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.

I primarily use Alteryx for connecting to various data sources, data transformation and modeling, and preparing reports or extracts or data that we can visualize in other tools.

The most valuable feature is user-friendliness, as Alteryx can be used by those without any coding experience or experienced data scientists as it has the functionality to embed R and Python scripts. Alteryx's versatility and ability to graphically present workflows of data are also excellent features.

Alteryx's development environment could be improved as it requires installation locally and can't be developed in the cloud. You have to push the model into the cloud from your local machine, which is a pain compared to other tools like DataIQ, which you can develop fully from scratch in the cloud.

I've been using Alteryx for two-and-a-half years.

We had some issues with deployability and scalability because we had to develop on our own machines and then push the workflow into the cloud, which caused issues with updates that caused functionality misalignments. Ideally, you'd be able to develop and run production applications in the same environment or platform, i.e. the cloud.

I previously used DataIQ, which is a similar product but is fully in the cloud. I was pretty impressed by it and would probably rate it higher than Alteryx.

The initial setup is quite straightforward, I would rate it four out of five.

We used a central support team from Vodafone Group.

Alteryx isn't extortionately expensive, but it's not cheap either. I'd rate its pricing as three out of five.

I would recommend trying a few different tools to see which one exactly fits your use case before deciding on Alteryx. I'd rate Alteryx as eight out of ten.

Alteryx has helped us spend more time identifying results instead of performing analysis manually. It has helped us in our loading process, including scrubbing data and identifying data elements that need to be corrected. It enables us to understand our data sets a lot better.

The most valuable feature of Alteryx has been Designer. That has been beneficial because we load our data tables for Tableau. We use Alteryx to load the data into a database from various different data sources to come up with dashboards our business can work with.

We've been working with the products a bit over two years now.

The stability is good. There are a few hiccups with specific data sets and languages or formats that the data comes in. That may be a minor problem, but we can work through it. We had some issues looking at XML format in added data, but it wasn't significant.

I don't think we've reached the limits of Alteryx yet. Our strategy is to push the envelope to see how far we can go, especially in terms of our advanced data analytics and using Alteryx to look for other types of anomalies or issues with our data.

We have around 20-plus active users doing transactional data analysis. We will likely increase the usage depending upon the use case and expansion of our global data sources.

Most of the technical support we have used thus far has been internal.

The setup was pretty straightforward. We have a group that handles deployments within our company, and we get our license. They have Alteryx servers set up. I'm just in charge of the users with licenses. We had an implementation strategy that was completed in a few weeks.

The setup was performed by an in-house team.

I can't speak about the specifics of our licensing. Our operating costs are all shared and allocated. That helps keep the cost down.

We evaluated other options, but the decision was made by our corporate leaders, and I can't recall what the alternatives were. 

I rate Alteryx nine out of 10. I might be biased because of what I'm achieving with it. I don't think we've reached the extent of our usage. I want to increase the data analytics with it. My advice to prospective Alteryx users is to have a good plan. Run through some tests in scenarios where you think it might provide you with a good solution.

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.

We were analyzing data trying to find anomalies with the servers and things that were going wrong. Normally in what we do, it can be a very manual process looking for that kind of stuff. Therefore, using this product, I created a workflow that went out and looked across all devices for problems that are happening, and managed to cut down the number of issues that were being created daily.

The amount of labor that it takes us to find issues is pretty high. When you put the automation in place, that frees up your time to concentrate on the advanced analytics. We recognized right away in that first year that the amount of FTEs that we would have to acquire to do that work was a significant amount of money. It just stepped us into automation of many things that we do. That frees us up to be able to do a more mature analysis, and not that day-to-day stuff that you we have to track.

Not everybody's budget allows for having a team of 20. We just don't have that. It's saving us a lot of money in FTEs.

Right away we knew this has a lot of value, even from the perspective of data manipulation and automation. 

There are all sorts of use cases that we could come up with. It does take a little bit of learning and figuring out how to work the tool, however, we've been really successful with it. I even went and presented at one of their conferences our use case. 

Automation is the most valuable aspect for us. The ability to wrap business logic around the data is very helpful. 

It not only helps us with efficiency. You can also flip the steps apart to make that data flow in various ways. 

Most tasks run in less than two minutes and provide us with what we need very quickly. I can wrap rules around the data to look for certain things or to notify people. 

The amount of manual labor that we're saving by writing those workflows is huge.

The solution does have a bit of a learning curve.

I initially managed the product completely. Then, when the user base grew, another group just took it over. What they're struggling with is it's not as mature as Tableau in the user management area. It was tougher to manage the server part of it right away, especially since the user base has grown. When you get hundreds of users in there, trying to manage access to the product is a little tougher than Tableau.

I've used the solution for maybe around five years or so.

The solution is quite stable. It's not as mature as Tableau in that stability area, however, it's pretty reliable. We don't have a lot of problems with it. Initially, we created a lot of cases as we didn't understand how to best manage the product. Now, very rarely do I open a case with support and have issues.

They recently moved to a different support model. Initially, when we came to the product, the support was pretty good. Now, when we open cases, it seems not so good. They're asking customers to pay more money to get a higher level of support. Of course, due to the fact that we don't open that many cases, it's not important to us to get to that higher level. That said, the response time given and the attention to us is not as good as it was initially.

When trying to pull large datasets into Tableau right away we realized that we had to do something different. Prior to Alteryx being available, I would use scripting to stage the data. I happened to notice an ad on LinkedIn for Alteryx, and I thought, "Wow, this is exactly what I need." I talked management into buying it. I was the first one in the company to start using the product.

We used outside vendors to come in and help in certain parts of the implementation.

It's more expensive than Tableau, for example.

There has been some scrutiny on the overall costs. We're trying to go to more open-source, with more products that don't cost so much. I have to continually work with folks on proving that the value of what we pay in licenses.

The solution has recently changed its support process and is now pressuring users to pay for a higher level of support services. That could potentially be an extra charge for some companies. 

We are just customers and end-users.

I would advise other companies considering the solution that the training is important. They've been great working with us on free training for this, that, and the other. The company really does provide a lot of those resources. The community is very good. The knowledge base that's out there in the community is great. A company should just tap into that and any free training they can. It takes a little bit of time to get proficient at it, however, the payoff is there.

Overall, I would rate it at a seven out of ten.

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.