A Data-Driven Meeting of the Minds

Industry and academia learn from each other at 5th annual Data Science Symposium.

Many marketers — now elbow-deep in data and analysis — had zero exposure to the discipline of data science a decade ago. Now, big data is big business, and, for many business leaders, a central piece of their day to day.

This shift has required marketers and other data-driven business leaders to take a step back and understand their new roles and the technology and trends associated. To support this learning and development, Adobe hosts its annual Data Science Symposium every fall, coinciding with back-to-school season. Here, industry and academic leaders unpack the latest needs, considerations, and cutting-edge research in data science. By looking at real-world applications as well as what’s emerging, participants can hone their skills and prepare for an even more collaborative, more data-driven future. This year’s Adobe Data Science Symposium, taking place on September 13, will feature presentations from professors from leading universities across the country that are working with Adobe, including MIT, University of California, Irvine, the University of Washington, and more.

A meeting of the minds

At the Adobe Data Science Symposium, it’s not uncommon to see academics armed with leading-edge research and a future-first view alongside Adobe innovators sharing a deep understanding of rich data sets generated from a customer experience perspective.

“We have rich data assets we’re connecting and managing on behalf of our customers,” says Anil Kamath, an Adobe fellow and vice president of technology. “This opens up areas of research, which the academic community was either not aware of or wouldn’t have access to ordinarily.”

Both the assets and access provide a clear benefit for both groups. It’s collaborative and connected, and an optimal basis for future research and innovation on both sides.

“If researchers are looking for marketing and advertising expertise and data assets in that context,” he adds, “it makes sense for them to work with us because of the sheer volume of real-world data we can bring to the table.” Adobe Experience Cloud manages more than 233 trillion data transactions annually, providing academia with a unique opportunity to tap into this data to create consistent, continuous, and compelling customer experiences — and this seismic growth isn’t slowing down anytime soon.

What to expect this year

As in previous years, the 2018 symposium will delve into new data models, advanced algorithms, real-world applications, and emerging trends in data science. There will be a greater focus on machine learning as well as artificial intelligence (AI).

As machine learning and AI become increasingly mainstream, people continue to have questions and concerns ranging from privacy, to use cases, to ethical implications, according to Anil. The Data Science Symposium will strive to address and mitigate them. “Marketers and business leaders are concerned about machine-learning systems being black boxes with uncharted biases,” Anil explains, “and everyone is looking for greater transparency.”

Katharina Reinecke, an assistant professor of computer science and engineering at the University of Washington, will address another hot-button topic: the need for more quantitative, data-driven design.

“There definitely should be room for intuition, experience, and inspiration,” she explains, “but I do think there’s an increasing demand for objective design guidelines,” which she’ll underscore in her session, “Designing for People Around the World.”

Professor Reinecke is also looking forward to presenting her latest research on interfaces that are built on data models about users’ visual preferences. “Since users differ so widely,” she says, “we shouldn’t rely on a one-size-fits-all design approach anymore.”

A case for continued collaboration

In connecting two distinct sides of the data science conversation, the Adobe Data Science Symposium helps both industry and academic stakeholders advance their thinking and their own work.

Georgia Perakis, professor of operations management and operations research and statistics at MIT, is both an Adobe grant recipient and presenter at the Adobe Data Science Symposium. This year, Professor Perakis will present on personalization in pricing using optimization and machine learning. She’ll share an improved personalized demand estimation model that incorporates the role of influence and word-of-mouth among customers.

By focusing on important industry trends and topics — data-driven design, personalization, visual preferences, and other key themes — the symposium ensures real-time relevance for both academic and industry attendees.

Ultimately, though, the Adobe Data Science Symposium is about bringing together a diverse audience with a common goal: to collaborate and ideate through passionate discourse, presentations, and hands-on experiences, with the goal of advancing the field of data science.

“The Adobe Data Science Symposium is an exciting opportunity to get to know industry insiders whose primary goal is to develop products that support real needs,” Professor Reinecke says. “As academics, we often tend to focus on a lot of problems and needs that we think are important, but others in the industry may feel otherwise.”

Professor Perakis agrees. “Exposure to industry insiders is very valuable as it gives us academics exposure to important applications in data science,” she says. “This keeps us working on what’s relevant in practice-driven problems.”

This exposure and exchange of ideas is something Anil has seen year after year at the event. “On the academic side, they may be certain areas of marketing and advertising, but not others,” Anil says. “So just seeing it through our market-driven perspective is helpful in advancing their work.”

Industry participants also have a lot to gain. “Our tech and engineering teams get access to better techniques than they’d normally know are available,” he says. “It’s a cross-domain exchange of knowledge that brings out better quality methods, builds better products, and drives better performance.” Very simply, “We are better because of what we learn here.”

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