Tableau Enterprise Reviews

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

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

I really like the interactivity of the dashboards.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It is usually used to visualize how the data looks. It is used for drawing charts and different types of visualizations. You can visualize sales, profits, and metrics by geography, product categories, and so on.

I'm using the 2020 version. The latest version came out in 2021. I've not downloaded that one yet. I'm using the last year's version.

It is easy to use, and it can handle a large amount of data.

An advanced type of visualization is a bit tricky to create. It has something called a Calculated field, and that sometimes gets a bit difficult to use when you want to create an advanced type of visualization.

I've been using this solution for five to six years.

It is stable.

It is scalable. We have around 10 users.

I have not contacted their technical support.

It is just a matter of downloading the file from the internet and installing it. That's it.

It is pretty simple to use. We don't require anyone for its deployment and maintenance.

I believe it has a lifelong license, and once you purchase it, you don't have to renew it, but I'm not sure.

I would recommend this solution to others. I would rate Tableau an eight out of 10.

It was for dashboards. The key use case was for creating visibility to performance metrics for the leadership team. It was the most recent version, and it was deployed on-prem. 

The key use case that we were going after very specifically created visibility to performance metrics for the leadership team. So, it allowed us to give that common view of performance metrics and drive business conversations based on the common shared set of facts. We were able to expose data and relationships that we otherwise couldn't do in our enterprise system silos. From that perspective, we were incredibly successful in really driving performance. When you combine that with some real championing in the business and with some leadership to push it down, the fact that it was Tableau wasn't as relevant as the fact that we had the championing pushing the process and pushing it down.

The number one thing was just the ease of getting something up quickly. The other thing that was good about it was that it was fairly fast for decent-sized data sets in terms of performance and run time.

From a downside perspective, some of the more advanced modeling techniques are actually fairly difficult to do. In addition, I just fundamentally disagree with the way you have to implement them because you can get incorrect answers in some cases.

One of the key challenges is that you never know whether it is how your developers developed it or whether it was the tool. We did find that once we got into more complex models, the ability to keep objects that should tally the same way but didn't became more and more difficult. That was probably the big thing for me. I don't know enough about how the tool was developed to know whether that was because they didn't follow a recommended practice. That was probably the number one thing that I found frustrating with it.

When we started to try and get into some very granular data sets that had some complex relationships in them, the performance on it degraded pretty quickly. It did degrade to such an extent that we couldn't use it. We had to change what we were trying to do and manage its scope so that we could get what we wanted out of it or reduce the scope of what we needed out of it. It doesn't have a database behind it, per se. So, while doing some of the more complicated things that you might otherwise do on a database, we started hitting some pretty significant challenges.

I used it for about three years.

Tableau worked fairly well for straightforward data sets, but it struggled when we got into the more complicated data sets and larger data sets. 

We were able to deploy it fairly broadly without a whole bunch of work. From that perspective, it worked fine. I was deploying my stuff to about 200 users across Canada, and I don't think we saw a blip on the server when people logged in. It was fine. If we were to roll out some of the bigger applications broadly, like the ones that we were having performance challenges with, we probably would have crushed the box. We would have had to get more CPU. Most likely, it would have been a memory issue, but we never hit that inflection point.

There were about 200 users of the solution. It went all the way from the equivalent of a senior vice president and all the way down to the equivalent of a line manager. So, we had business unit leaders, vice presidents, and operational managers.

It was being used extensively for a specific use case. There were lots of other use cases that it could be used for, but there needs to be an appetite from leadership to go, drive, and commit resources to go do that.

I didn't have to deal with technical support. Mr. Google is pretty good on the topic.

We had previously used Cognos to do the exact same thing. The only reason why we replaced it was that the business decided to go towards Tableau. Otherwise, there really wasn't any real reason to replace it. It was probably a little bit easier and more interesting for people to learn and to develop applications in the program than in Cognos. The ramp-up time to get to reasonably proficient in Tableau plus the support through Mr. Google made it a lot easier for me to get resources and do development on Tableau as compared to Cognos.

The organization decided to move away from the old platform. So, basically, I was lost when they asked me to shift off so that they could shut it down. I personally prefer the previous platform. I understood it very well. I had used it for years, and it worked just fine. For the most part, the challenges that we had on the old platform were not resolved by Tableau, which just reinforced to me that it wasn't a tool problem. It was a people problem.

It was pretty straightforward. The big thing that confuses people in a project that involves Tableau is that Tableau is a very visible but small component of the overall solution. That's because 80% of the work is data. It is not Tableau. So, Tableau is actually a fairly small component over that overall solution. It took a few days to get it up and going. Almost 80% of the work is actually on the data side, which takes forever, but the actual Tableau component of it was pretty straightforward. It was not that difficult.

You can get a Tableau dashboard up on a weekend. It is not hard to get something up and running. It is pretty trivial. It isn't any more or less difficult than any other tool to get up and going. I've used a number of them, and they're all pretty easy to get up and going. Tableau was the first one out of the gate with this democratized data perspective, where they were going to do departmental BI and up to enterprise BI years ago. Now, they now charge a fairly hefty premium to leverage that product. It is not a cheap product.

In terms of maintenance, it can take as much or as little as you want because it just runs. So, technically, you don't have to have anybody to do very much. You just need a very skeleton crew to operate as is. The challenge that you run into with solutions like this is that you need to continue to refresh the information with new and different views because people want to know more, and they want to go deeper into it. It is not a function of the technology. It is a function of the use case. So, you tend to have lots of new requests for new reports and analysis, and that's where you tend to have more challenges.

We didn't get into analysis users who are able to sort of do a little bit more themselves. There were viewer licenses where you are just using preset reports, but there are obviously additional training and things like that, and you have to deal with it if you start getting into more advanced power users.

I was at another company, and we were the integrator.

It is fairly expensive. I have no idea what they paid. We were on an enterprise license, so whatever it is they licensed at the enterprise level is what we paid.

A good chunk of it has got nothing to do with the tool. It has everything to do with your leadership and your governance requiring it. We had our IT team roll up Tableau multiple times and not a single person used it because there just wasn't enough leadership support to use it. There is nothing wrong with the tool, and it worked fine for what it did, but every time I logged into it, I go, "Okay, but what did you want me to actually do with this? I see all this information. I understand it clearly. I'm not sure what I do with it though." So, without that additional guidance from leadership, rolling it out is irrelevant. You need to have that strategic leadership associated with it.

The key piece of advice would be that you got to look beyond your tool. You need to look at how you're going to get this information used in your organization. What kind of leadership support, governance support, and ongoing support are you going to have? It is all based on trusted data. The value of the tool is based on the quality of your data and the leadership's support to use it. So, if you don't have high-quality data and you don't have leadership support to use the data, you don't need any tool because nobody is going to use it.

I would rate Tableau a seven out of 10. It suits the purpose, but in and of itself, I don't think it is significantly better or worse than its key competitors.

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.

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.

We just created the data visualization and analytics models and the complete setup file was sent to the client for deployment in their own premise as that was their client policy. We just created the solution and the solution was transferred to them.

We were trying to create a dashboard for the contractors in an oil and gas plant. It's for a million-dollar company. That was the level one priority. The use case involved a pilot project meant to drill down the visualization of each contractor and the sub-KPIs at a sub contracting level, plus the geographies involved. Tableau is a data visualization tool with analytics involved in it. The art was to create a set of dashboards that were geography-specific plus contractor-specific. Along with that, there were some common KPIs against which the visualization was supposed to cover as well.

The best part about Tableau is the visualization. 

Tableau has some amazing features. We can have some additional UI features that act like a skin. You can get it to really customize to your needs and then you can incorporate items as a plugin in your Tableau version and the user interface.

The graphics are quite good.

The solution can scale.

Technical support was helpful.

We found the solution to be quite stable.

Tableau is good, however, it lacks a bit on the integration side if you compare it to Power BI, for example. Power BI has quite a good amount of connectors. Even though Tableau does have some, Power BI works well with the Microsoft environment and most of the firms are in granular detail. That's where Microsoft shines. Maybe Tableau can collaborate with other bigger, well-recognized solutions in order to get an edge in the market the way Power BI does with Microsoft. 

The pricing is a bit expensive.

There's a bit of a learning curve for those new to implementing the solution.

We've been using the solution for less than a year so far. In the last eight months, we've developed and deployed the solution. We haven't been using it as long as, for example, Power BI.

It's a reliable solution. There's no doubt about it. The solution is stable. It does not crash or freeze. There are no bugs or glitches.

For deployment, the capacity was increased. It required really strong contractors also. That was easily manageable. It is quite scalable.

We had a team of around 20 developers who are working on the solution.

We only dealt with technical support once or twice. There wasn't a lot of interaction in the time we used Tableau. That said, they were always helpful and responsive. We were happy with their amount of assistance. 

We are also using Power BI. We use both products currently.

For us the solution's initial setup was complex as we were just aware of how to use Power BI previously. It was a completely new solution. We had to get an instructor to get the things in place.

I'd say it was about an eight-month installation process for the particular software. In-app work was one month and then seven months of development everything and all over the board.

We had an instructor assist us in the implementation process. and show us how to use the product.

We bought a monthly license as we were not able to continue with it long-term. It was simply a specific client requirement that was not needed forever.

The pricing of Tableau is a bit on a higher side compared to Power BI, however, for us, it didn't matter much as we were charging it to the client. That said, for a normal end-user, it would be considered a bit pricey compared to Power BI.

For Tableau we have been using only for a specific team within that also was for the external clients. That experience was only around eight months. However, it was a pretty good experience. Up to that point, we had been strictly using Power BI. Adding Tableau was for a specific client who just wanted the Tableau licenses created and developed for them.

We were using the latest version. It was not a cloud version. It was the desktop on-prem version we were using.

The solution would work well for small, medium, or large enterprises. It caters to all different sizes of companies.

I'd rate the solution at an eight out of ten overall. We've mostly been quite happy with it.

We do tons of data analysis for the organization to try to plan for resources that are needed in the infrastructure, specifically servers, storage, and that kind of stuff.

There are about 30,000 devices that we have to manage. We need to make sure that we have what we need for our internal customers, which is a really tough task unless you can analyze data every single day. We look for all sorts of anomalies about how the devices are functioning or how they are growing in consumption. We pull up about 1.1 billion rows of information every single day about what they're doing, and then we've got to take that mountain of data and pick out what we are concerned about. Tableau is key in providing that information to our internal customers and just analyzing different sets of data, such as the asset data and the consumption data. It is just data analysis all day long. That's basically what we do.

The data visualization piece is most valuable. We do ad-hoc analysis or one-time shot things, but there are things that we have to track every single day. When our management and our customers want to see how things are changing, the dashboarding provides that information. Tableau is key in providing that data on a refresh basis. We use a data blending tool that pumps the data into Tableau, and we just schedule it to run every single day. So, the automation of the data and being able to present it to people who are interested are the most valuable features.

Its price is a concern. It is more expensive than Power BI. The other thing that I never liked about Tableau is its ability to handle large sets of data. To present the data in the dashboards, we have to stage it up exactly like it is going to come into the dashboard. We use another tool called Alteryx that does that for us. So, we manipulate the data, get it staged, and then push it into Tableau. Tableau is terrible at handling large data sets, and we knew right away that we couldn't use Tableau to do data manipulation.

I have been using this solution for ten years.

It is a stable and mature product.

It is able to scale to the user base that we have, but pulling large sets of data into the dashboard can be problematic. You have to reduce the data to only what you're going to present, and that's it. Otherwise, it is unusable in my opinion.

We've got hundreds of users on the product. When I came to this organization about five years ago, Tableau was fairly new, but it grew very quickly. Initially, only I and a couple of other people were using it, but now the user base has grown significantly.

Their support is good. They provide good training and all sorts of stuff.

We have a whole group that manages that. We don't get involved in it. We just ask for it to be installed and available, and they support it.

Its price is a concern. It is more expensive than Power BI. My guess would be that it is $1000 or less per year.

We might go for Power BI in the future because of its umbrella with Microsoft licensing. It is much cheaper for us to use Power BI, and some folks will go in that direction because they don't want to pay the higher license.

I would recommend getting Tableau to help with the training because there is a learning curve with it. Make sure the training piece is in place, and your account rep provides resources to get people started because it does take a little bit of training to get proficient at it.

I would rate Tableau an eight out of ten. It is not perfect, but it does the job for us.

We primarily use the solution for our data visualization, our different types of data. It is linked to our normal data visualization. It's not usually related to the medical side of the business. However, it is related to the revenue, and financial accounting, and submission on the RCM side. 

If I compare Tableau with Power BI, I prefer Tableau. It's easier to use.

The solution has very good drag-and-drop functionality and the screens are easy to navigate. You can easily create measures and dimensions. It has a user-friendly layout that makes task completion simple. In comparison, in Power BI, all of these actions are quite cumbersome.

It is quite similar to Excel. If a person has good Excel knowledge, it will be quite intuitive to learn.

Tableau is the whole package.

The solution allows you to write in SQL and Python. We don't need to write the Python code and we don't need to write the SQL script. However, it is an option that's on the table.

The solution is very stable.

You can scale the solution well.

It's very easy to set everything up.

There is another ETL tool for Tableau that is new. It takes time to reach some level of experience. IN Power BI, they have Power Query. I find it easier to convert the information in Power Query with a single shortcut key. That's not an option in Tableau. 

You have to prepare your data. It will take a lot of time to clean the data. 

There's no mature ETL tool in Tableau, which is quite a negative for them. They need to offer some built-in ETL tool that has a nice and easy drag-and-drop functionality.

There needs to be a bit more integration capability.

I've used the solution for about six months to one year. It wasn't very long. I used it at my previous organization. We're also using it at my current company. At this organization, we've only had it for about three or so months. It's quite new here. 

The solution is extremely stable. It's much more stable than, for example, Power BI. There are no bugs or glitches. It doesn't crash or freeze. It's very reliable. The performance is great. We've never faced any stability issues while using the product.

I'm not sure how many users we actually have within the company. 

Tableau is one package and there isn't too much complexity. The main pieces are Tableau itself, Prep Builder and Tableau Server, and Tableau Mobile. Sorry, Tableau Online. These four are the most basic software pieces of Tableau.

Whenever you purchase Tableau, you will pay a bit more and more. You will have access to the four main software products. After this, there is no need to purchase something extra. Therefore, in Tableau, there is no scalability issue. In comparison, if you will to Microsoft, there is a lot of products - such as Power BI. There is Power Automate RPA and Power Apps and MicroPower Apps also. You will need to call to Microsoft and they will integrate this Power App with your account. It takes time. With Tableau, there isn't an issue like that. 

We haven't had any sort of technical issues. They did assist us a bit at the outset. and they were very good. They are always online and easily approachable. We're quite satisfied with their level of service.

We also use Power BI.

The initial setup was very simple. It's not a complex process.

They have an excellent team here over at Tableau. They assisted us. 

The setup wasn't too difficult due to the fact that our system is not very complex. We work with rather simple data, which helped save us from suffering through many complexities. 

Maintenance is required at our database level. Our database is smart and lean, and therefore it's pretty straightforward. However long it takes for maintenance tasks is based on the level of data and on the heaviness. We basically do a sort of troubleshooting and some fine-tuning at the database level.

At the time of making visualization, we have to do some research to load everything properly on Tableau and have a refresh rate we can maintain. There should not be too much of a refresh rate every time. 

We had Tableau's technical team help us here and there. They were great and we were satisfied with their help.

The pricing is $70 per month. You have to pay about $800 or something in that ballpark annually for one license.

We are a customer and an end-user.

We are currently using the latest version of the solution.

I would recommend the solution. If a company really wants to go for some easy solutions, and something that is robust and dynamic this is a great option. Microsoft's Power BI also has its advantages and could be a good option as well, depending on what a company needs. If Mircosoft offered a bit more, we might even consider switching over. However, for us, Tableau is the better option. 

I'm using Microsoft Power BI also. Therefore, personally, I see the importance of the ETL tool. Microsoft is also adding many items rapidly - with new features two or three times a month. Tableau isn't making such advances regularly. 

Many people are considering shifting from Tableau to Microsoft very seriously. Therefore, Tableau needs to begin to compete. They need to offer more integrations and invest in a robust and easy ETL solution. It would really assist in cleaning the data.

If a company wants to onboard Tableau, they need to have some sort of ETL tool on the side as well. If they don't, and they don't have SQL or Python, I'd actually direct them to Power BI - simply to get that ETL capability. However, if the data is ready, and no ETL is required, Tableau is an excellent solution. If you just need to visualize the data, Tableau is the best.

Overall, due to the lack of ETL, and the inability to effectively clean the data, I would rate the solution at a six 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.

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.