McChrystal Group Accelerates Machine Learning Data Processing on AWS

The obstacles they faced

While helping businesses with organizational development and process improvement, the McChrystal Group uses surveys to get input from various stakeholders. The surveys include multiple-choice and open-ended questions designed to identify underlying issues. For some of its larger client accounts, as many as 20,000 responses can be received on a single survey.

Survey reviews relied heavily on manual processes, and as a result, were time-consuming and labor-intensive. The firm wanted to develop an automated natural-language processing solution that would be significantly faster and able to identify key topics in the open-ended questions.

Having previously worked with Rackspace Technology for an infrastructure move and data flow modernization project, McChrystal Group knew the advanced cloud capabilities of AWS. The firm also had a very clear idea of the solution it was looking for but needed an external development partner to meet an aggressive timeline. It chose Rackspace Technology to leverage its machine learning expertise.

What we achieved together

One of the key benefits of automated language analysis is the faster identification of latent topics in survey responses across large datasets. By automating the analysis on AWS using natural-language machine learning results are now available in minutes versus days for teams of analysts to process. Also, the new process frees up the analysts’ time to focus on other important tasks, such as working with the visualization results, rather than spending days creating the visualization.

Another advantage of a machine learning solution is that it can detect subtle, hidden and abstract topics that might be missed by human analysts. It’s also more comprehensive and consistent in data processing. This enables McChrystal Group to provide its clients with more accurate and effective solutions to identified issues.

Once deployed, the Rackspace Technology natural-language machine learning solution enables a quick visualization of survey results and rapidly identifies the underlying response themes. It also provides a classification of business challenge types, quickly identifying the specific challenges that McChrystal Group’s clients face, such as indications of corporate communications issues, process issues and merger issues. The solution adds texture to the analysis and ensures a standard process focused solely on the extraction of relevant data, including subtle topics, enabling them to provide clients with the intricate guidance they require to manage any identified issues.

The solution is also designed to be scalable, with the ability to process data of varying sizes from its small and larger clients. The scalability is so advanced that it could process all the data from all client accounts at once, along with processing data from some of the largest organizations in the world. In addition to this, the solution requires minimal management, leveraging AWS’s high availability, redundancy and scalability. The only management required is for the visualizations, or output for each client account.

The Rackspace Technology automated, machine learning, natural language processing solution on AWS will enable the firm to replace a time-intensive manual process that consumed considerable resources with an automated process that is significantly faster, more consistent and more replicable. Not only does the solution increase the rate at which survey results can be processed to extract relevant data, it also increases the accuracy of the data enabling McChrystal Group to provide more holistic and effective solutions to its clients.

“Our experience working with Rackspace Technology gave us a lot of confidence that it would be the right partner to deliver high-quality results,” said Victor Bilgen, Partner and Head of McChrystal Analytics. “Rackspace Technology understood our vision and displayed a depth of expertise in designing and implementing a solution that processes our surveys faster and more accurately than before.”