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Autodesk Scales Product Insights by 300% with MongoDB Atlas

Photo of a man working on a laptop.

INDUSTRY

Computer Software

PRODUCT

MongoDB Atlas

USE CASE

Analytics
Personalization

CUSTOMER SINCE

2011
Ashish Arora, Head of Engineering and Machine Learning for Product Analytics at Autodesk, discusses how MongoDB Atlas powers their Product Insights platform.
THEIR CHALLENGE

Capturing Autodesk usage data using MongoDB

Transforming data into actionable insights to enhance the user experience requires a comprehensive database solution. Autodesk, a software company that helps customers design and make a better world for all using CAD and 3D design programs, chose MongoDB to be the cornerstone of its user-centric approach to data collection and application.

Autodesk’s software is considered an industry leader in many sectors, including engineering and construction, product design, and media and entertainment. Because the company is always working to improve its offerings, it launched the Product Insights platform in 2020. Product Insights is an internal platform that gathers and analyzes usage data from Autodesk products, and this data can be used to deliver insights that increase ease of use and user efficiency.

Originally, Autodesk started the Product Insights program to uncover ways to improve its products, but it soon transformed the platform into a first-of-its-kind feature accelerant for Autodesk’s end users, enabling personalization at scale. The platform captures usage data and offers users recommendations through emails, web portals, and in-software pop-ups. In turn, these personalized insights enable customers to use Autodesk’s software more proficiently. For example, the platform might analyze a repetitive workflow and then recommend saving it as a macro to perform the task with a single click. “Learning any new software takes time,” said Ashish Arora, Head of Engineering and Machine Learning for Product Analytics at Autodesk. “Our offering helps our customers improve and expedite that learning process.”

As insights within the platform grew, it became clear that Autodesk’s original cloud database solution, which used a combination of an object store and a meta store, was reaching its capacity. To meet rising demand, Autodesk recognized the need for a new solution. The company also wanted to increase the flexibility of its solution so that it could better adapt to the unpredictable nature of user behavior.

To achieve these goals, Autodesk followed a simple process to migrate its databases to MongoDB Atlas, a developer data platform with an integrated suite of data services designed to accelerate and simplify building with data.

“MongoDB offers immensely powerful technology. It can be used internally to answer critical questions that we have about our products, and it can power the services and technologies that we serve back to our customers.”

Ashish Arora, Head of Engineering and Machine Learning for Product Analytics, Autodesk

OUR SOLUTION

Scaling product insights by 300% in one year

MongoDB Atlas integrates with Autodesk’s infrastructure, giving development teams the ability to store the usage analytics in a single document. This makes it possible to retrieve the data and provide insights for the various software products that Autodesk builds using the platform. “We partnered closely with MongoDB so that we could power multiple different use cases and services,” said Arora. “Whenever we had support questions or development struggles, we could always reach out to them. They have been solving problems with us hand in hand.”

After migrating to MongoDB Atlas, Autodesk overcame its scaling and operational challenges, enabling the company to deliver several million insights weekly. The solution continues to grow rapidly, and by the end of 2024, the company expects to support even more users with no increase in overhead costs.

Autodesk also gained the flexibility to respond to changing data types and variable frequencies. Product Insights’ data model has evolved over time, and MongoDB has adapted to each new schema requirement, even when Autodesk performed complete overhauls. “MongoDB Atlas itself has grown so much since we started our journey with MongoDB,” said Arora. “It’s constantly innovating, which has empowered us to do the same with our services.”

Autodesk also saw operational benefits from its use of MongoDB Atlas. “Using MongoDB Atlas saves us a lot of time,” said Arora. “Taking away that operational work means our developers can focus on innovation.” Previously, Autodesk had to build custom solutions for crucial features such as scalability, fault tolerance, and resiliency. MongoDB Atlas provides managed services that reduce the team’s overall workload and can accelerate onboarding and training. With a simple user interface, developers can start using MongoDB Atlas without delay.

Autodesk improved performance through its use of MongoDB, as well. Autodesk increased the accuracy of its API calls while simultaneously achieving subsecond latencies, a critical SLA for the company, because MongoDB integrated with GraphQL.

OUTCOME

Innovating across Autodesk’s software

Product Insights, powered by MongoDB Atlas, is now fully embedded in several Autodesk software products in the engineering and construction sector, which helps users quickly and efficiently build better workflows. Autodesk plans to harness the scalability of MongoDB solutions to expand Product Insights across more products.

As Autodesk continues to innovate with usage insights and personalized recommendations, it is investing heavily in generative AI (gen AI). In addition, the company plans to make use of more capabilities within MongoDB, including Atlas Vector Search and Atlas Stream Processing solutions.

“MongoDB is more than just a database; it exists in the middle of everything we do. As we have adapted to the changing needs of our services and the underlying database that we were supporting, MongoDB has been with us every step of the journey.”

Ashish Arora, Head of Engineering and Machine Learning for Product Analytics, Autodesk

To learn more, visit MongoDB Atlas.

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