Tableau Reviews

We use it in our parent company as well as in client companies. A few of our environments are on-premises, and a few are on Tableau Online. We have a mix of both.

The UI part is the best. The end-users can easily get started with Tableau Desktop or Tableau Online because of its user-friendliness.

I also work as an SME on the platform side. Tableau is very nice and jazzy for the end-users, but there are pain points for the admins. Performance is something about which we hear a lot of complaints, such as the dashboard doesn't open in time. It performs well on the desktop but not on the server. I know that there is always a limitation when it comes to a huge amount of data or the complexity of the calculations, but we often hear from end-users about the performance on the server side. It is easy to drag and drop all the columns and do what we want, but if it is not going to load better on the server, users are not going to like it.

Their standard support is not good. They should improve it. I don't know if it has anything to do with the acquisition, but lately, their support has not been great.

Their upgrades have always been an issue. They never work. 

Tableau is a little bit costlier than other tools such as Power BI.

They should make it easy to integrate with tools like SharePoint, Teams, OneDrive, etc. Its integration with Office 365 should be improved because most of the users already have tools like Outlook, Teams, and SharePoint, and they want to integrate a reporting tool or a visualization tool with their existing tools. 

It is very easy to integrate scripting in Spotfire. We can do a lot of changes in the UI by writing some scripts. That could be something that Tableau can look into. They can also consider providing APIs, but most of the people who work with Tableau do not really work much on the scripting side. So, I am not sure if it is feasible or required technically.

I have been using this solution for almost eight years.

It is scalable for online and on-premises versions. With the online version, they take care of the load, and we don't have to worry about that. For on-premises, initially, we used to have core-based licensing in which we had a cap on the number of cores we could expand to. We then moved to user-based licensing, which makes it easy to scale.

In our parent company, we have close to 200 users. We also have two clients on this platform. One of the clients has about 400 users. For another client, overall, we support close to 20K customers on this platform. That's one of the biggest environments.

Their premium support is really good, but their standard support is failing. Their standard support is the worst. We recently had an experience where we waited for more than seven days for a ticket to be picked up. It was vacation time, and there were a lot of factors, but their standard support is not good. For a few of our clients, we do have premium support, and they respond to any issue because we include the technical account managers in the communication.

Comparing it with Power BI, the main differences are the cost and the integration with Office 365. It is very easy to integrate Power BI with Teams or SharePoint because they all belong to the same company, whereas with Tableau, I have to integrate it with a third-party vendor. It is a little tough to integrate Tableau with SharePoint or Teams. Nowadays, everyone wants everything in one place; it could either be in SharePoint or in Teams. 

I have been working with Spotfire lately, and it is very easy to integrate scripting in Spotfire.

The Tableau server-side used to be very good initially, but for the last year, we are having issues with the upgrades. Their upgrades never work. We always get stuck while doing the upgrades, and we end up taking a different approach. We take the data and keep it somewhere. After that, we wipe out the entire server and install it again. We then restore the data into that environment. 

In terms of maintaining this solution, I used to belong to level three (L3) support till last month. We were only handling the performance issues and any issues that need RCA. The L3 support used to have close to five people. L2 support, which usually included adding a user, removing a user, adding a new group, and providing usual production support, was taken care of by the Ops team. They used to have more than five people on the team.

Cost is where tools like MicroStrategy, Power BI, or Spotfire come into play. Cost-wise, Tableau is a little bit costlier than other tools such as Power BI.

I have been using Tableau all these years, but about four years ago, Power BI came out at a very low cost. Their desktop version was free from the beginning. Power BI Desktop has always been free, whereas Tableau Desktop is costly. When it comes to cost, people prefer Power BI because it integrates very well with Office 365. You don't have to worry about integration with Teams or SharePoint.

It is easy to use for most business users, but before using it, you should understand the basics of dimensions or measurements. If you directly come to this tool without understanding the concepts of warehousing, dimensions, and fact tables, you would not get what you want. This applies to any reporting tool. You should have a basic understanding of what data warehousing is all about and then get started with the tool.

I would rate it an eight out of 10. Tableau has always been at the top of my list. Because of the technical hold that I have on the tool, I always prefer Tableau. It would always be on top as compared to any other tool.

In my consulting firm, I use Tableau for data visualization and data analysis. Alongside Tableau, I also use Python and, on occasion, SPSS.

The EU had recommended Tableau for use in some of the statistics offices in Africa, including government institutes in Ghana, and just last week I was using it to do a program for the Ministry of Monitoring and Evaluation. There, I used Tableau to convey selling points to buy Tableau, which is one example of the consulting work I do for clients. 

I also train others in visual analysis with the use of Tableau. This September, I trained 265 medical statisticians. Last week, I trained 13 staff from the Ministry of Monitoring and Evaluation. And soon, I'll be training another group of 20 people on Tableau.

Tableau is a good product for people like me who provide data analysis training because it makes my job far easier. It's a good product and very easy to use, making the introduction of key technologies extremely simple.

For instance, when you get data, and go in to analyze it, people ask, "This is numeric?" People start thinking about, "How do I get all these tools?" Tableau takes the data and automatically breaks it down into two dimensions and measures. That makes it easy for me when I'm doing training.

So what I would say to trainees is, "Don't worry about all these data types, when you are designing your questionnaire, because in Tableau it breaks them into two. And the measures are the ones that you are going to actually work on. You normally break them down by the dimensions." And that makes it simple for people to understand. Otherwise people don't know where to start when it comes to data analytics.

Tableau makes life very easy for not only myself but also for others, because you can quickly get into data analytics and visualization with it.

Tableau is easy to use. That's the first and most important thing. I not only provide consulting but I also train people to use it, so with its ease-of-use it's not as difficult for me to train executives and management staff, because they don't have the IT background, unlike when I'm using Python. They don't know anything about programming, so Python is more difficult.

Tableau is also, right from the outset, a self-service product. It's easy for anyone to understand and use. Some of the organizations that I introduce to Tableau are using the full-blown version, i.e. the commercial version, and they can very quickly start analyzing data with the use of the Ask Data feature, where you can simply drag and drop while querying for data with natural language processing. You type in English and it will pick the data and analyze it for you.

Those features are built into Tableau which makes getting started with data analysis very easy. And it's also got some pretty good built-in visualization tools. I would say Tableau is one of the best when it comes to self-service functionality.

I attended a Tableau conference recently, and a quick improvement came to mind. When I am training people how to use it, I've come across situations where I've found it difficult to explain relationships. For example, when you want to blend data or when you want to show relationships, like when linking multiple tables; well, if you're an IT guy, that's easy. But if you are not an IT guy, you don't know anything about entity relationships, and it becomes a bit difficult for others to follow along.

It takes me a long time to get people to understand, even up to the point where I feel that this is the lowest level that I can go in terms of explaining it. I realized that many people don't really have any experience or knowledge about relationships between objects, and it makes it hard for me to get my teaching across. 

So I was suspecting, and I think I made this recommendation, that Tableau could find an easier way to introduce relationships. For now, if you want to build relationships in Tableau, or even in Excel, you have things like Access modules and Sheets. But how do I know that I need to use one object with another for the relationship. And if you then put in a table, what do you do after that? You have to double click, but people don't know that you have to double click.

I was hoping that there's a way that they can make that process a bit easier, though I don't know how they will do it. Perhaps when you load Tableau and connect to a data source, there would be a prompt that asks you if you want to link two tables together. So if you want to link two tables together, maybe you do A, B, C, D.

That might help with the self-service idea. If you're talking about self-service, then it should be easy for people who do not have the time, or who do not have that IT background, to pick the data and use it correctly.

In addition, and more generally, what I would like to see more support for is predictive analytics. When you're doing descriptive analysis, Tableau is excellent, and it's easy to do. But when you are trying to predict something, like in Tableau's forecasting feature, it seems to require date fields, or it won't work.

But I can forecast something without relying on date fields; maybe I want to predict that a branch has to close if it doesn't want to make something soon. I don't need dates to do that. For this reason, I'm using Alteryx for predictive modeling instead of Tableau.

Overall, the only major frustration that I have had so far is with Tableau Public. I first used Tableau Public when I was building capacity, and when there was a later release to download and you wanted to upgrade, all your work would have to be manually re-entered. I don't know how they can solve that. I was expecting that they might make a release on this upgrade, and then I can hit upgrade and it will install over what ever I have already.

Otherwise, for now I think they are doing well and I know they're still adding a lot of features. But it does sometimes make our work difficult, for those of us who are building capacity, and who are regularly changing people around. It means you have to keep learning all the time.

Another small detail for improvement is that when you draw bar charts, the default color could be something more neutral like gray. Instead, the default is blue, and I don't exactly get why this is the case.

I've been using Tableau for the past three years now.

I contacted support when I had a problem with data entry in 2018 or 2019. I spoke to a man based in Ireland and he was super.

I had originally put the problem I had on the Tableau community support forum, but I didn't get the right answer. I've forgotten the exact problem but it involved connecting to a data set from an Excel file. Instead of the data field displaying the data for you, what I got instead was an error or no response.

It kept happening like that so I sent a message to support, who gave me some steps to follow. I followed them but it still did not work. However, I realized that any time I do it and it shows up and I click data,  it then suddenly goes off. I'm still wondering why that happened. I think it depends on the size of the file or some other reason. I have not tried it again because I'm a bit busy now but it's something that I want to go back to because support didn't give me a satisfactory answer.

They told me, "Do this." I said, "I tried it. It did not work." They asked me again to do something and I tried it, and it still did not work. But then I tried on my own, and this time when the problem came up I clicked the data interface twice to reload it. On the second time I clicked, it worked, but I don't think that is the right way to handle it. 

I used Power BI before discovering Tableau in 2016/2017.

At first I did not like Tableau, because Tableau initially put me off considering that I have a problem with how Françafrique countries, like in West Africa, are controlled by France to not buy anything from Anglophone countries. I've worked in 15 African countries. And for instance, in Ghana, we are bordered by Françafrique countries but they haven't bought things from here because France tells them, "Don't buy." Which to me is wrong. Why should you sit in Paris and dictate to Africans?

I also decided that, "Okay. I'm not going to go into any French country and work." So, for my consultancy, apart from mainly Côte d'Ivoire, I also said, "Look. It is the attitude towards Anglophone and West African countries, I'm not going to help anybody." Because my contract with the World Bank was to build capacity. So I decided I'm not going to go there.

So, when I saw Tableau first, the word itself made me think that this might be the same kind of product, and I would not even look at it, because I was against it.

I kept on using my Power BI until a colleague, another consultant who we met from South
Africa, said to me while I was demonstrating Power BI, "I think you can use Tableau." I said, "What is Tableau? I don't want it." He said, "Oh I don't know much about Tableau, but somebody told me it's easier to use than Power BI." He said, "Why don't you look at that?"

We were working on the same project and I told him, "No, I'm not interested, I will not
look at it. It's a strange product, I don't want to look at a different product." And the guy insisted, "Oh please, you must take a look at it." Because we were looking at the project like we're a team, I said, "Okay, I'll look at it."

So that evening I downloaded it and I realized that all the things that I'm doing in Power
BI, that requires some level of IT background, well, I don't need that in Tableau. So then I decided, okay, let me really look at it. Who is behind Tableau? I asked where is the name Tableau from? Where did you get that name from? Okay.

So that was the time I changed my mind towards Tableau, and to be honest with you I've not regretted anything for doing it. I'm quite happy about it.

Setup is not that difficult for me. However, I remember in Gambia, there was some initial difficulty when I was teaching how to set up the organigram for the National Social Staff System.

In the National Social Staff System, you have about 11 ministries involved and the coordinator, and it's the coordinating agencies in Bureau of Statistics. So I needed to set up the system so that all the other ministries can enter their data. And when you enter the data, the other ministry, let's say, Ministry A can also enter data. And Ministry B cannot see what Ministry A is doing.

Now, when I was doing it, it was not difficult at all, but because I had to handle other systems and leave, I tried to explain it to them but they found it a bit hard to grasp.

So where you have multiple alliances and you set them up like organizations, it can get a bit complex. Because there's differences within the same organization under different departments. It's not a big problem when you buy Tableau for one single organization, but when you set things up for multiple organizations like the National Social Staff System, it can get problematic.

The national system is made up of different entities: Ministry of Health, Ministry of Agriculture, Ministry of Finance, etc. They are different ministries and they don't necessarily need access to all of each other's data. But if you buy Tableau for each of them then that is fine but if it comes to a situation where they all come under one number and you're setting up, you don't want one ministry to see what the other is entering.

So there was definitely a bit of a problem there. But I can't blame Tableau because no matter what it is, sometimes you need a certain level of IT skills to get certain things done. 

At $70 per month, I think the price is a bit scary. I have a small consulting firm in Ghana, working in about 15 different African countries, and when it comes to our part of the world, $70 a month is a lot of money for software.

In fact, where Tableau was approved for use in Gambia, I had the EU pay for three years. But I know it's expiring soon, and I don't think they will have the money to renew. I don't know how they're going to do it. When you come to Africa, especially when you're on the net, we don't use it so much, so I don't know if there is something that they can do about pricing for people in the African continent.

Yet recently, I trained 265 medical statisticians on how to visualize their data, using Tableau Public. They were so happy. And they thought, "Oh, this is very easy for us to do." But when they asked me about the price and I told them, they said, "$70? But we can't pay."

So that for me is a problem here. And, mostly, it's a problem for everybody. There are some companies that can easily afford it, but the majority of companies cannot.

I have occasionally used IBM SPSS for similar work that I perform in Tableau, but I only use it when the client absolutely requires it.

I wouldn't tell people to go with Tableau just because it's the tool that I use. I would instead emphasize its remarkable ease-of-use and the way Tableau really listens to their users and comes up with frequent upgrades. 

I would rate Tableau a nine out of ten. 

As an administrator, I use the solution in my company to deal with customers' current deployments and help with the troubleshooting process in case of issues while keeping a check on the performance, like the coverage provided by the solution.

Tableau is a good product for some of the projects my company has handled, and we have also used it as a visualization tool. Considering how certain products work for customers and how their businesses function, it is easy for my company to use Tableau in such environments as it is a user-friendly tool.

The most valuable features of the solution are the permission management and the user management.

With Tableau, the only downside stems from its prices, especially in the country where I reside, which is Sri Lanka. When my company reaches out to new customers with Tableau's new costs, we find ourselves at a disadvantage. The price is an area where improvements are required.

I think Tableau plans to release some AI-integrated features in 2024 for one of our company's clients we had initially. My company plans to use the AI-integrated version of the solution when it is released. AI is one of the best things that I was hoping to have on Tableau.

The only issue with the solution is with its prices at a regional level. Integration-wise, it is a good product. Tableau always provides support to users. Tableau should introduce some special pricing for its existing customers, and it should be possible for the partners and resellers to provide such special prices to customers.

I have been using Tableau as an administrator in my company for four years. My company, which is in Sri Lanka, has a partnership with Tableau.

It is a stable solution. If the people who are working on the tool know about the product, then it's a stable solution. New people who have an understanding of the product can use its stable nature. Stability-wise, I rate the solution a nine out of ten. There is a possibility that the product crashes at times.

The product offers scalability or scaling out options, but at the moment, there is no demand for the product from the customers, and nobody is getting it deployed. In general, though it is a scalable solution, no one prefers using the product presently. Recently, I have only done two deployments of the product for our company's customers. The people who use the product are not satisfied with it.

The product's clients are major banks and some other businesses operating in the financial sector, meaning all of the tool's customers run enterprise-sized companies.

The solution's technical support was good. I rate the technical support an eight to nine out of ten.

Positive

The product's initial setup phase was not very easy as it was a little bit of a technical process about which I didn't have any experience. I rate the product's initial setup phase a six to seven on a scale of one to ten, where one is a difficult setup process, and ten is an easy setup process.

Most of the solutions I have deployed have been done on an on-premises model. For some new customers, my company has deployed the product on the cloud. If customers are okay with their budgeting part, I suggest they deploy the tool on the cloud.

For new users, our company can set up an environment. If the customer can provide an on-premises environment, we can set up Tableau in four to five hours. A single-node deployment takes around four to five hours, but if it is a marginal level of deployment, it might take a day or a day and a half.

When involved in the deployment, if there is some support from the customer's IT team, then only one person is required to take care of the deployment process.

I rate the product price in the Indian region a seven to eight on a scale of one to ten, where one is a low price, and ten is a high price. For the other regions in the world, the price of the product might be average. The additional cost attached to the product crops up when users plan to use the product on an on-premises model.

I recommend the product to those who plan to use it. Tableau is a good visualization tool for everyone, and it is also easy to learn, especially when compared with its competitors.

I rate the overall tool an eight to nine out of ten.

We use Tableau for data analysis. We integrate 15 sources of data and then that data is brought into AWS. From the AWS, the data is uploaded onto a dedicated Tableau server where we have all our dashboards running. We then run code on the data to return results, such as the regression and causation.

The solution has great features which nobody can beat, you can do a lot of customizations, such as use different dimensions and colorize them. Additionally, you can use the numeric values for the customization, which is an exceptional feature.

There is a lack of visualization in Tableau which could be improved. For example, if you want to do a Sankey in Tableau, you have to do a lot of work to do it. Sankeys are available, they are for sale for a minimum of $400, which is out of the question. Whereas if I wanted to do a Sankey, it is simple for me to use a free visualization and put in the data, and from what I already have, receive a fancy Sankey.

Whenever it comes to specialized visualization, Tableau is an absolute failure.

The integration between Tableau and our statistics software or other software, such as Python is very loose and undefined. If they improve that it would be a benefit.

I have been using Tableau for approximately two years.

The solution is stable.

If I have web analytic data on session IDs, Tableau or Power BI both fail miserably in scalability because you are not able to go on a session-level and have 18 million rows fire up visually.

We have approximately 100 users using the solution.

The initial setup is simple.

We had a local team of two technicians that do the implementation and maintenance of the solution.

We are on an annual license which costs us $1,400 which is very expensive. Microsft BI is less expensive.

I have evaluated Microsoft BI.

The reason why we are not getting rid of Tableau is because of legacy reasons. Legacy, meaning it is being carried over and our organization does not have time and energy to transfer everything to Microsoft BI.

I will not give any credit to Tableau or Power BI for the very simple reason that everything is in the hands of the developer of how you visualize the data. There is not any magic in what Tableau is doing, the magic is from the developers who are creating the visualization. Any visualization tool which gives that capability sets you apart.

I would not use Tableau if I am working out of a financial organization because it is lacking fancy visualization. However, if I was a bank or a government organization where I am only looking at trend charts and bar charts I think Tableau comes out ahead of other solutions, such as Microsoft BI.

I rate Tableau a five 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.