Alteryx Reviews

Alteryx is the ultimate replacement for a data shop. I use Alteryx in place of a data center.

We using Alteryx on-premise using gaming systems for performance.

The most valuable feature of Alteryx is its unlimited handling capabilities.

Mastering Alteryx, a comprehensive solution, takes time. However, once you have gained proficiency with its layout and how to drag, drop, and connect components, it becomes remarkably easy, yet still thorough.

The visualization could improve in Alteryx. It is ideal to use another solution, such as Qlik, to make up for the lack of visualization.

I have been using Alteryx for approximately five years.

The solution is scalable. To scale the solution you will have to add more hardware to the on-premise setup, such as CPUs and memory.

We have received a return on investment using Alteryx after the first job.

The cost of Alteryx is approximately $2,900 annually.

When I first started using the solution I was working with the state and was given six years of pregnant women and children data and it took 35 minutes to process the data. I tested the same data recently and it took .3 seconds.

Data should be returned within 30 seconds to make to process useful and if the solution does not provide this, then it is not beneficial.

Cloud deployments are not as quick as the on-premise.

I would advise others that they need someone who understands their domain in terms of the field that they are in. They need to have someone to hold their hand for the first ninety days. Once they pass the ninety days they will be set for the future.

I rate Alteryx a ten 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.

I believe we are using the new version. 

I use the solution for development and certain financial projections, such as making a business plan. I employ the solution for the amalgamation of big data. 
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I also use the solution for certain analytics deployment, such as that found in the commercial arena, for ascertaining the projections of sales. 

I like the solution's velocity, the speed with which it processes data, and its ease of use. It has good usability. 

A colleague of mind mentioned that the solution should have more options for the visualization of data. The visualization filtering could stand improvement. 

The documentation could be improved upon. 

The price could be better. Though we are satisfied with the solution, we feel the price to be a bit on the expensive side. We make use of another ETL solution, the PowerCenter. We would like to improve on and escalate Alteryx, but find it too expensive at the moment to do so. 

I have been using Alteryx within the past 12 months. 

I believe the solution to be stable. 

I believe the solution to be scalable, although I cannot say for certain, owing to the price. It We are not talking about a corporate solution at the moment. While we wish to improve upon and escalate the solution, I believe it is too expensive for us to do so at the present time. 

While I cannot speak about the future with certainty, we do have plans to increase the usage. We are satisfied with what we have at present. My area currently consists of four licenses and we would like to improve on this, to have more users. For the moment, we don't have definite plans to buy another license, certainly not in 2022, although, perhaps, in 2023. I sure hope so. 

Technical support is good.

We make use of another ETL solution, the PowerCenter.

We also make use of Databricks, which we use as a data science platform, although I cannot say how this compares with Alteryx. We use Databricks as a corporate ETL solution from the Data Lake. 

We use Microsoft Azure, as well.  

The initial setup was easy.

The deployment was fast, I believe lasting one day. 

We handled the setup on our own. 

I don't know how many people are needed for deployment and maintenance. 

The price could be better. Though we are satisfied with the solution, we feel the price to be a bit on the expensive side. We make use of another ETL solution, the PowerCenter. We would like to improve on and escalate Alteryx, but find it too expensive at the moment to do so.

The licensing costs are around 26,000 reals, perhaps $6,500 a year.

The licensing is all inclusive. There are no further costs. 

I don't make use of Alteryx myself, although my area, of which I am a manager, does. 

I am a customer of Alteryx. 

There are six people making use of the solution in our organization, two from compliance and one a data scientist. 

My colleage rates Alteryx as a nine out of ten. She says she loves it. 

Primarily, I use Alteryx for Excel. I also use it to automate pipelines whenever necessary.

The most effective feature of the solution is that it is a fast product with good performance compared to traditional solutions. The tool can deal with large or huge datasets.

I think the tool does not run on some operating systems. I think it would be great if Alteryx could provide an in-built machine.

The software is not fully optimized. What I try to do is use Microsoft tools if I have a heavy workflow. I move to a tool to be able to respond to the workflows for a few seconds. I have to wait until I am able to move to another table, and then I can't duplicate. There are too many optimizations to run.

I have been using Alteryx since 2018.

Stability-wise, I rate the solution a nine out of ten. Overall, I have had a good experience. I encountered a few bugs, but I think it is okay because it doesn't happen every day and is normal.

Scalability-wise, I rate the solution an eight out of ten. Currently, the tool also offers a cloud version. Usually, scalability is not needed in my environment. In my company, we only use desktops.

I think that, in my company, I work with the tool as part of a team. Additional departments also use the tool. My team uses Alteryx. A total of ten people use the tool.

The solution's technical support is okay. I posted a few queries and called up the support team for help. I felt that the support team was quick to respond and was also helpful. I rate the technical support a nine out of ten.

Positive

The product's initial setup phase is simple and straightforward.

The solution is deployed on an on-premises model with servers.

If one is a high price, and ten is a low price, I rate the tool's price as a one. The tool is expensive.

I think that apart from Alteryx, I have had experience with a lot of other products, including Sagemaker. I had purchased a product for one of my customers, who then compared it with Alteryx. I also know about some products like Talend and Rapidminer. I didn't like Rapidminer's interface.

I don't use the predictive analytics part in Alteryx.

I am not sure about the data blending part of the tool.

It is easy to integrate and set up the product with other third-party solutions or hardware. The biggest challenge with the integration is probably the need to get approvals and follow up with the organization. I think the process is quite easy.

My company has not had the opportunity to integrate Alteryx with AI tools.

I recommend the tool to others.

I rate the tool a nine out of ten.

The biggest use case for the solution is that it allows you to it allows me to combine data from different sources and different formats to create a unified data set to answer business questions. I can take data from my general ledger and combine it with my expense report data or with external data or with different parties or different questions, and it could all be in a different format. For example, one source can be from a database, one can be an Excel file, one can be a PDF, or a CSV file. Alteryx lets me very easily put them all together and create one database. 

The biggest thing I like is that you need zero coding experience. I'm not a coder. I'm an accountant by trade, and I've been able to pick up the tool fairly quicker, basically.

The tool is very drag-and-drop. The tool has very good documentation to help you understand the features.

The community is very active and helpful.

I like that I can merge data from different sources into one place. 

With this solution, we can do reporting in about five minutes as opposed to day.

Once we've vetted the data, we can get the solution to run during off-peak hours so we can out data very easily. 

There's a lot less maintenance required.

We like that we can make notes right on their Canvas, so it's easy to pass workflows over to other users.

The visibility is very clear. We can show what data is being used and how it is being transformed.

My true data is able to stay intact even though I can easily manipulate it. 

I'd like it to be easier to work with PDF. When you want it to read PDFs, it can be a bit difficult. That may be a PDF issue, however. 

Maybe if I was doing more spatial or geographical tasks, I might need a bit more; however, for my purposes, I have all the features we need. 

I've been using the solution since 2017 or 2018. I've used it for five to six years on a daily basis. 

I find that the Designer is stable since it's on my machine. Sometimes the server environment is not the most stable. I'm not sure if that's due to the fact that we don't have enough cores or whatever that might be; however, that's something that's probably a little less stable, the server at the bottom. Again, the servers will be used to run flows, not to create the flows.

The Designer is where you create the process and your logic, and you can use a Server to run the processes at different times throughout the day.

The scalability is fine. That said, the pricing model is not the cheapest. You have to be careful who will be using it. The pricing is the only hindrance to scaling. 

We have around 300 to 500 licenses. It's pretty mature in our organization. 

We have a great relationship with technical support. They are helpful and responsive, and they have a great community.

I'm not sure if the company used something previously.

In order to deploy it, you need to have Alteryx Designer. You have to have that installed on your machine. We have a yearly license and you just basically renew your license with Alteryx to keep it active. 

In order to maintain our flows, there is very little maintenance needed. 

We have witnessed excellent ROI. The fact that we can schedule flows and reduce time and entry to market has been extremely positive for the company. 

The pricing is reasonable; however, if you need a lot of licenses, it will get expensive. 

I am not using the latest version of the solution. We're always one or two versions behind the latest, as we only do updates once or twice a year. 

The great thing about the solution is that if you know Excel well, it translates very well into Alteryx. It's important to have an open mind; however, I'd recommend everyone give it a try. 

I'd rate the solution eight out of ten. 

I am using the product for making machine learning models for particular uses that we have to focus on analytics.  

I didn't like the data analysis features at first, but they were actually quite good. The built-in machine-learning models that they have are quite extensive and useful. There are a few models that are normally not found in any other drag-and-drop interface platforms. For example, when I was using and testing other tools, I could not find vector autoregression models. I also could not find the rather complicated tentative models and other model types that I find very useful. Those are built-in to Alteryx as cubes. On top of that, the capability of writing Python commands and creating our own tools and our own models for machine learning make the solution customizable and quite interesting.  

There are a few ways the solution could be made better. The graphical user interface could be made more friendly. I mean from the GUI point of view, it is a little dated. But from a functionality point of view, I think even for people who do not know the coding part and who are more business users than programmers, they would not have a very hard time using  Alteryx for creating machine learning models. It is quite good and quite useful already without improvement to the GUI. It could just stand to be more modern-looking. 

I would like if they could make some type of free license for the product. Not a free full license but a limited edition or limited capability free version. The limited-time version is a little restrictive. A free, open-term desktop version like Power BI and Tableau and KNIME might help users more with discovery and utility.

I've used this solution for about five or six months.  

We were using Alteryx from a development point of view. We did not use it to test it to its breakage point. So it handled our demands and was quite stable.  

The response time was quite good. Even running on a laptop, it was like using it on my desktop. The response is consistent, fast and stable.  

From my experience and from my point of view, the solution is quite scalable. We did not use it for the production system yet, but from my experience of using it on a laptop more recently, it runs with a few hundred thousand rows and columns without a problem.  

The other good side of the scalability is the number of connectors available for data sources. They have more than 200 connectors, which allows access to well more sources than anything I have needed.  

I did not contact the technical support at Alteryx directly. When we initially got the license, they had us talk to the local support here in Singapore. We went to them for a small issue to see what exactly the product can do and then we did not need any support from their support at all. We are quite adept with this solution and modeling. We called up the Alteryx local support to show us how to do that one thing but it was very easy. Once we saw it, we did not have additional reasons to contact them again.  

Before Alteryx we tried of KNIME. The learning curve for KNIME is steeper than Alteryx. Alteryx is much more user-friendly than KNIME, at least from our experience on both products. Also, KNIME functionality is limited for the free version so it was harder to explore the full capabilities. When we tried the free license for Alteryx, we got the full use of the client license for 14 days and then we were able to get an extension to explore our testing of it for some time. But we did use KNIME before that for long enough to be familiar with the differences. My impression of Alteryx is that it is more user-friendly than KNIME and the built-in modeling in Alteryx is much better than KNIME.  

The initial setup was very straightforward. I was able to install it and get it running up and running quickly and the process was very simple.  

When I started doing a machine-learning model in a project writing my own code, it took me in-all about three or four days to complete it. For my initial experimentation, I decided to replicate that model in Alteryx. As someone who understood models but did not know the program at all, it took me two hours or less than that to complete the test. Once you learn how to drag-and-drop things in the interface, it is very easy to start on development as long as you know what you want to do.  

We did not use any help as far as an integrator, reseller or consultant. We did the deployment ourselves.  

Our approximate cost as we understand from the company is that the total is $5,000 per user per year for the desktop production license. So, for us and the ten licenses we need, that means the production license would be around $50,000 per year. That might not be the exact number, but I think it is close enough to be correct. With that, there are not any additional costs. Probably you can pay for support and for training if you want something like a dedicated workshop. That might cost more as an add-on but there is no additional cost for using the tool itself.  

I have a really good impression of Alteryx at this point in time. So I would suggest to people who are considering this type of solution to go ahead and try it. If you are looking for a self-service data analytics tool, Alteryx is a good solution. It does a good job of end-to-end integration as well as accessing data from multiple sources or email modes. Then you can push data out to Tableau and Power BI or tools like that if you need other capabilities. So, 'go ahead and try it' would be my recommendation.  

On a scale from one to ten where ten is the best and one it the worst, I would rate Alteryx as easily a solid eight. From my use case and considering the point that I'm not seeing and using everything, I can't give it more or less. But from our use case and what we have in our company, it is pretty good for several purposes.