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We use the product for data transformation and analysis. We primarily use it for data preparation, analysis, and sometimes generation. So, it covers a wide range of tasks beyond just loading data.

Alteryx has a good UI. We use it frequently in our projects. The tool comes with drag-and-drop features and is easy to understand for business needs. One situation where Alteryx's advanced analytics capabilities were particularly beneficial for us was during a forecasting project. Unlike Python, which requires coding, Alteryx simplifies the process significantly. With Alteryx, users can adjust parameters within the user interface without writing any code. 

Its user interface is one of the tool's best aspects. Once we became familiar with it, we found that tasks can be completed within seconds or minutes, especially for small data analyses. The tool's computation time is also commendable.

We have integrated the product with Tableau and Python. 

There have been some issues with licensing, particularly with the increased prices. This has led some companies, including ours, to consider reducing the number of licenses or potentially discontinuing their use of Alteryx.

Since most of our team members work with Alteryx, deploying workflows to clients who may not have licenses became a hurdle. Every user needed a license to run the workflows, which could pose difficulties, especially for clients who only needed occasional access for small data analyses. Unlike other software like Microsoft Power BI, which offers free trials or user licenses for basic functionality, Alteryx doesn't provide such options, potentially limiting its accessibility for occasional users.

It should improve scalability. 

I have been using the product for more than ten years. 

I rate the tool's stability a ten out of ten. 

I rate the solution's scalability four to five out of ten. 

The tool's deployment can be completed in a day or two. We have 24 engineers to handle it. 

The solution's deployment was done in-house. 

I rate the tool's pricing a two out of ten. 

Overall, I would recommend Alteryx for complex data analysis needs. We are considering moving to Tableau Prep. 

The solution is great as a standalone product. However, there are certain add-ons provided by Alteryx that are very useful but come with additional costs on top of the licensing fees. This aspect can be a bit of a downside. So, overall, considering this factor, I would rate Alteryx at around seven out of ten.

There are numerous benefits to using Alteryx. It's user-friendly and allows for data transformations, making tasks easy to understand. 

My main use case for DataRobot is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of the examples is I use DataRobot to predict which students are likely to accept the university offer. It basically helps us and the admission team to focus their efforts more efficiently. It also helps us with data matching and cleaning in large data sets, which reduces manual work.

The prediction helps our team and the admission team to prioritize outreach to the students who are most likely to accept the offer. They inform marketing and follow-up strategies as well, making efforts more efficient and quicker. One example is if DataRobot predicts a student has a high likelihood of accepting, the team can send personalized emails or call them to provide guidance and support directly to these students. It basically focuses on these specific students which have been just highlighted by DataRobot. It also reduces time spent on students who are unlikely to enroll, allowing us to use our resources more efficiently only on the people who we think are actually going to come back and enroll with us.

I do use DataRobot for many other things as well. For example, other than the target of student enrollment, I use DataRobot for data cleaning. I do the cleaning of deduplication as well. I also use this to detect any anomalies. It basically helps me to automate all the repetitive tasks and saves me some time. One example I can share is I use it to flag duplicate student records across multiple systems, which used to take us hours to do before, and now it's done a lot more quickly by using DataRobot.

There are many features that I appreciate about DataRobot. Some of the features which I personally prefer are the ones that save time. First, I would start with the automating features. If I want to do the data preparation, clean the raw data, or upload a student admission data, DataRobot automatically generates features such as the number of applications in the last month and previous offers accepted, and it can remove duplicates as well. This is one of my favorite features. Secondly, it tests my machine learning models for me, and the testing and selection are very efficient. For example, if I want to run many algorithms, DataRobot will compare them and pick the best one, saving me time from manually checking which one is the best. Lastly, another feature that I appreciate is the integration and scalability with our cloud system. It helps us connect with the various data sources we work with in our university, such as SITS, Azure SQL, and CSV exports, allowing DataRobot to handle joins and feature engineering effectively without requiring extensive coding from me.

DataRobot has positively impacted our organization in many ways. First, it has improved efficiency; tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours. This ultimately helps us make better decisions, particularly with admission data where we can rely heavily on the predictions made by DataRobot. It has also helped to reduce a lot of manual work and has allowed me to execute automation tasks more quickly. Furthermore, DataRobot provides scalable analytics, enabling us to run multiple predictive models across different departments without needing extra staff or extensive infrastructure. For instance, it allows the admission team to prioritize outreach to students likely to apply, ensuring we spend our resources effectively.

Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial.

In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.

I have been using DataRobot for about one year, which is about the past 12 months.

DataRobot's customer support is good but could improve with quicker response times and better documentation or community support. The scalability is robust, managing large data sets, although it sometimes slows down when processing bigger data, but being cloud-hosted enables automatic resource scaling, which supports collaboration across teams.

Neutral

Previously, we used different solutions, including manual model building through Python, Excel, and Azure ML for some projects. We switched due to the burdensome manual workflows that were time-consuming and required extensive coding, making it difficult to test multiple models quickly. DataRobot allowed us to experiment faster, achieve better model accuracy, and facilitate simpler collaboration without needing high-level programming skills.

We have indeed seen a return on investment. On average, we're saving about 10 to 15 hours per project. The efficiency has greatly improved; tasks that used to take a day now take mere hours. While we haven't reduced staff, their workload has lightened, enabling them to accomplish more within the same timeframe. The standout metric remains the 10 to 15 hours saved per project.

While pricing falls more under my IT colleagues, from my perspective, the overall experience feels justified. The premium pricing is reasonable for the value provided, and I'd say it's worth the investment. The setup cost was minimal because it's cloud-hosted, eliminating the need for heavy on-premises infrastructure, allowing us to start using it immediately after purchase.

We evaluated several options, including Azure Machine Learning and manual Python workflows. DataRobot offered the best combination of automation, model accuracy, and ease of use, which ultimately saved us significant time and resources, making it the clear choice.

For those looking into DataRobot, I recommend starting with a small project to grasp the workflow before scaling. Utilizing the automations offered and dedicating some time for training is key, along with collaborating and sharing models and dashboards within the team to maximize the platform's value.

DataRobot is a powerful, user-friendly platform that saves time and provides accuracy, although improvements are needed in handling larger data sets and flexibility. You can use my real name for the public review. I have provided this review with a rating of 7.