also and few of them had medium complexity to begin with.

Deployment was basically in the development environment and then we deployed it in the UAT for the users. They had a look at our reports in UAT first and then we deployed it into production. I was also working as a Tableau administrator also and then I learned Tableau administration in order to handle that aspect. I handled Tableau administration with the user and deployed the reports, etc.

The company has different verticals basically. I worked on HR and energy verticals. They also had finance and accounts. I have to maintain that administration part for all of them; not just my dashboards. From a deployment perspective, it was tough for me to maintain all the users and all the rules for the accounts department and for finance, and to be in the group for them, and to assign permissions for them. I did have issues in the servers in terms of gateways. I resolved them myself in the end with the help of Tableau support. 

I handled the implementation myself. I didn't need the assistance of a reseller or consultant.

The company chose to purchase a creator license for me, which was $70. With that license, you can also be an administrator. We also have 20-25 extra licenses and they cost around $20-$35 each. Those are for normal users who will be viewing the dashboards. Those are monthly charges.

There aren't any other costs over and above that.

Apart from that we had database licensing. So because we used Snowflake as a cloud database.

Our company has a partnership with Tableau.

I've used both on-premises and cloud, depending on the requirements.

This particular solution is quite an easy to use product. It's very robust. Even a layman who has previously was not worked with any other BI tools would love to work on this. They will find many things easier to implement. There are a few other tools that are in the market. However, from an implementation point of view, it's very robust. 

I'd rate the solution nine out of ten overall.

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.

My primary use case of Tableau was in conjunction with Salesforce to enhance my data visualization and analysis capabilities. 

Tableau empowers users to delve into their data deeply, effortlessly unlocking valuable insights and critical information. It's got a user-friendly interface and extensive visualization capabilities that allow me to create compelling reports and dashboards that help me make better decisions. 

Tableau has greatly enhanced our organization's data-driven decision-making processes by enabling us to create visually compelling reports and dashboards.

This capability has led to better communication between teams. Despite some limitations associated with custom data connectors, Tableau has proven to be an invaluable tool for aggregating data from multiple sources, offering us a complete view of our operations. 

This comprehensive perspective has allowed us to identify trends, make informed decisions, and optimize our strategies, ultimately leading to increased efficiency and competitiveness in our industry.

The most valuable features of Tableau for our organization have been the interactive storytelling capabilities and easy integration with Salesforce. We can create visual descriptions of data, making complex information accessible and interesting to all sides. This feature enables us to effectively communicate data-driven insights. 

In addition, our integration with Salesforce provides access to critical sales data in real-time, which facilitates quick decision-making and improves customer relationship management. Together, these features enable us to get the most out of our information assets, which helps us to develop informed actions and strategies.

Tableau is excellent at visualizing data, however, I think improving the data preparation features would be a great addition. Navigating through activities like cleansing, reshaping, and wrangling extensive or complicated datasets could prove challenging within the Tableau environment. 

Also, the settings for working with complex datasets also need to be changed. In the next version, it would be good to add user-friendly resources for beginners, such as interactive tutorials and templates, to make Tableau even more accessible to a wider audience.

I've used the solution for more than three months.

I used a range of solutions to find the best one. I haven't switched; I'm using them at the same time.

I evaluated QlikView.