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Beyond “Just” Data Sharing: 4 Keys to Cross-Industry Innovation

 
As we move towards a future shaped by Web 3.0 and the metaverse, we can expect to see an increased focus on how firms share or own data.

 

From predictive analytics to performance optimization, the potential for innovation is enormous. But we still have a lot to accomplish before we can seamlessly share data across multiple industries.

 

Recently, a panel of experts from Microsoft, McKinstry, MovieLabs, and Emerson came together to discuss all things data—including where we are today and where we still need to go for true cross-industry collaboration. Four key insights emerged:

 

 

1. Cross-industry data-sharing challenges go beyond the application programming interface. There’s a need to tackle the human component, too.

 

With historically separate industries—from architecture, engineering, and construction (AEC) to design & manufacturing (D&M)—converging, we face challenges beyond the API, according to Susan Etlinger, director of artificial intelligence and innovation at Microsoft. The measures of success are different for a designer versus an engineer. We need to think about the handoffs and how to digitalize with a better understanding of each other’s domains.

 

“It’s almost like a human API in which we can convey success from one stage to another,” Etlinger said. “Technology and data are critical, but that human understanding of standards and how each individual sees the world are also really important.”

 

 

2. Collaboration means more than just a common data environment.

 

According to Dace Campbell, director of product management at McKinstry, the term “collaboration” is often incorrectly associated with simply sharing data in a common environment. True shared, task-based collaboration will go far beyond that.

 

“Our ability to work together is enabled—but also limited—by our ability to agree on how and where to store data,” Campbell said. “But that’s not enough. We need to be able to work together and make informed, collective, consensus-based decisions, and today’s tools and workflows don’t enable full collaboration across our industry.

 

"For example, when you take an extended road trip, you need to do more than agree on who will hold the keys or what road to take. You need to actively take your turn behind the wheel, adding individual value to the greater shared process, even if it’s not your car.”

 

 

3. Open-source sharing among media, entertainment, and AEC will spur new ways of working.

 

Mark Turner, program director of production technology at MovieLabs, shared that there is an incredible opportunity for multi-industry collaboration between AEC and media and entertainment (M&E). The challenge is figuring out how to handle complex data payloads, such as sharing a 3D model between two companies that may use different file formats or software types.

 

Once we come up with an open-source way to share that data, AEC firms could tap into millions of CG models originally created for feature films to help populate architectural designs. At the same time, AEC’s virtual environments could be leveraged to create more accurate visual effects scenes.

 

“There’s a whole bunch of wasted recreation of assets going on in our different silos because we can’t pass a sort of standard payload between different parts of a workflow,” Turner said.

 

 

4. Security and sharing data remain a challenge for industrial applications.

 

Brad Budde, vice president of digital at Emerson’s Automation Solutions business, noted that customers are drowning in data. New AI tools can help sort through the noise, which will promote faster decision-making. But there’s a catch.

 

“The tension is that often the data in these industrial applications is viewed to be confidential or restricted for national security purposes,” Budde said. “There’s this complex situation of whether we can share it or not, and then, if we can, how do we share.” 

 

Almost every industry is transitioning to a data-first strategy. Gartner estimates that firms that share data externally generate three times more measurable economic benefit than those who do not. Increasingly, high quality, well-federated data is becoming a valuable commodity, and successful organizations will find a way to maximize and monetize it.