UPDATED 12:30 EDT / JULY 23 2018

BIG DATA

MIT research scientist reveals four-step plan for data transformation

The difference between traditional business and digital business lies in the degree to which data is recognized and employed as an asset, which requires not only a financial investment, but a cultural shift involving full organizational buy-in.

For businesses born in the digital age, these processes come naturally. But for the legacy organizations working to compete with digital natives, this data disruption requires the challenge of both developing and following a modernization framework in real time.

“Data is a foundational capability of a digital organization, a core resource of a digital firm,” said Barbara Wixom (pictured), principal research scientist at the MIT Sloan Center for Information Systems Research. “As we all become digital … a lot of traditional companies will require data-driven transformation in order to get there, unlike digital companies that are naturally leveraging data as a strategic asset.”

Wixom recently spoke with Peter Burris (@plburris) and Rebecca Knight (@knightrm), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the MIT CDOIQ Symposium in Cambridge, Massachusetts.

With an extensive background and nearly 25 years of experience in big data and analytics, Wixom is paving the way for companies at every level of modernization to better leverage and develop new uses for data through her work with the MIT Sloan Center for Information Systems Research, or CISR.

This week, theCUBE spotlights Barbara Wixom in our Women in Tech feature.

The case for transformation

Established in 1974 as a non-profit out of the MIT Sloan School of Management, CISR is the oldest academic research center for tech in the world and operates with a mission of helping organizations succeed in digital. Wixom focuses specifically on data and the development of tools and processes to facilitate business leaders in their transformation journeys.

It’s through this work that she’s received recognition for a case study on the multinational Spanish banking group Banco Bilbao Vizcaya Argentaria, or BBVA. With Wixom’s guidance, the bank established a center for data excellence in record time, improving the business’ bottom line and overall organizational health.

“They got a sense that they could commercialize some of their data services to create new revenue streams [and sent] some of their innovators over to the MIT Media Lab,” Wixom explained. “They said, ‘Let’s establish a legally separate subsidiary with a focus on data monetizing by selling new data products.’”

After establishing a separate entity and working with data scientists, BBVA realized the greater potential for value in enabling the bank to appreciate data as an asset as opposed to selling data. The company shifted focus to internal monetization, pointing efforts at a data-centric organizational reconstruction.

A new data framework

Through her work with BBVA, Wixom honed a data monetization framework built around four fundamental steps. The guideline provides a high-level look at how to drive value and can be customized to the needs of any individual organization, according to Wixom.

“It’s a conceptual model that basically says you can monetize your data by using [it] to improve your company processes, create lift, process lift efficiencies. You can wrap data analytics around your existing offerings, your products, in order to add value to those,” she said.

The first step is to develop a target state for the data. “BBVA did this by creating the subsidiary data science group,” Wixom said, “In effect, they were creating in an incubated way where they eventually wanted to head.”

The second step is to use that target to promote and communicate the value of data across the organization. Wixom advises measuring the value of all data projects and providing team trainings at each level to facilitate full comprehension at scale. “They had training for all 130,000 employees at BBVA,” she said, “For people who just needed basic literacy regarding data, they held an event that was livestreamed in order to capture about 18,000 people at once.”

Wixom’s third step is innovating new ways to work. “You have to figure out, ‘What are we going to do now that’s different?’” she explained.

BBVA found its differentiator through investment in social good projects, working with the Government of Mexico and the United Nations to develop an exploration of bank card and payment data to understand disaster response. When Hurricane Odile hit Mexico, BBVA could use bank transactions to understand what the economic recovery was like and redirect efforts to better assist in disaster response.

“That became one of their best channels for attracting new data scientists through those social good projects, as well as interesting new ecosystem partners like start-ups who might not have [otherwise] worked with BBVA,” Wixom said.

The fourth and final step is identifying and sharing best practices discovered throughout the process. “[At BBVA] every product that the data scientists worked on, they took the new algorithms, the new models, and they started creating a catalog of analytics tools that they then could start reusing across other projects. That became a really strong enterprise capability that everyone could benefit from,” Wixom said.

Within these four steps, Wixom identified data wrapping as a key piece to a successful enterprise monetization strategy. Data wrapping is the process of selling a product as a service through data by creating digital features and experiences that are fueled by analytics, which holds huge potential for an enterprise moving increasingly into the virtual space, according to Wixom. This can include an app that is offered in addition to an existing product or reporting and insights shared to help a business partner sell product more effectively.

“A customer is going to want you because of the analytics you’re associating with the offering to help you consume it better, sell more of it. Wrapping is honestly, I believe, the future,” she said.

Using these contemporary optimization methods, BBVA realized a $35-million lift in just a year. Wixom attributes the organization’s rapid success to the concurrent practice of the transformation steps, as opposed to sequential.

“What was really striking about BBVA is that in three years, they set the bank up to make significant inroads in monetizing [with] 130,000 employees,” she said.

Leading enterprise through modernization

In addition to her work with CISR, Wixom explores analytics and the optimization of data practices as associate editor of the “Business Intelligence Journal” and as research fellow and best practice judge for The Data Warehousing Institute and its annual BI Best Practices Awards. She’s received recognition for four other case studies prior to her work with BBVA and has authored two leading systems analysis and design textbooks.

Throughout her 25-year career, Wixom has observed firsthand the pitfalls of data transformation at a variety of organizations and understands the challenges modernization poses for legacy businesses. “It requires a big shift. It changes the relationships across the organization, with the data team. A lot of dynamics really get shifted,” she said.

As data’s rise scales exponentially and the challenges of transformation become increasingly inevitable, Wixom hopes her guidance can help these organizations succeed in developing and reaching new goals.

“At the end of the day, organizations trying to distinguish products in the marketplace are going to need to rely on analytics to help us do this,” she concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the MIT CDOIQ Symposium.

Photo: SiliconANGLE

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