The rapid rise of artificial intelligence has put data back at the center of corporate strategy. Many organizations, however, are discovering that deploying advanced analytics or AI tools doesn’t automatically translate into better decisions or business results. What separates leaders from laggards is not how much data they collect or how sophisticated their models are — it’s how easily data can be reused, combined, and put to work across the enterprise.
Researchers at the MIT Center for Information Systems Research call this capability data liquidity: the ease of data asset reuse and recombination. In the new research briefing “Data Liquidity Levers at Caterpillar,” principal research scientist and co-authors demonstrate that companies with high data liquidity outperform their peers on customer experience, speed to market, and data-driven decision-making.
But data liquidity doesn’t happen automatically. Without intentional choices about how data is architected, prepared, and governed, organizations struggle to reuse data at scale. This limits the value they capture from digital and AI investments, the researchers write.
To understand how organizations can unlock data liquidity at scale, Wixom and her co-researchers Joaquin Rodriguez, Gabriele Piccoli, and Cynthia Beath examined a multiyear data transformation at global heavy equipment manufacturer Caterpillar. As part of a broader strategy to grow its services business, Caterpillar focused on three practical levers that determine whether data becomes a reusable strategic asset or stays trapped in silos.
Those levers — data architecture, data preparation, and data permissioning — offer a road map for leaders looking to turn data into sustained competitive advantage.
Data liquidity and why it matters now
Data liquidity refers to how easily data assets can be reused and combined across use cases and organizational boundaries. In a highly liquid data environment, data flows freely enough that employees, systems, and AI models can draw on it without undue friction, delay, or duplication.
In today’s digital economy, data liquidity is a critical enabler of AI-driven business models, connected customer experiences, and adaptive operations, the researchers write. Organizations with high data liquidity harness more reuse, reduce wasteful data duplication, and make insights more broadly available.
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The 3 levers that unlock data liquidity
1. Data liquidity Lever 1: Data architecture
Caterpillar faced a complex and fragmented data environment, with siloed applications, hundreds of dealer interfaces, and equipment that generated millions of telematics messages with varying levels of detail. To support diverse use cases, Caterpillar designed a modular platform with a thin application layer, a service layer, and a data layer built for reuse.
Data flowed through stages — from raw ingestion to validation and normalization, then into stable master datasets or combined derived data sets with clear ownership. This architecture enabled Caterpillar to create reusable data products, such as a fleet list dataset that reduced duplication, sped development, and improved the consistency of customer experience.
2. Data liquidity Lever 2: Data preparation
Caterpillar prioritized reusable data — particularly customer, contact, and asset master data — that directly supported its service revenue strategy. Customer data captured who owned equipment; contact data identified company contacts with whom Caterpillar needed to engage; and asset data represented customers’ full equipment fleets. Combined, these datasets enabled the company to answer critical business questions, such as which customer contact was responsible for replacing specific machines.
The company created a dedicated data quality group to ensure that its data assets were reliable. That team defined four quality levels and validated data using algorithmic, statistical, and machine learning techniques embedded as reusable services. Data quality was continuously monitored, with problematic records flagged so that data stewards could resolve them.
3. Data liquidity Lever 3: Permissioning
Access is the final determinant of whether data liquidity delivers value. Caterpillar followed a “least privilege access” tenet, giving employees the least amount of access they needed to accomplish a goal. The team also identified sensitive or confidential data and ensured that it was accessible only to employees assigned certain roles. An access request portal helped people understand the datasets, entitlements, and objects available to their roles, the researchers write.
Takeaways for leaders
High data liquidity is a managerial challenge that requires coordinated choices across technology, process, and governance. By intentionally shaping data architecture, investing in strategic preparation, and enabling safe access, organizations can increase data reuse and accelerate the value they derive from digital and analytical initiatives.
Caterpillar’s experience demonstrates that when companies treat data as a reusable asset — and not just a byproduct of operations — they are better positioned to scale innovation and capture sustained business value.
Barbara Wixom is a principal research scientist at the MIT Center for Information Systems Research. Since 1994, her research has explored how organizations generate business value from data assets. Her methods include large-scale surveys, meta-analyses, lab experiments, and in-depth case studies. She teaches the MIT Sloan Executive Education course Data Monetization Strategy: Creating Value Through Data.
Joaquin Rodriguez is an academic research fellow at MIT CISR and an assistant professor of information systems at Grenoble Ecole de Management. He researches digital strategic initiatives, digital transformation, and competition within platform ecosystems.
Gabriele Piccoli is an academic research fellow at MIT CISR and a professor of information sciences at Louisiana State University. His research interests are digital strategy, digital customer service systems, and digital customer relationships.
Cynthia Beath is an academic research fellow at MIT CISR and professor emerita at the University of Texas at Austin. Her research interests include organization redesign for the digital era, the management of data assets, and the organizational impacts of AI.
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