For years, business leaders have heard the refrain: “Data is the new oil,” yet many have struggled to extract value from their information reserves.
Today, advancements in artificial intelligence (AI), particularly large language models (LLMs), are changing the value proposition by potentially transforming mountains of unstructured data into actionable insights. Manufacturing giant Siemens is among those leading the charge by using data-driven technologies to improve production processes, predict maintenance needs, and develop new product lines.
However, AI also raises fresh challenges for corporate strategies regarding regulatory compliance and cross-border operations. Last year, Siemens signed an open letter urging European policymakers to rethink their approach to AI regulation, warning that EU laws create “disproportionate” compliance costs and liability risks for companies operating in Europe.
Here’s a look at how executives can navigate this new landscape to capitalize on emerging opportunities in the AI era.
High-quality corporate datasets have become increasingly valuable in the AI era. That’s because these datasets can power algorithms that can help predict future trends and optimize existing processes. As a result, smart executives are considering ways to audit their data assets and invest in robust analytics capabilities. In the automotive sector, Toyota is applying AI tools to enhance vehicle safety, optimize supply chains, and improve autonomous driving technologies.
A “circular data economy” may also emerge, where one industry’s data “waste” becomes another’s raw data material. Anonymized traffic patterns collected by ride-sharing companies, for example, could inform urban planners or retail businesses making location-based decisions. To capitalize on these opportunities, executives should consider measures to make their datasets available for internal experimentation and other use cases.
Data transfers within G7 countries are generally manageable, but hurdles remain. The EU-U.S. Data Privacy Framework, which governs personal data transfers between the EU and certified US companies, remains in its early stages. Data transfers involving countries like China are more complex.
Repurposing data, particularly personal information, can carry significant compliance risks. In March 2024, the US Federal Trade Commission (FTC) initiated an inquiry into Reddit’s plans to sell user-generated content for AI model training, focusing on compliance with regulations against unfair or deceptive practices.
Separate discussions about treating sensory car data as sensitive information are ongoing, and the use of geolocation data already faces strict regulations in many jurisdictions. For example, the FTC recently investigated Kochava and InMarket for allegedly selling location data without proper consent.
These efforts could significantly expand the pool of data available for analysis and innovation across industries
European regulators are actively shaping corporate data use through various existing frameworks and market-making initiatives. The European Union’s Data Act and Data Governance Act aim to create efficient markets for non-personal data and establish frameworks for data intermediation services. These efforts could significantly expand the pool of data available for analysis and innovation across industries. In Switzerland, the Federal Council recently launched an action plan to build a data ecosystem along similar lines.
The Chinese government is taking the data economy very seriously, having declared data a “factor of production” in 2020. The Chinese government recently established the National Data Administration to oversee the creation of a thriving data economy and launched initiatives like “Data Factors X” to accelerate its development.
China is also taking concrete steps to invigorate the national market for data, starting with public data initiatives. The Ministry of Transport is developing sensing networks to automatically collect and process shipping data, aiming to improve broad data sharing by 2027. The government has released a policy to enhance the sharing of metrology data, which includes calibration information for advanced manufacturing machinery.
These efforts are complemented by the creation of data exchanges in pilot provinces including Shanghai and Shenzhen, where standardized public datasets serve as initial trading products. To further advance these pilot regions, local regulators work to clarify property rights and establish specialized courts for data disputes.
New accounting standards, effective from January 2024, allow Chinese companies to recognize data as assets on their balance sheets. The principle is that what gets valued, gets traded.
Companies whose data supports a wide spectrum of use cases should consider developing a data monetization strategy
Executives must approach data management with the same rigor they apply to other critical resources. Here are some specific data management practices:
Companies whose data supports a wide spectrum of use cases should consider developing a data monetization strategy. This may involve enhancing existing products and services to capture valuable information or identifying new revenue streams through data products or partnerships. Regular evaluation of data assets and management practices is necessary because the value and relevance of data can change rapidly.
Recent government initiatives create opportunities for new data sources and business models but also require companies to adapt to new regulatory requirements and reassess their data asset management strategies. Finally, companies should foster data literacy by providing training and tools that enable all employees to work effectively with previously inaccessible information.
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