Tableau Enterprise Reviews

I am using it for personal and organizational purposes. Most recently, we used it to collect and analyze the personal and geographic data input by people over the web.

Makes analysis fast and easy, providing insights that could be missed otherwise.

It is easy to use with very powerful data visualizations.

I am not a frequent user of this solution, so I am not sure what they've been doing recently. The last time when I used it, I had to use other tools with it for data extraction and cleansing.

Its price should also be improved. It is more expensive than Power BI. In terms of training, there is generally better online training for Power BI, but I am not sure of that. It would be helpful to know from where to access its training.

I have been using this solution off and on for four years.

It is stable.

It is scalable if you use it with other tools. I have used it with other tools.

I am a freelance consultant. I use it myself. My clients have hundreds or thousands of users.

I don't have any experience with their support.

I also use Power BI. Tableau is very comparable to Power BI.

If I remember it correctly, it was easy.

I can do it on my own.

My clients don't measure an ROI, they are interested in getting insights faster and when the get past the basics right then modelling scenarios. If you invest in BI tools it is part of a journey requiring investment. Analysis often helps to increase revenues and reduce costs. 

It is expensive when you compare it with Power BI. It should be cheaper.

I have looking at SAP SAC and Oracle / Essbase for a client.

I would recommend this solution. I would rate Tableau a nine out of ten.

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 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. 

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.

I am into a new healthcare domain. I publish my dashboards and my audience are the providers.

I am not well-versed with the latest 2020 version of the solution, but I have heard that it has made parameters dynamic. For example, when we are publishing the dashboards, if we want the dashboards to be set as a default as the previous month, we do not have to modify it again and again. It can republish month after month using an assigned default. This will save me time from not having to redo the dashboard all the time. The dynamic parameters are amazing in Tableau, it has been improved in the latest version.

I love the customization skills that Tableau has, it is not restricted to what is built-in already. We have many customizable options and they come out to be very fancy and beautiful charts. Also, I like the extract feature that makes my dashboard run faster. The low-level security that Tableau handles works well. This solution is user-friendly and suitable for any type of user. Lastly, the dashboard and reports are very user-friendly to navigate.

This solution has some features which really needs to be improved. For example, the sorting feature, If we compare it with ClixSense, ClixSense has a direct sorting feature available to users. Wherein Tableau, we have to go and create a parameter, make it dynamic, force users to click somewhere else on the filter, and then maybe you can sort it. Tableau is really new for sorting features.

With performance tuning, it generates a pretty complex query when it is not required. We do not actually write 100 lines of code for a single KPI indicator. What we do is run the performance tuning model which will give 100-200 lines of code for a single KPI. That is not exactly an optimized query. While running performance tuning on the query, it should be pretty optimized, but it does not seem to be doing this.

I have been using the solution for three years.

The solution is fairly stable. However, the issue is when it is time to upgrade. Upgrading a Tableau server is too tedious compared to other tools where normally you just have an update EXE file you run and it is done. This is not the case with Tableau, it requires the dashboard to go down for an hour and then we have to republish all the dashboards again. 

The solution is scalable without a doubt.

This solution has the best technical support compared to any tool. 

The solution is expensive but it depends on the customer's needs which will determine the cost of the licensing. How much they require and what package they want.

We have evaluated ClixSense in the past. With ClixSense, it is not as customizable as this solution, I would even say restricted. For each and every customization and format change, the basic stuff, it asks us to buy another extension. We will not be doing that.

This solution has broadened my VA exposure and it has allowed me to do things differently with respect to VA concepts and visualization. I have a great takeaway from my experience with the solution.

I would recommend Tableau because of the amazing dashboards that are appealing and that also gives us the ability to create with far better customizations.

I rate Tableau an eight out of ten.

The primary use case is for household survey and population, and housing census analysis. I have also been using it for monitoring and evaluation of projects.

I provided Data Visualization skills to selected staff of The Gambia Bureau of Statistics (GBoS) in 2018 using Tableau. Previously, SPSS was used to analyze the data but after I introduced them to Tableau, the team was so happy that together we decided to look at the Census 2013 dataset and analyze it using Tableau.

I have also used it to develop a key indicator list based on the Living Standards Measurement Survey.

GBoS, with the help of the European Union, bought Tableau and have since been using it to analyze household surveys instead of SPSS or Stata, as they were doing before.

Another project that I am about to complete using Tableau is the Data Lifecycle Assessment of the Planting for Food and Jobs project in Ghana. I am working on this project with the Ministry of Monitoring and Evaluation and it is being sponsored by the Alliance for Green Revolution in Africa.

Already, the staff is so much in love with Tableau that they want me to provide Advanced Training for them so that they can adopt it as their Data Visualization Tool.

The most valuable features are the ability to classify variables into Dimensions and Measures, Ask Data, and Show Me.

The Dimensions and Measures feature solves one of the basic problems most would-be data analysts have, which is to determine which variables to analyze or focus on. Tableau makes this easy by splitting them into Dimensions and Measures and then use Dimensions to dissect or dice and slice the Measures.  

Ask Data enables non-technical personnel to quickly derive insights from data just by knowing which variables are available.

Show Me is a feature to help with knowing which chart is an appropriate one for the selected variables, and it makes helps in creating appropriate visuals.

I cannot end without mentioning the overall seamless flow of how to derive insights from data using Worksheets, Dashboards, and Stories.

I think predictive analytics is the main driver of business decisions and hence Tableau should strengthen the ability to make predictions.

The forecasting feature in Tableau in my view is too limited because it must have dates but I should be able to predict the outcome of an event without having a date as part of the input.

In situations where you are analyzing or using just one measure such as Sales, Tableau does not create the header for you. Furthermore, it is not straightforward as to how to create it.

I would like to have the ability to perform multiple pivots and creating different variables. For example, if I have the regional population for six regions and branch offices, together with the number of clients per branch, all as a record or observation, then I should be able to pivot them separately resulting in the Region, Population, Branch, and Clients.

I have been using Tableau for three years.

The scalability is simply awesome.

The technical support is awesome.

I have worked with Excel, SPSS, and Power BI.

Tableau is easy to use and the support from the community is awesome. Also, I see Tableau as a product for the future due to the commitment from the development team.

The Tableau Development Team listen to suggestions from the users and follow discussions on the community forum and factor that into product upgrades. 

End users want things to be done quickly and intuitively and the only way that any product will survive is by listening to end users 

The deployment was straightforward. I would say that it was a nice and easy, no-brainer setup.

The implementation was done in-house but there was an occasion that I had issues. I contacts support and it was super.

The ability to make insightful decisions quickly is a big benefit to all and therefore reduces cost in terms of manhours spent in cleaning and creating visuals.

It is a bit difficult for some people when they hear $70.00 per month, as some solutions are available for less than $10.00 or for free. What I always tell my trainees is that it is not so much about the cost, but rather, how you can quickly get the insights you have derived into the minds of your audience or stakeholders.

It is important to consider the ease of use. If a product is free but usability is a problem then it is not actually free because it cannot be even used.  

I had already used other tools and also wrote my own program in VBA to analyze data.

Customers need more insights and hence, they should look into Natural Language Processing (NLP). The ability to analyze text data or focus group discussions or radio phone-in programs would be helpful. 

When there is a major upgrade and you install it, it wipes off all of the recently used files and newbies get frustrated. If something can be done about it then that will be very helpful