McChrystal Group Accelerates Machine Learning Data Processing on AWS
This global consulting firm accelerated its client survey data processing with a natural-language machine learning solution to help businesses resolve issues faster.
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This global consulting firm was struggling to analyze answers efficiently and accurately to open-ended questions in client surveys.
Soluções Analytics & Business Insights, Artificial Intelligence & Machine Learning Plataformas Amazon Web Services (AWS)Our customer
McChrystal Group is a global consulting firm founded in 2011 by Stanley McChrystal, a retired United States Army four-star general best known for his command of Joint Special Operations Command in the mid-2000s. Today, the firm helps organizations tap into the potential of their people and teams and achieve organizational transformations to better compete in a complex and interconnected business environment.
"We needed to build a natural-language machine learning solution on AWS that would automate our survey analysis process and expedite time-to-value for our clients."
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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.
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"Our past experience working with Rackspace Technology gave us a lot of confidence that its team would be the right partner to deliver high quality results on the project."
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How we helped
McChrystal Group had considered several off-the-shelf language analytics tools for its workflow. However, the firm could not find one that met its specific needs. Having significant experience and expertise in machine learning and natural-language processing, Rackspace Technology was able to suggest tools that would provide the visualizations that could best serve the firm’s use case.
The Rackspace Technology machine learning team worked closely with the firm’s team to first understand what the highest-value output from the machine learning data processing would be to ensure the proposed solution produced the results it needed. With this information, Rackspace Technology was then able to select the right tools and machine learning models to ensure high-value data delivery.
The solution designed by the Rackspace Technology machine learning team uses Amazon Athena, AWS Glue, Amazon S3 and Amazon SageMaker. As some of McChrystal Group’s clients respond to the surveys in different languages, Amazon Translate is used to first translate all responses into English before processing.
The solution also employs Latent Dirichlet Allocation (LDA), a statistical model for topic discovery often applied to large document sets. The Rackspace Technology machine learning team optimized the LDA implementation to McChrystal Group’s specific needs, improving its open-ended question processing.
One of the biggest challenges that the team encountered while developing the solution was the varying sizes of the text responses. Responses to the same question might elicit a lengthy answer from one person and a shorter answer from another. The team had to make calculated decisions on how to select words or n-grams (multiple words or phrases) and the number of topics based on the number of text variations and lengths.
Another challenge was the significant variation in the sources and amounts of data from both large and small client accounts. The Rackspace Technology machine learning team used an iterative approach in getting feedback from the customer to ensure that the different modeling approaches being tested and deployed would achieve the best visualization result.
The effectiveness of visualizations also varies depending on the person viewing them. This is because a detailed, intricate visualization may be ideal for a trained specialist, but too complex for someone unfamiliar with big data. The Rackspace Technology team optimized the visualizations for McChrystal Group’s users and analysts based on the feedback.
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By automating data analysis on AWS using natural-language machine learning, results are now available in minutes versus days for analysts to process.
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.”
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