Tableau Reviews

I use Tableau to understand how day-to-day business is going, where the gaps exist, and the KPIs. We use it for target analysis, identification, performance tracking, and financial metrics. 

The features I like most are data manipulation, Tableau Prep, the ability to do manipulation on the desktop, its connectivity to different and vast data sources, the capacity to handle such huge sets of data, and its flexibility to play around and create calculated fields and customized charts. LOD expression is also fantastic. Another useful feature is drag and drop, which means that if you're not into creating data manipulation, data comes very clean and clear to you, and you just have to drag and drop to create a job. This gives an upper hand to the end-user to work on the analytical tool and create their own dashboards. And even on the Tableau server, they can create their own metrics and publish them as a simple dashboard. You can create a view as per your user.

When you're working on a dashboard, you can't select multiple components at a time and align them, so you have to go one by one. This is very cumbersome if you're floating, and it loses in comparison to Power BI, which does allow multiple selections. In the next release, I would like to see an enhancement of the prescriptive analytics features.

I've been working with Tableau for a year and a half.

Technical support is more-or-less fine, though we have had a couple of cases where we weren't satisfied. We have had to ask our senior management to intervene sometimes because the support period has gone beyond fifteen or twenty-one days.

Neutral

I consider Tableau to be the best analytical tool available. It's really handy to use and can be used by non-technical people. For those thinking of implementing it, you can go with Tableau Online if you don't do too much data manipulation on the Tableau desktop itself. Try to keep it in a different layer of Tableau Prep and also make sure that your desktop is not heavy and leverage the features properly because Tableau offers a lot. I would give Tableau a rating of eight out of ten.

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.

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.

It was used for a project in the capital finance domain. We used it to develop the dashboards. My role was to plan the development activity and prepare the dashboards.

All features are valuable. It is very user-friendly, and it is mostly drag-and-drop. If we have the dataset available, then we can develop any dashboard very quickly.

SAP BusinessObjects has some semantic layer designs that give the flexibility to do ad hoc reporting or dashboard designing. If that can be brought into Tableau, it would be great. We have the data in the database, but we should also be able to bring something between the database and the dashboard and do some semantic layer modeling for ad hoc reporting requirements.

I have been working with this solution for almost three years.

It is very stable and user-friendly. It is overall good.

I have used it only for building dashboards. I have not used it much for other areas, so I don't have any inputs about its scalability.

Its users are from the finance department. There are more than 20 people for that project, and they are using different dashboards. Its usage would expand in the future. They have a plan to also use it for machine learning. I am not sure if that would be a different team or if we will be involved, but machine learning is coming into the picture in the future.

We have experienced people in the company. Whenever we are stuck with something, or we want to achieve something new in Tableau, we consult each other. We help each other, and we get the solution. There is also a Tableau community where we can get help.

For any technical support, Tableau administrators raise the ticket and get the answers from Tableau's support team.

Initially, they were working on SAP BusinessObjects, and then they moved to Spotfire. After that, they moved to Tableau. SAP BusinessObjects was more for reporting purposes, whereas Tableau is for dashboarding purposes.

We also liked Tableau, and that's why we moved to Tableau. It is more user-friendly, and it is also better than SAP BusinessObjects in terms of look and feel. 

Its initial setup was easy. The time taken to develop dashboards depends on the dataset that we want and the data source with which we have to connect. We have to create the data source and the dataset, and then we have to develop the dashboards. If we have the datasets available and we have an understanding of the requirement, then in one or two days, we can develop a dashboard.

For its maintenance, there are three people in a project, and we are able to manage their requirements. There is one administrator and two developers.

If you have more ad hoc requirements, then I would recommend evaluating other BI tools as well. If you have fixed requirements and you know what type of dashboard or reporting is needed in advance, and it is not going to change very frequently, you can go for Tableau. It is very user-friendly. If product owners or users want to go for a self-serving tool, Tableau is the best option.

I am satisfied with it, but there is always a scope for improvement. This is a competitive market, so there will always be some scope for improvement. I would rate it a nine out of 10.

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.