Likelihood to Recommend
Well Suited Scenarios
Data Preparation and Integration: Alteryx excels in data preparation tasks, such as data cleansing, transformation, and integration. Predictive Analytics and Modeling : It enables users to perform complex statistical analysis and models and algorithms, making it more valuable for cases like customer segmentation, demand forecasting, fraud detection. Automation WorkFlows: Amazing for repetitive data processing task, or automated rescheduling.
Less Appropriate Scenarios:
Real Time data processing, large scale big data processing, Heavy statistical computations. Read full review
MATLAB really does best for solving computational problems in math and engineering. Especially when you have to use a lot of functions in your solving process, or if you have a nonlinear equation that must be iteratively solved. [MATLAB] can also perform things like integration and derivation on your equations that you put into it.
Read full review Pros Pulling data from multiple disparate data sources. Allows users to see the data at every step of the workflow to be able to cleanse, analyze, and optimize the data. Provides an analytics platform that is easy for users of all levels to thrive in whether they are just starting out in their analytics journey or they have a master's degree in Data Science. Read full review It has a very user friendly library which helps users learn this software fairly quickly in a short span of time. The graphical user interface provided by the software is really good. The code that a person writes allows options for debugging. One can visualize the flow of control of their code inside MATLAB. Read full review Cons A larger library of data sources being leveraged/licensed through Alteryx directly similar to the Experian and Dun & Bradstreet data that is available now would be ideal for additional data enrichment. The ability to take R and Python-based data insights/model outputs/forecasts/etc. and pull directly into downstream tools and reporting is a bit lacking and could use improvement. Read full review MatLab is pricier than most of its competitors and because of this reason, many organizations are moving towards cheaper alternatives - mostly Python. MatLab is inefficient when it comes to performing a large number of iterations. It gets laggy and often crashes. Python is better in this regard. There is a limited number of hardware options (mostly NI) that can be connected directly to the data acquisition toolbox. Read full review Likelihood to Renew
We've developed a working partnership with Alteryx. As an enablement suite, we're continuing to innovate and deliver great products with use of Alteryx in our solutions. Alteryx use expands to our global product development teams and is in use in multiple parts of our organization. Alteryx also delivers Experian demographic content to other clients in their product offering. We're highly likely to renew, but that decision is way above my pay grade.
Read full review Usability
I've found that while some things might take a little longer to create, the flexibility of Alteryx allows you to perform any function needed. I haven't found a use that was not available in Alteryx yet. APIs and XMLs can be created to perform certain functions. In addition, CMD line commands can be sent using Alteryx to perform certain functions as well.
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MATLAB is pretty easy to use. You can extend its capabilities using the programming interface. Very flexible capabilities when it comes to graphical presentation of your data (so many different kinds of options for your plotting needs). Anytime you are working with large data sets, or with matrices, MATLAB is likely to be very helpful.
Read full review Reliability and Availability
I use many programs and compared to others, Alteryx virtually never goes down, freezes up or gives an application error. Over a 4 year time period that I have used this program, any of these may have happened 3 times. It is an incredibly stable program that I feel completely confident in.
Read full review Performance
I have not complaints with the system. there are after events that can be added to flows that send emails with a stack trace for visibility into any errors. Stack traces include Tool IDs at the failure point to help debug. Overall the flows run quickly and once I work my own bugs out they are reliable
Read full review Support Rating
Stellar, bar-none. Some of the best support folks of any vendor. The Alteryx Community is the most responsive and supportive. On the rare occasion of a release issue or bug, we've been able to get quick help to solve the core problem. Alteryx does not play the blame game. They genuinely help the users solve their issues or respond to questions
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The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.
Read full review Implementation Rating
There is really not much to it (the installation, that is). Once you get it installed, along with any of the add-ons (demographics, R, etc.), you are up and running almost immediately. There is really no additional setup. You can immediately begin blending data, running demographics, performing spatial queries, running predictive analysis, etc. And for many of these functions, the learning curve is quite easy.
Read full review Alternatives Considered
Knime is open source and free. It also positions itself a little on machine learning. But the user experience and the features available are far less powerful than Alteryx. I was a former user of Lavastorm (ancestor of Infogix Data360) and got acquainted with the low-code graphical approach thanks to this tool. RapidMiner is the Alteryx for machine learning, but is not at all as tooled as Alteryx for data prep. I also like to use Orange BioLab which is really for machine learning and offers an excellent low-code/graphical user experience... on top of being free.
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How MATLAB compares to its competition or similar open access tools like R (programming language) or SciLab is that it's simply more powerful and capable. It embraces a wider spectrum of possibilities for far more fields than any other environment. R, for example, is intended primarily for the area of statistical computing. SciLab, on the other hand, is a similar open access tool that falls very short in its computing capabilities. It's much slower when running larger scripts and isn't documented or supported nearly as well as MATLAB.
Read full review Scalability
Individual analysts can quickly generate results using their own copy of Alteryx Designer. But using the Server and developing macros for more complex needs can be time consuming.
Read full review Return on Investment Error handling - allows controls to be built into workflows easily and allows them to be isolated and spat into control reports that can be easily reviewed and audited, thanks to the ability to create multiple outputs in one go. Time-saving - saved huge amounts of time, especially when moving Excel processes into Alteryx. Product development - allowed my firm to create products that we have been able to market and sell to clients. Read full review MATLAB helps us quickly sort through large sets of data because we keep the same script each time we run an analyzation, making it very efficient to run this whole process. The software makes it super easy for us to create plots that we can then show to investors or clients to display our data. We are also looking to create an app for our product, and we will not be able to do that on MATLAB, therefore creating a limiting issue and a new learning curve for a programming language. Read full review ScreenShots