{"id":235845,"date":"2026-06-23T10:29:00","date_gmt":"2026-06-23T14:29:00","guid":{"rendered":"https:\/\/testing.news-you-need.com\/index.php\/2026\/06\/23\/data-liquidity-leads-to-ai-success\/"},"modified":"2026-06-23T10:45:42","modified_gmt":"2026-06-23T14:45:42","slug":"data-liquidity-leads-to-ai-success","status":"publish","type":"post","link":"https:\/\/testing.news-you-need.com\/index.php\/2026\/06\/23\/data-liquidity-leads-to-ai-success\/","title":{"rendered":"Data liquidity leads to AI success"},"content":{"rendered":"

Data liquidity leads to AI success<\/a><\/p>\n

https:\/\/mitsloan.mit.edu\/ideas-made-to-matter\/data-liquidity-leads-to-ai-success<\/a><\/p>\n

Publish Date: 2026-06-23 10:29:00<\/a><\/p>\n

Source Domain: mitsloan.mit.edu<\/a><\/p>\n

Using an unordered list, summarize the following article with between 4 and 8 key points. <\/p>\n

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\u2019t 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 \u2014 it\u2019s how easily data can be reused, combined, and put to work across the enterprise. <\/p>\n

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 \u201cData Liquidity Levers at Caterpillar,\u201d principal research scientist Barbara Wixom and co-authors demonstrate that companies with high data liquidity outperform their peers on customer experience, speed to market, and data-driven decision-making.\u00a0But data liquidity doesn\u2019t 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.\u00a0Those levers \u2014 data architecture, data preparation, and data permissioning \u2014 offer a road map for leaders looking to turn data into sustained competitive advantage.Data liquidity and why it matters nowData 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.\u00a0In today\u2019s 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.<\/p>\n

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The 3 levers that unlock data liquidity1. Data liquidity Lever 1: Data architectureCaterpillar 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 \u2014 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 preparationCaterpillar prioritized reusable data \u2014 particularly customer, contact, and asset master data \u2014 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\u2019 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.\u00a0The 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<\/p>\n

Access is the final determinant of whether data liquidity delivers value. Caterpillar followed a \u201cleast privilege access\u201d 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.\u00a0Takeaways for leadersHigh 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.\u00a0Caterpillar\u2019s experience demonstrates that when companies treat data as a reusable asset \u2014 and not just a byproduct of operations \u2014 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.\u00a0She teaches the MIT Sloan Executive Education course\u00a0Data 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.\u00a0He 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.\u00a0Cynthia Beath is an academic research fellow at MIT CISR and professor emerita at the University of Texas at Austin.\u00a0Her research interests include organization redesign for the digital era, the management of data assets, and the organizational impacts of AI.<\/p>\n

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