Alteryx Reviews

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. 

Alteryx is one of our primary tools in our company and we use it mainly for future analytics. We are primarily trying to determine what the past has looked like and then apply predictive analytics. 

I think that our success with this product is mixed. It has probably gotten some people started easily or fairly easily on extracting data. But I also think that to some extent, it puts a limitation on us, because I don't think that we really realized the training that you need in order to be really strong in the product.

I like the fact that you can easily blend data from different platforms. That is one nice feature.

Additional comment: You can connect multiple input tools to different platforms and use joins/union to either concatenate or blend data in a seamless fashion.

Alteryx brands itself as a tool that you can use to avoid having to code. In reality, however, as soon as you start with your task, it gets just a little complex. Alteryx is just as complicated as coding, in my opinion. It is great for relatively simple data mining and data extraction type tasks, and I sure that it can do very advanced things, in particular when you look at the tool installation. That said, as soon as it becomes advanced, it is not very easy to use. In fact, it's quite complicated, so when persons with a coding background attempt to use it, they fall back on trying to code something, instead.

The biggest complaint that I have is that the online help is not very comprehensive. It is very formal, but there are not a lot of examples and use cases, and there are many signs there are difficult to understand. I think that it's a challenge.

In the future, I would like to see improvements made to the machine learning capabilities such that they are comparable with Python.

I have been using Alteryx for three years.

My impression is that it's quite stable. You can easily make small mistakes that have an impact. For example, we use Alteryx gallery and sometimes it seems what when you have a relative path versus an absolute one, it is tricky to keep track of.

I have never spoken with technical support.

This is my first job in analytics where I have used a dedicated tool. Prior to that, I did some work that involved coding.

Although I use this product regularly, I used to code for many years and I am not a big fan of tools like Alteryx. I don't think that it's a good product, and the reason is that when the use cases get more complex, the models get exponentially larger and more complex to put together.

My advice for anybody who is implementing this product is not to underestimate the need for training, in particular, when your use cases get a little more complex.

I would rate this solution a five out of ten.

The team uses Alteryx for many things because they code various algorithms to create different dashboards. These are used as solutions for various things from sales to customer experience to whatever else the customer needs. So there are many areas where the team is using the product. There is not one, but as of now, there are some 30 different use cases that we have.  

The ease-of-use has allowed our business users to directly create their own solutions without the use of additional development resources.  

I think the most valuable feature is the ease of use for my business scientists. They do not need to be experts in R and Python coding, and they can do their complete analysis projects without knowing the code. I think that one of the biggest reasons we are depending on Alteryx.  

I'm sure there is always development happening with the product, but I think there should be both cloud and on-premises options. We have not seen a cloud deployment and have not heard they will be making one available. I think if there is the cloud deployment available, it would definitely be more convenient for some users to engage with and use while lowering the implementation costs.  

Cloud deployment would be easier for us and our clients because a lot of services that we push to the clients are through the cloud only. It is expensive to buy additional licensing, and it is time and resource consuming to install onto our own hardware — which itself requires maintenance and administrative attention. On-premises can raise the cost of deployment over just using a cloud enrollment option.  

We are one of the main solution providers in the region and we have been using this solution for almost two years.  

The solution is stable. We experience no bugs, glitches or crashes.  

We can scale usage of the product as much as we want and we do that depending on the instructions that we have from the client. It is quite scalable in that regard.  

We have more than 50 users who are using this solution within our company. They are a mix of data scientists, administration and business users as well.  

Alteryx is used quite extensively in our organization and we are planning to increase the usage in the short term.  

We are sometimes in touch with Alteryx technical support. If I had to evaluate them, the waiting times are not long, the response, in general, is pretty good. So we are satisfied with their support work.  

I would say the initial setup is straightforward. It takes the technical team just a few days to deploy this solution. It is deployed in days, not months or weeks.  

In terms of deployment, you only really need one person to complete it and that is good enough. You might use two technicians depending on the skill set, the scale of the deployment and the business areas the client focuses on. The maintenance is pretty cheap. The same people that do the deployment manage it for us.  

We do not use an integrator, seller or consultant for the deployment as our team has training and they do these things.  

We have a yearly cost that we pay for the licensing. We do not pay any costs in addition to the licensing fees. The technical support is included as part of the fees that we pay.  

Before we choose Alteryx, we did weigh other options from different vendors. In fact, we still use other technologies. We cannot properly service client needs by just using Alteryx as the only solution we offer. Because client needs differ, we are using other products that we evaluated and determined were also serviceable technologies to deliver for client use.  

Advice that I would give to people considering Alteryx as a solution depends on the real needs from business to business and in accord with what use cases they have. I think Alteryx definitely takes away the potential problem of having to have a lot of technical expertise. Any business user can use Alteryx to develop their business case and use the program's options much quicker without having the technical knowledge and programming know-how. It really depends on the skills and resources of the individuals in the company. They should use Alteryx if they want users to be able to walk through the process and deploy their own use cases without having a deeper technical knowledge and skills that other products may require.  

What we have learned from using the product is that it is definitely a great advantage for the business users to not need to have great technical skills and programming knowledge to complete their analysis. The business users can build their own analytical use case, visualize what they want to do more easily and quickly, and revise it without any dependencies on other staff and additional communications.  

On a scale from one to ten where one is the worst and ten is the best, I would rate this product as a seven-out-of-ten.  

Data blending and complex data manipulation and augmentation.

• Fast return on investment
• Increased data analytics productivity.

Workflows and a large number of tools were a quick QA methodology and rapid prototype creation tool.

More emphasis on extensible R and data augmentation interface design.

ETL.

Fast data processing and cleaning.

Crosstab and transpose.

Add inbuilt interactive visualization in Alteryx.

The drag-and-drop capability to create a workflow works like drawing a flow chart. Each icon represents an action. You drag the action on the canvas and connect it to the data source, make configurations on the action, then run the workflow. When data goes through the process, the action will execute and you get what you want afterwards. The actions are intuitive and you can design you own way to do the same thing.

Alteryx automated some manual standard processes and the workflow is easy to maintain. Staff do not need to wait for IT to prepare data once a data warehouse is set up. The output can be shared within the company which other teams can make use of to reduce rework. Advanced analytics such customer response prediction, time series demand forecast can be done without a PhD.

I would like to see the field tracks along the workflow show the route of selected field. That would be easier to do checking or modification when designing a complex workflow.

Also, data profiling would be helpful if audit/compliance requires the info of data transformation. That would be easier to trace the data along the whole process.

I have used it for one year.

I did not encounter any stability issues, at all. This tool is designed for big-data analysis, so basically there are no input/output issues. It can process more than 10 million records in 10 minutes on a normal laptop.

I did not encounter any scalability issues, at all. As long as the server is set up, it can share the workload by the load balancer. It depends on how complex the problems you want to solve are or how many people you want to access them simultaneously.

Technical support is 8/10:

• Active community
• Quick response from client service
• International partners to help in person, on site

We previously used Informatica. The position of Alteryx is not only ETL, and we need to do more than just ETL, so we switched.

Initial setup was very straightforward. You can also download a comprehensive guide from the official website.

It is not cheap. So I would suggest you start with a trial and then evaluate how many licenses you need for your analysts. Basically, I do believe each analyst – regardless of whether they are in the sales operation, data analytics, or planning – requires one license. When they acquire the skills of how to use it, the ROI will be much larger than you can imagine.

We did not evaluate other options before choosing this product.

It would be great to find a local partner to do some training and use the consultation service at the beginning. It would help you and your staff to learn fast.

It is a very user-friendly product that doesn’t require a strong technical background; you can build something within an hour. It encourages self-service, has good capacity and supports a variety of data sources.

We have several Alteryx certified analysts on our team, and we serve customers who want Alteryx. We don't use it in our company.

The philosophy of the citizen data scientist is the key piece, which means the no-code analytics capability. This is the feature that attracted us the most.

It should have Linux support. It currently supports only Windows. It does not support any other platform. Its price should also be lower.

Their US partner management program is actually unresponsive. One of the reasons why we don't have a formalized partnership plan with them is because their partner management team is atrociously unresponsive. This is something that they need to change.

I have been using this solution for a little over a year.

It is stable.

We are currently using it in environments with fairly low operational needs. For our clients, it is more of an analytics workflow rather than a production workflow. The scalability requirement has not really come up for us in practice.

We have worked with RapidMiner in the past. I am also familiar with a few other solutions that came to me. We are also exploring a partnership with DataRobot. There are a lot of overlaps between products. We have also used a few analytics products.

Its price should be lower. The key thing that we see is that talking about ROI is an important element at the time of purchase. Cost becomes a factor in every discussion. Justifying the ROI for these kinds of workflows is always a challenge, and the only way to counter the challenge is by addressing the pricing.

If a customer already has a data engineering and analytics team, I would lean towards RapidMiner and DataRobot, depending on the specific need of the customer. If they want more AI, I would recommend DataRobot. If they want more analytics and data, I would go with RapidMiner. If they don't have a data engineering and analytics team and they're starting something afresh, or if they have a capacity issue with the analytics team, then I would recommend Alteryx. Alteryx works out pretty okay if they don't have a sufficient number of qualified data and analytics people because it goes with the philosophy of citizen data scientists.

I would rate Alteryx a seven out of ten.

We can now provide a single source of truth and build a bigger picture.

The data preparation. We have high reporting needs and the data is stored in many different tools. We are moving to predictive analysis too. Still, we need an easy to deploy solution until the IT department finds a better solution to connect all data sources.

The learning curve is long, and there is lack of e-learning; the tool is not user-friendly to a non-technical user.

No stability issues.

No scalability issues, if it is accompanied with centralization or the organization has a strong top-down culture.

I would rate tech support five out of five. The team is willing to work with you and help with any difficulties.

We were using Oracle BI, and we are still using it. The value add of Alteryx is the agility for making changes, and speed of deployment.

The seat is too expensive.

I am using the desktop version. The only issue is it supports only a 64-bit system so I have to use an earlier version.

My advice to regarding this product is to keep learning and not rely too much on the support team.