Alteryx Reviews

We primarily use the solution for basic data analysis and forecasting.

The giga size is very good. 

The solution offers excellent predicting power. The accuracy and confidence have been great.

You don't have to be tech-savvy or technical to use the solution. There's a lot of drag-and-drop functionality.

The visualization could be improved, as could the BID bots.

The solution could work on the BI side of the tool to make it a bit better.

I've been working with the solution for five or six months.

I've liked working with Alteryx so far.

The solution is totally scalable. Users can work with the data at all levels and determine if you want to scale it. You just have to choose the product model with the larger amount of data.

The technical support is very good.

I've worked with other solutions such as Knime, among others, however, in a corporate environment, I've only worked with Alteryx.

The initial setup is very straightforward. You just need to go through the manual. Reading it makes everything easy.

Each deployment is different. Some jobs seem easy, but then the back end is complex. Other times it can be deployed in practically one click. It all depends on the customer requirements.

We're Alteryx partners. We handle various deployment models, depending on the client's needs.

I've worked with other solutions such as Knime, among other tools for data science and data prep. It's great that this solution is just drag and drop and easy.

I'd recommend the solution. I'd rate it 9.5 out of ten.

I implemented Alteryx for a logistics company. 

It helps clean messy data and provides spatial analysis.

We create workflows to automate central reporting and visualize data directly in Tableau. 

We use Alteryx everyday. Using the product, we earn a lot of time, which is used to do other tasks.

Multicategories exist in Alteryx: These categories are flexible and answer a lot of controlling rules.

• Alteryx could be improved in the area of analytics and central governance. 
• The next feature release should include easier reporting. 
• Improvement in the coexistence of In-Database, Python and R tools.

• Data integration
• Automation
• Data analytics

It saves time on a lot projects.

The analytics are easy.

In the database, it should be more functional and connect to more big data, especially using IPI.

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.

We primarily use the solution for automation, data cleansing, QA, some data science and machine learning software for the company.

The product's Macros probably are one of the most useful aspects. It's obviously useful overall if you just combine all the tools, however, in terms of usability, the Macros really stand out. 

Using the Alteryx scheduler, and the use of Rtools for machine learning is very helpful.

These are all a bit more advanced, in terms of tools, however, they make for a very good user experience.

We've had a lot of issues within the Alteryx Server. It really could be improved.

There are memory management issues in terms of the Mongo database that they manage. There could be a better user interface for the gallery, which is where we automate the workflows. 

Sometimes workflows tend to queue up, and they tend to get canceled for some reason that we don't know sometimes. 

I've been using the solution for five years at this point.

The stability is okay. It's good on the designer, however, it could be better on the server.

In terms of scalability, the server is quite scalable, however, the designer is not.

The initial setup was easy, overall. It's easy to use and easy to learn and you don't have to be a data scientist or an engineer to use it. It makes the setup very straightforward, as the overall design is very approachable.

The deployment likely took us about two to three months to set up. It was five years ago now, and therefore it's difficult to recall.

We mostly handled the initial setup ourselves. We didn't hire consultants or integrators.

We're just a customer and end-user. We don't have a business relationship with the company.

We have an Alteryx server, and we also use the desktop version or the designer version on our workspaces, which are hosted on the cloud.

In general, I would rate the solution at an eight out of ten. We've been mostly happy with it, however, there are areas in which is could improve.

I am the head of investigation on-site, I use the solution every day for merging and other management of records. I also export data to individuals that need the information we store, such as facilitators.

The solution allows us to manage our databases data.

I think the most valuable feature for Alteryx in a health facility is that it permits cleaning, organizing, and merging of databases such as Excel and Access. This is very valuable for us since we have many databases.

The interface could be improved. Currently, when you are looking at the information on the screen it is broken down into three parts of the screen. For example, when doing algorithms and you want to see the results on the screen this is when it gets really difficult to view. However, this is fine visually to work with on a big screen but when you are working with a laptop screen it is not very comfortable. Everything is very small.

You have to know to use it. You have to have some training and it's hard. This could improve.

I have been using the solution for approximately 10 months.

I have not had any issue with the stability of the solution.

Since I only have one license to use the solution it is difficult to know about the potential scalability.

The setup is really easy.

The install took approximately 35 minutes.

The license is really expensive, we cannot afford to have two or three. It takes away all the budget of my area.

I would recommend the solution 100%. I really think it is a better platform than competitors.

I rate Alteryx a nine out of ten.

The solution is primarily used for data preparation, data cleansing, and, in some cases, predictive analytics. That said, the major use is in the preparation of data.

I am the sales guy. I don't work with the product every day. However, I hear from my customers that they like the easy-to-use interface. Another feature they like is the in-database feature, and, right now, the new intelligence suite add-on they delivered a couple of years ago.

The support is very good.

The principal problem is the pricing. They're expensive products. They don't have an option to scale the product while the customer is starting to use the product. There are no options for customers that want to move from smaller scale and expand the solution to the rest oth the organización. The Designer (pleople who develope models and flows) cost 5,195 USD each one, but if you want to collaborate, you have to buy a server which cost 80,000 USD not matter how many users you have, so you cannot scale from a small group of users to a large one.

It's only available as an on-premises solution.

It would be ideal if the product offered some sot of option for smaller companies.

The solution is hard to scale in that it is pricey. You need to buy a server. You would need to have the correct sizing, and, if you don't, you would need to buy a bigger server, which is expensive. They don't have a way to grow the product progressively, in a progressive way.

The support is very good overall. We are satisfied with their level of service. They are knowledgeable and responsive. 

We also use Dataiku. We are partners for both products.

In the case of Dataiku, they have a stacking in Amazon. We are offering this option right now. I know that they are going to deliver an option in Google Cloud also. Alteryx, on the other hand, is only on-premises.

The licensing model that they use makes it a very expensive proposition for many companies. It requires buying a server, as it is only on-premises. 

In order to have designers, and, if you want to collaborate, you have to buy a server. If the designer is $5,000, and if you want a server, you have to pay $80,000.

I've been searching about the difference between both Dataiku and Alteryx products. I'm working to evaluate the two and compare them to each other. Technically, we have a customer who is comparing both products, so we have to prepare a document comparing them for that purpose.

We are Alteryx partners.

I'd rate the solution at an eight out of ten. It's a very good product, however, the pricing makes it quite expensive.

In comparison, I love Dataiku. I've seen it's a very, very good product and we are working hard to start offering this product to all our customers. They have an option for companies to from medium-size to large-size. However, they don't have options for small customers. That said, you can start from, for example, an investment in $30,000 and up. You can go progressively investing in the product. That's not the case for Alteryx.

The primary use case of this solution is mainly for advanced analytics, including some bison-scripts for the machine learning on data, as well as data cleaning and data processing activities.

The connectors are a very good feature and Alteryx can connect to a lot of data sources and there are a lot of options. We can use the ready-made models in the solution for advanced analytics, and we can also include our custom scripts. It gives a lot of flexibility and there are a lot of characters that are available. It's a very user friendly product. 

The deep learning models are not currently supported by Alteryx. The world is moving towards more advanced machine learning with deep learning models and integration, and Alteryx should do the same. The advanced features are not there, given that most companies are constantly increasing the size of data. Alteryx is not very robust when it comes to working on large sets of data. The new platforms that are coming onto the market, even the open source technologies, are moving really fast. Alteryx needs to find an edge.

I've been using this solution for at least a year. 

This product is quite stable. 

There's room for improvement with the scalability aspect, because the size of data always increases and it is not an optimal solution for processing large sets of data. For a smaller data size it works really well. Our developers work with Alteryx and we have close to 10 users. We don't have plans to extend usage. 

I've only had one contact with technical support and they were very responsive. 

The initial setup was easy. Alteryx is a drag and drop tool so the deployment was quick. It doesn't require a lot of scripting.

Licensing is on an annual basis but I don't work in procurement so I have no idea of the cost. 

Any company considering a data processing platform which is light, offers good flexibility, and deals with moderate or low sized data, will find this is a really good spend. If, on the other hand, you're working on big data and a higher volume of data, this may not be the right choice for you.

I rate this solution a seven out of 10.