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 use Alteryx to modify and modernize data based on different types of data requests. The data and the data source may be in different forms, resulting in unstructured data in the database. We use Alteryx to give good shape and size to such unstructured data.

Alteryx makes it easy for the end customer to see clean data in a structured form. Alteryx has many features, such as info data, data cleansing formula, the select tool, union, summarize, and text-to-form. We use Alteryx in the database for reporting, documentation, email, and data preparation.

To use Alteryx, you need to think like the people who write software codes. The formula we currently use in Alteryx can be automated. This would be helpful for people using Alteryx who are not from a coding background and can write the formula differently. Whoever is using Alteryx should feel some convenience using it. We found most of the useful features built into Alteryx, but they all are on a paid basis.

I have been using Alteryx for nine to ten years.

I have not faced any challenges with stability.

Alteryx is really scalable. It adds value and fits our business. More than 300 users are working with Alteryx in our organization in India. I do not have the exact figures, but 2000 users globally use the solution in our organization.

During COVID, they had on-call engineers for very good technical support. I would like to have that support again because they have now closed it, which is difficult for people who need it. They should continue that kind of support again. It would be good if they could provide technical support on screen.

We did not use a different solution but we are planning to use Google Cloud Platform.

The implementation is not a big thing for Alteryx. It is simple and easy to use.

We have seen ROI with this solution. However, if we explore other tools in the market, we will probably get a better ROI.

The solution has a more costly license than other tools in the market.

Alteryx is the best solution for people who want their data in a structured way. It is a bit complicated for a new user. The complexity of the tool is evident by the fact that only if you provide an exact input to this tool it will give you the output. However, if you know a little about Alteryx, it is a very good tool for you. We are using an on-cloud deployment for this solution.

Overall, I rate Alteryx a seven out of ten.

Right now we have huge amounts of data metrics. We have a marketing team. We have sales. We have a networking team. We have door-to-door. In terms of data preparation, this solution was the primary method to get the data from many sources. It is easy for us in terms of integrating with the cloud and we can do most of the preparation. We can enable Alteryx to handle our data preparation, and sometimes you can enable it to set a pipeline and it can be stored in CCP. We mostly use it for working with Tableau. We're currently running it for multiple use cases.

The best thing is that the product is low-code and mostly no-code. It can be used dynamically by both customers and the internal team also. 

In terms of speed and reliability, it is comparatively good and actually leads the pack in terms of competition.

It is easy to set up.

The cloud deployment ensures it scales easily.

The solution is extremely stable.

The solution is improving continuously. They have, for example, just added automatic insights. If they continue to improve on their overall service offering, that would be ideal.

I've been using the solution for quite some time - almost four years. I used it when it was being a stand-alone application and now we are using Alteryx Cloud with integration to Trifacta.

The solution has been highly stable and reliable. The cloud is very stable. There are no bugs or glitches, and it doesn't crash or freeze. 

We have 200 to 250 people working with the product in my company.

We faced a few issues with scalability in the past when it was a desktop application. However, after we started to use the Alteryx Analytics Cloud, this application has been really stable in terms of scalability. Initially, we started the trial with 30 people. Now we have around 200 to 250 people who currently use Alteryx Cloud. It scales quite well for us now.

We have a special SME which is assigned by Alteryx to our organization. If we have any problem, we reach out to them in terms of any support. Mostly, myself and my team have never needed to connect with tech support as it is highly stable. The only thing we've had problems with is having issues around how we can automate the workflow. When we have questions like that, we just check with them.

We've been testing RapidMiner for quite some time. We wanted to have another option in pipeline if there happen to be any stability issues and budget issues happening or if the cost of Alteryx becomes an issue. However, in terms of RapidMiner, we have not fully used it for multiple reasons.

The initial setup is very simple and straightforward. I've used it for a long time and previously used it as a desktop application. While it was simple then, now that they've moved to the cloud, it's even easier. 

The solution is now quite expensive. You need to have a budget for it. However, if you do, it is worth the cost.

We're a customer and end-user.

We are using the cloud deployment and, therefore, always on the latest version as it updates itself. 

I'd recommend the solution to others.

I would rate the solution nine out of ten. It's been highly useful for my organization in terms of data strategy. In terms of usability and scalability, Alteryx is the best.

It is mostly used for data validation rules. For example, for many legacy systems files, we check whether the count in one file is matching the logic that we have in another file. If it doesn't, then we fill that particular data load. The ultimate destination for that is a database, but to avoid data mismatch or the garbaging of data because someone didn't put files in a proper format, we are doing the validation with Alteryx. So, it's basically for data cleaning and removing some line characters from the data in the files so that there are no data issues while using SQL Loader.

I haven't worked much with Alteryx because my primary work is in Informatica. I work with it only when I don't have any option or it is too tedious to work in Informatica for a particular file or schedule a load. For example, if the load will fail in Informatica, or it will not allow the rest of the stream to continue, I use Alteryx. In Alteryx, I have that option. I can have a dedicated flow that will run for that particular file. For my 100% ETL work, 10% work is in Alteryx, and 90% is in Informatica. So, I haven't explored it much, but whenever any requirement comes, I work on it. So far, I was able to utilize it for whatever I need. Everything I needed was already there.

It has everything that one needs. Whatever you want to do with the data can be done with Alteryx. However, we haven't implemented it on a large scale because we don't have a physical server. We have a virtual server for this, and there are limitations to that. So, we use it for smaller data chunks, not bigger data volumes. It is mostly for files, not for databases.

There is a community for Alteryx, but it is so spread out. For example, if I face an issue with Informatica, I have Informatica forums that keep on adding to that. They have a very cohesive structure when it comes to the issues and their error messages. They are very easy to use for Informatica issues, but in the case of Alteryx, the information is too spread out, and it is difficult to find the answer to a particular issue that someone faces. If Alteryx can organize its forum and the issues in a better way, it will be easy for developers.

I mostly used it for flat files, but I have many colleagues who reported that to tune a query, in case they want to directly connect to the database, there is no option to optimize the performance of the query, as we have in Informatica. With Informatica, we can use hints and other things and they directly hit the database, but with Alteryx, that is not the case. So, when it comes to database connectivity, the performance is not as good as a flat file as a source.

I have been using Alteryx for around 10 months. 

It never crashed in the last 10 months.

I assume it would be scalable without any issues. Most of my work has been related to files without any databases or other connectors. 

We have more than 5,000 people using it. This is a free tool across our organization. So, everyone uses it in case they want.

I only installed Studio on my laptop. The server was installed way before I joined. So, I can't comment on that, but the Studio setup was easy. It was almost like Tableau.

It has Excel and diagram functionalities. So, not much technical knowledge is required to use it. If you are aware of both or have Tableau and Excel experience, then you are good to go.

I would rate it a seven out of ten.

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.

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.

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.