Exploring the Future of Circularity Through Data and AI

Last week, I enjoyed attending an AVT Business School Masterclass with Professor Venkat Venkatraman from Boston University’s Questrom School of Business. His discussion, centered on his and Vijay Govindarajan (VG) ’s new book Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future, emphasized the importance of integrating traditional industrial strengths with digital capabilities like AI and data analysis to unlock new strategic opportunities.

Venkatraman’s key message was that industrial companies must fuse their expertise in creating physical products with the strengths of digital companies—using algorithms and AI to unlock insights from vast interconnected datasets. This combination allows manufacturers to identify strategic connections and opportunities that remain hidden. In his words, “Datagraphs are the next great digital advantage”—a core observation that builds on the notion that real-time data and AI can power future strategies.

Building Circular Data Insights

Venkatraman's observations align strongly with my arguments on the circular economy. In previous Circular Bytes articles and my coming book, I mentioned that manufacturers must avoid the traditional "sell and forget" mentality. In the circular economy, success will depend on following the product through its entire lifecycle, collecting data on how users interact with it, and understanding how it is repaired, redistributed, shared, recycled, or refurbished. This continuous monitoring offers unique opportunities to learn from product usage across its multiple life cycles, providing manufacturers with valuable insights that can fuel innovation.

Unlocking Value Through Datagraphs

One of Venkatraman's most compelling concepts is Datagraphs. Much like how social graphs allow companies like Facebook to map user relationships, datagraphs enable manufacturers to track and analyze product interactions across their ecosystems.

Facebook’s social graph, for example, helps it understand how users connect, communicate, and share content, allowing it to optimize its engagement, advertising, and community-building platform. Similarly, Spotify’s Music Graph tracks listening patterns, preferences, and social interactions, providing personalized recommendations and creating a deeply engaging music discovery experience.

Airbnb’s Travel Graph offers insights into user preferences and travel behavior, enabling the platform to recommend accommodations and experiences tailored to each user’s history and context. Likewise, Netflix’s Movie Graph analyzes viewers' watch history, preferences, and viewing patterns, allowing it to suggest highly relevant content that keeps users engaged for longer periods.

In manufacturing, datagraphs are equally valuable, allowing businesses to map product usage across the ecosystem—from production and distribution to use, repair, redistribution, and recycling. These datagraphs are essential for manufacturers to fully grasp how products are used in real-world environments, generate actionable insights that optimize the product lifecycle, and predict future maintenance needs. Manufacturers can improve product design, extend lifecycles, and enhance customer experiences while achieving sustainability goals by understanding product interactions as comprehensively as Facebook understands its users or Spotify understands musical tastes.

Manufacturers can extend this idea further by reaching out across their business ecosystems. Companies can build interconnected datagraphs that cover the entire value chain, tracking resource flows, product repairs, recycling, and more by working closely with suppliers, customers, and other key players. These networks enable manufacturers to gain comprehensive insights into product performance, resource utilization, and customer preferences.

Shifting to Datagraphs – A Strategic Move

Venkat Venkatraman and Vijay Govindarajan’s HBR article "The Next Great Digital Advantage," reinforces this point by explaining how traditional companies must rethink their data strategies. Manufacturers must move away from storing customer data in isolated silos that hinder the ability to see the broader picture. Instead, they should organize data as a graph of interactions, which algorithms can analyze to generate insights and deliver personalized value. This shift allows companies to map and analyze how customers interact with their products throughout the lifecycle.

Venkatraman poses a key question: “Does knowledge about our products exist mostly as separate data sets, or are we developing machine-readable graphs to identify patterns of preference for our customers?” This distinction is crucial because datagraphs enable businesses to transform scattered data points into a cohesive understanding of customer needs, preferences, and product usage. These insights are even more critical in the circular economy, where products have multiple life cycles.

To extend this thinking, I would pose an additional question for manufacturers: A digitized circular economy enables new revenue opportunities, reduced costs, improved sustainability, and greater resilience. Do we have the knowledge and interactions necessary to understand the lifecycles our products will follow in a circular economy, and do we know how to use that information in our datagraphs to gain a competitive advantage?

This question highlights the importance of tracking products throughout their entire lifecycle, from manufacturing and initial use to repair, recycling, and reuse. Tracking customer behavior during the sale and warranty period is no longer sufficient. In a circular economy, companies must follow products through multiple phases of reuse, refurbishment, and recycling, leveraging these interactions to build ever-richer datagraphs.

By organizing and analyzing these interactions across all stages, manufacturers can:

• Identify patterns of product wear, repair needs, and user preferences.
• Develop personalized offers and services that enhance customer loyalty.
• Based on real-world usage data, improve product design for longevity, modularity, and easier recycling.
• Create more resilient supply chains by understanding material flows and optimizing resource recovery.

This shift to a datagraph-driven strategy gives manufacturers a holistic view of their products' entire lifecycle within the circular economy, empowering them to continuously improve product performance and customer experience while driving down costs and improving sustainability.

Capitalizing on Data Network Effects in the Circular Economy

To remain competitive, manufacturers must collect relevant product-in-use data in real-time. This data informs immediate product improvements and allows companies to build data network effects—the more a product is used, the richer the data becomes, enabling better decisions across design, manufacturing, and circular lifecycle processes. This is especially critical in the circular economy, where collaborating with ecosystem partners to track and analyze product lifecycle data is essential to driving sustainable growth.

Table 1 is adapted from the HBR article "The Next Great Digital Advantage," which outlines the shift from traditional advantages to datagraph-driven advantages in the digital age. I have extended this framework to highlight how digitized circular business ecosystems build on these advantages, emphasizing the critical role of lifecycle data, ecosystem collaboration, and circular innovation in driving competitive advantage in an interconnected market.

By blending physical product expertise with the power of AI-driven data insights, manufacturers can unlock new opportunities in the circular economy. Datagraphs offer a unique advantage by enabling companies to understand product usage, optimize processes, and continually improve customer experiences. As we move further into this fusion of industrial and digital worlds, manufacturers that embrace data-driven circularity will remain competitive and lead the future of sustainable manufacturing.

Watch video - https://youtu.be/VH7ltCNOS50