Alteryx Reviews

We used the solution to design our models and databases, and then we used it to automate it through Excel.

The fact that it can turn up all the functions that Excel takes hours to do in a faster way is a very valuable feature. Inputting from various sources is easy. The fact that it can filter or do other kinds of inputs is very useful for Alteryx. Also, the thing with Excel, as opposed to Alteryx, is that Excel can only take up to a number of rows, and after that, it freezes. In my previous work experience, I had to deal with 10,000 or even more rows. Excel was not feasible at all as opposed to Alteryx, which just automated tasks and gave us the results in a few minutes. The reliability was much better.

The initial setup is easy.

I have not yet been exposed to Alteryx as much to comment on negative experiences. I'm taking a class this semester that has to do with Alteryx. I'm pretty sure that once I'm learning the projects and utilizing Alteryx more, then I can give back more feedback.

The usability could use some improvements. There are icons that you choose in terms of inputting the tool or even for filtering and then running different functions, there are different ways of designing your input tools and output tools, and that could get confusing.

I would suggest that if there's any way of simplifying it so that instead of tools or instead of understanding how it works, we could focus more on its usability and the output of it. For me, it was a bit challenging to figure out how to work with Alteryx. If there is any way to make the learning curve less steep, that would be ideal. 

I've used the solution for a year and a half. 

The stability is good, and, unlike Excel, it can handle large amounts of data and not freeze. 

There have been actually hiccups and issues with Alteryx, however, to deal with that, what we did was we went back and saw what the issue was, and mainly the issue was due to Excel. Maybe the value, function time, or strength value was not supporting it. We had to edit many functions and strengths to rerun it through Alteryx. That's one thing that's good in Alteryx: if we run and it's not working, it gives us an output and tells us where we are going wrong so that we can go back and troubleshoot on the problematic areas.

I haven't had a chance to deal with the tech support due to the fact that mostly whenever things went wrong with Alteryx, we were able to fix it by ourselves or with the engineering team. Engineering was able to troubleshoot it and fix it by looking at the output given by Alteryx in the email. It tells you where things are going wrong. For that reason, we did not have the opportunity to go and ask for advice from support.

I have had experience in automating projects in Excel and Alteryx, among others.

I have more experience in Alteryx itself than in Designer. A lot of databases were modeled, and we went and changed or updated the models, attributes, and entities. Then, what we did is we created them in Excel and we just auto-run it whenever we needed to.

It's not complex, and it's also not straightforward. It depends on which database and how many functions you are using or how much you are designing through Alteryx. If there are a lot of structures and if there are a lot of values that you're doing through Alteryx, then it would be hard. That said, from my experience, it's actually not that tough.

I don't deal with licensing. I can't speak to the exact cost of the solution. 

At first, you need to know the capabilities of Alteryx. It's a good idea to attend a lot of webinars or do a project. That would be really helpful to understand how powerful Alteryx is. Alteryx is used a lot in financial industries, according to what I have seen. It becomes really taxing to do everything in Excel or spreadsheets. Something that would take an hour or two hours to do it manually can take five to 10 minutes for Alteryx. Therefore, try a small project or understand the intricacies of Alteryx and that will get you started.

I'd rate it seven out of ten.

We have our data in several ACP-based systems, so we rely on Alteryx to extract the data from the system and build the data modeling in Alteryx. There isn't a cloud version. It is on-prem, but we can deploy it on the cloud, too. We do on-prem development and deploy it on the cloud. 

Alteryx's connectivity is essential. We like the ability to connect the solution to multiple sources. It's easier than other data modeling and extraction solutions. It's built on a self-service concept, so it's easy for anyone to open the tool and directly import or export data from it.

It would be nice if they can provide Alteryx with more options for In-DB connectivity. That functionality is there, but it doesn't include all software we are connecting.

In-DB is a concept in Alteryx where we don't need to export data into the Alteryx server to process. The data remains in the source system, so Alteryx can directly manipulate it. That feature is available only for a few tools with limited options. 

I've been using Alteryx for about two years now.

Alteryx is stable. 

Alteryx is scalable. My teammates and everyone in my company have an enterprise account, so we are able to access it.

Alteryx support responds quickly, and my interactions with them have been good. 
I rate Alteryx nine out of 10.

Setting up Alteryx is straightforward.

I rate Alteryx nine out of 10. I have worked on multiple media tools. This one seems to work for most use cases, and it is pretty fast when compared to others. 

If you're implementing Alteryx, you should get the Alteryx application and take advantage of the self-study materials available in their portal. It'll be easier if you try out this application before you start working on the toor.

The use cases range from basic EPL to predictive analytics and spatial analytics.

For example, within our functions group and our larger finance organization, we have a number of use cases where people are doing a lot of manual work, mostly in Excel. This has enabled us to move away from that, resulting in not only streamlined processes and efficiencies but also a significant increase in our ability to have confidence in that data. If the need arises, we may need to refresh data or revisit something.

It essentially automates what would otherwise be some extremely tedious activities to rerun and re-perform.

I believe that the ability to leverage the gallery for scalability, as well as the general data blending functionality, is most beneficial to our core-based users. Obviously, there's a lot more to it, but these basic data prep and blending tools help us a lot.

In some ways, I believe it is not yet as integrated as it could be. I believe that the data integration inquiring component could be improved slightly.

The data integration component could most likely be improved to increase enterprise scalability.

I have been working with Alteryx for two years.

It varies, but it will be the 2021 version.

The stability of Alteryx has been great.

It is highly scalable for the vast majority of things you want to do. When it comes to extremely large data sets or very specific analytics that would be delivered at an extremely high scale, either really, really tremendous scale or tremendous volume may be problematic. However, those are only a small number of possible scenarios. As a result, it hasn't been a problem for us, but we've switched to another technology for those types of use cases.

The roles are extremely varied. We have over 100, and less than 200, but that will increase in the coming months.

We are currently at capacity in terms of licenses. So we're looking to go from the low triple digits to over 1000 in the next year, maybe a year and a half. I believe we will quadruple in the coming year.

Technical support is quite powerful. I believe you must know how to take advantage of it, but I believe they have the necessary infrastructure in place to get you where you need to go. I believe it is somewhat incumbent on the organization to have someone serve as that contact, that partner. They're fine as long as you keep your end of the bargain.

We switched because we wanted to. We didn't have anything that could do everything Alteryx did, in my opinion. We had some minor transformation tools and such, but nothing that could truly compete with Alteryx.

The initial setup is straightforward.

That's something we're still working on. We started with about 100 licenses and identified areas where we thought it could add value. That has been demonstrated, and we are working to scale significantly over the next year. It's been a two to four-year journey from discovery to widespread adoption.

The majority of the work is done in-house. We initially worked with Alteryx, but it was primarily done in-house.

It has certainly paid for itself on a per-user basis. We haven't done a group ROI yet. But it more than pays for itself. Our "highlights" are quite powerful. The more prominent business cases are extremely compelling.

It's not cheap, but they offer a variety of packages to help you grow in practices and programs that allow you to scale effectively while also testing how deep the use can penetrate while allowing you to really reduce the risk. They offer various bundles in which they offer free licenses as well, so you can essentially see if you're able to use them consistently. Then, the next time, you can add that number or various other programs. It's probably on the pricey side, but they provide some really useful ways to grow and test.

If you're serious about scaling, you'll need a product manager, which is a major cost aside from the license.

We evaluated other products.

Simply strike a balance between democratizing the use of data transformation and analytics and having a vision for how you might want to roll things out. I think it's not the easiest thing to strike that balance, but that's my recommendation. To try to have a plan, but also to make sure you're not putting up too many barriers.

I would rate Alteryx an eight out of ten.

At the time, I used the solution for data preparation and data handling, which is cleaning the data and then working with the data, and linking different data effects. 

The solution is on-premise. I use it on my desktop.

Alteryx is great for someone who doesn't want to program and doesn't fiddle too much with the machine learning algorithms. If you need to do sophisticated things (if you're a real data scientist), then Alteryx is not the right way to go. For most users, there is plenty of capability and that's enough for more mundane use. The solution is rich and very flexible.

They have the capability to integrate some oiutside coding, but I didn't invest enough time to understand how that can be done and what the limitations are. If someone needs to do it, I'm not sure if they will be able to save time in the process or if that is cumbersome or not. I haven't tested whether or not integrating R code is flexible enough to accomodate specific developments with both platforms. Integrating optimized algorithms for the sake of automation would be great.

The biggest limitation is that the solution doesn't allow you to do sophisticated things in terms of fiddling with their implementations of machine learning and deep learning components.

I think they should really work on integrating or have a capacity to integrate tailor-made algorithmic code. I think that's one of the most important things they need to be doing.

Alteryx is very stable.

I haven't needed technical support, but they seem to be very available to talk. The people that I spoke to in Alteryx were very reactive and present. The moment you download the software, someone will call you, try to get in contact with you, and try to understand what your needs are.

I only installed the desktop and it's like any PC-type software. It's done in a few minutes, so it's easy. The server part is something else, and I haven't tested it so I can't elaborate on it.

I tested the solution for a long time so I never actually bought the software, although Alteryx is very costly. In comparison, KNIME is free.

To start with Alteryx, you need to pay for the desktop. The desktop platform costs $5,000 per year. You probably get more support with Alteryx, and it's good for non-sophisticated users who can benefit from the support included in the price.

KNIME is probably the alternative to Alteryx for someone who needs a package. The package can do a lot of things without needing too much programming and with zero code. It's very good for someone who needs access to algorithmic content without needing to develop it. However, I don't have concrete experience using it.

They are both very good packages for someone who wants to develop dashboards or use algorithms without knowing the nuts and bolts or with limited coding. They are very practical. The difference between the two platforms is very limited. They are equivalent.

I used Alteryx for data preparation, and KNIME can do it just the same. The two are very close in terms of concept and the way the two softwares work, but there are lots of tools for data preparation. Right now, I use a lot of loose programming. I develop scripts with R, and that is the most effective way I've seen because I can do whatever I like. I have 100% flexibility.

I think that a real data scientist would go with R or Python and use Spark. It's a totally different world, and these softwares need much more investment in time and effort to understand how to use them. It's a lot of work, whereas Alteryx and KNIME are for the casual user. They are more for the people who are in companies and need to work on data without clearly understanding the mathematics behind it.

I would rate this solution 9 out of 10. 

Alteryx is the kind of software that a corporation would want to go with and to deploy on for people who are not really data scientists and that have to use data, design dashboards, have to clean and prepare data and so on. That's an effective way to use Alteryx. To go for a sophisticated data science type of added value, a data scientist would want to work with programming languages and use a lot of the libraries that are available. At some point, they would want to put all this code and integrate this code into the platform so that less sophisticated users can use the analysis done outside operational lines.

This is definitely the kind of software that is a time-saver if you want to start working on different data sources or a lot of data sources, and you want to work with the data, angle the data, cross-link and so on. It's very good and very easy to use. It's visual, so it's easier to understand what you are doing. When it comes to putting some intelligence into it, it's enough for most people. There is enough that is loaded into software that can help you. The Alteryx interface may be easier to understand than KNIME. You are able to do some nice things with it in just a week.

Alteryx is a simple and easy-to-use solution.

Alteryx's predictive data models are pretty average and can be improved. If I'm using it on a day-to-day basis, the predictive model should keep improving based on data gathering and data preparation.

Alteryx is a user-friendly solution; its content is available everywhere to help users use its functions.

The solution’s initial setup is super easy.

Alteryx is an expensive solution.

Alteryx uses the UI interface wherein you can easily do a lot of things, and it doesn't have to code a lot.

My initial objective was to understand if a non-technical person with a small data science team could leverage a platform that can do a lot of things other than putting in a lot of resources. That's one of the reasons why our company chose Alteryx.

Alteryx is one of the few tools where user experience and customer feedback are up to the mark. I was from a non-technical background, and I wanted to create operators and do a lot of analysis and data cleaning on my own. With Alteryx, I didn't have to do anything except create the flow.

I would recommend Alteryx to other users.

Overall, I rate Alteryx ten out of ten.

We use the solution for its customer segmentation feature.

The solution works efficiently in handling a huge amount of data.

They should improve the solution's pricing.

We have been using the solution for two and a half years.

It is quite a stable solution. I rate its stability as a nine.

We have six users of the solution, including engineers and data scientists. I rate its scalability as a seven.

The solution's initial setup is time-consuming but easy to process.

We installed the solution with the help of our in-house team.

The solution is expensive. We have purchased its yearly license.

I evaluated Spark for its functions. In comparison, Alteryx manages massive data more smoothly. 

The solution requires three executives for the maintenance. I recommend it and rate it as an eight.

We use this solution mostly for data integration.

The three data signs and data engineering are great features.

Configuration is very low and is not good for the customer end.

They should be focused more on regulation. They need to come onto the cloud as soon as possible. 

We have been using this solution for about a year, and have been supporting it both on cloud and on-premises.

It is a stable solution.

It is a scalable solution because it works on multiple platforms. The petabyte of solutions they have been considering can be handled. 

I rate the technical support an eight out of ten.

The initial setup was straightforward because they had a suite. They have packages that need to be integrated and can be set up easily. They have different strategies for cloud and on-premises. For on-premises, they have a suite based on the license.

The deployment and maintenance require only about three technical staff. We have about three clients using this solution and plan to pitch more plans.

They have the complete licensing package. There are yearly licenses and other combinations. 

I rate this solution a seven out of ten. We target different domains because Alteryx is a product that can fit all kinds of domains.

For the next release, they should focus on the cloud. They should be platform agnostic so they can be easily integrated with multiple cloud systems. It should have the capability to work as a SaaS product.

I'm a contractor who works on different teams. Right now, I'm working on three different projects. 

Alteryx has cut down on time spent cleaning and transforming the data. It improved productivity. Transitioning from PoC to other users was seamless. The users upskilled quickly, so they could take ownership without a steep learning curve.

Alteryx effectively visualizes the flow of data and what happens at each stage. I also like that it's a no-code solution. I also like that you can troubleshoot certain parts of the workflow by putting them in a sandbox.

I have been using Alteryx off and on since 2020. 

Alteryx is stable.

Alteryx customer service was great. They gave me a trial version, and the client company bought licenses. The technical support and the sales guys were all helpful and supportive.

I use Excel and Power Query. I don't use Alteryx personally because I don't have the funds for an enterprise solution.

Setting up Alteryx was straightforward. The drag-and-drop menus are fantastic, and the icons are great. There isn't a steep learning curve. You become familiar with it quickly.

Alteryx wasn't deployed in the previous project. It was somewhat irregular. We ultimately did the dashboard in Power BI, but nobody was using Power BI as a data cleaning and transformation tool, so we used Alteryx as a proof of concept, We also deployed the cleaned data to use in a dashboard, and the dashboard was delivered to the client.

I rate Alteryx seven out of 10. I recommend using the 14-day trial version to learn how it works. You can use some of their in-house data to try real information or run a parallel. The trial version is super helpful for making a decision and understanding the capabilities. That's what kind of sold me.