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Composable shines versus Alteryx

Lars Fiedler 0

We’ve written previously about the numerous products that market themselves for Data Analytics and Business Intelligence. As we noted earlier, this can be confusing, but we are typically successful in demonstrating how Composable Enterprise differentiates itself from other products. We’ve recently had the opportunity to go head to head with Alteryx, and we were quite pleased with the results of the demonstration and audience reaction.

A Global 2000 Company, one of the World’s 25 Biggest Insurance Companies, invited us in for a head to head showdown with Alteryx. The rules were simple: they wanted their own staff to walk through a single use case on both platforms and demonstrate to a team of roughly 30 sales and marketing analysts how to complete the task in both products. The selected use case is a common task performed by analysts within Sales & Marketing business units across most firms: match leads from one report to an internal marketing database. The specific requirements in this case made the problem a bit more challenging:

  • Match must be a fuzzy match on a combination of fields (name, address, etc.)
  • Fuzzy match algorithms should be flexible to user needs
  • Match to a marketing database (Salesforce) containing 800,000+ records with low time latency
  • Output a formatted xlsx report of the results
  • (Optional) Output a quick visualization of the results

While Alteryx did not meet all the requirements, Composable Enterprise demonstrated its ease of use, flexibility, high performance and extensibility during this demonstration. The solution in Composable Enterprise was a simple dataflow application:

Here, moving from left to right, the top branch pulls an Excel file with the records of leads being matched, while the bottom branch reads the 800,000+ records from the source marketing database. These two data sets are brought into a single module where the fuzzy matches are computed, and were the output is then sent to both an Excel report (top right branch) or into a visualization (bottom right branch).

Some highlights to note:

  • The entire process, including the fuzzy match computation, took just 35 seconds!
  • The Fuzzy Match algorithm can be modified and extended. To do so, the user just needs to drill into the single Fuzzy Match module, and explore the dataflow application it represents. Composability at its best!

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  • Visualizations inside the dataflow can easily be generated!

Alteryx failed in two substantial ways. 1) While the configuration of a visual workflow was similar to the Composable solution, it could not complete the computation. With it being a desktop application, Alteryx’s in-memory tables and computation are limited by the hardware of the user’s machine (in this case, a Dell Latitude laptop). As noted earlier, Composable, a modern, cloud-native web application, completed the computation in just 35 seconds. 2) The Alteryx workflow could not be easily customized. While there was a fuzzy match workflow to use, its configuration was not extensible. In Composable, the end-user is given complete flexibility to extend any process.

So, while the number of products in the Data Analytics and Business Intelligence space remains large, clear usability, extensibility and performance features sets Composable Enterprise as a disruptor in this space.

Lars Fiedler

Lars has comprehensive expertise building large complex software systems, and has served as a Software Engineer at MIT’s Lincoln Laboratory since 2010, where he began developing Composable Analytics. Prior to joining Lincoln Laboratory, Lars worked as a Software Engineer at Microsoft Corporation from 2006 to 2010. Lars received his MS in Computer Science from Georgia Institute of Technology in 2004, and his BS in Computer Science from Georgia Tech in 2003.