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The Center for Business Analytics is pleased to recognize as an honoree of the 2023 Drexel LeBow Analytics 50 Awards. Read more about how used analytics to solve a business challenge.


Ben Litvinas, Senior Vice President, Data Science and Analytics



Real Estate, Internet

Business Challenge is one of the nation’s most popular home search destinations, with more than 100 million users visiting the site each month. With hundreds of thousands of property listings, it is also a data-driven organization, but the company grappled with operationalizing machine learning algorithms at scale. While the product team was focused on developing features that used complex algorithms, the company initially lacked a cultural mindset to leverage and utilize machine learning algorithms as building blocks to provide more personalized consumer experiences.

As part of a company-wide initiative to understand site users on a deeper level, the marketing team enlisted the data science team’s help. The marketing team created a list of 30 different predictive dimensions that they wanted to identify for each site visitor. To deliver on this request, the data science team would need to develop many algorithms. Typically, it would take several months to complete each model, making the total time to complete the project too long to be viable. This was the impetus to develop an acceleration framework, an internal project that leverages advanced data science and machine learning techniques, processes and tooling to create automated and scalable workflows for the data science team to access, aggregate and explore data. It also enabled the team to utilize that data to develop production-ready machine learning models exceptionally quickly.

Analytics Solution

The growth of the ecosystem of tools and shared best practices to build machine learning systems enabled this project to take shape. These advancements have helped make even small teams productive at scale. The output of the project was twofold — first, the team created a standardized access framework for data from multiple cloud platforms that manifests itself as a Python library that any data scientist can easily access. Second, the initiative created a standardized process, infrastructure and tooling for deploying machine learning algorithms in a directed acyclic graph such that they become automated, scalable and simple.


The biggest advantages of the migration to the acceleration framework are time savings and increased collaboration. The team has shaved off months from the time it takes to build a productionized machine learning model, and at the same time, improved the overall collaboration of the data science organization with other departments within the company — most notably impacting the speed of the business.

By developing a repeatable, automated deployment methodology and tooling, the team was able to coordinate data science activities into the same agile development sprints that the rest of the organization uses while delivering measured, stepwise improvements to machine learning models. These efforts led to a better consumer experience, a more positive and collaborative environment within the data science organization, and ultimately a more effective use of data in production. To date, the team has built 21 different productionized machine learning algorithms leveraging the acceleration framework.