For over a decade, the phrase “data is the new oil” has echoed across boardrooms, tech summits, and digital transformation strategies. But in today’s AI-fuelled economy, this metaphor has matured from buzzword to bedrock reality. Data isn’t just powering algorithms, it’s become the essential currency of innovation, competitive advantage, and economic value. And much like oil during the Industrial Revolution, data now shapes entire business models, market dynamics, and even geopolitical tensions.
We need to understand a bit about the analogy of data and oil, its an important aspect to review before we get excited about new ‘innovations’ of data
A New Rush, A Familiar Risk?
When oil was first discovered and its industrial potential unlocked, it ignited a revolution. Entire economies were built around it which resulted in fuelling transportation, powering factories, and shaping the modern world. But over time, the hidden costs emerged: pollution, climate change, geopolitical tension, and a planet pushed to its limits. After centuries of extraction and dependence, we’re now scrambling to reverse the damage.
Data centres currently consume about 3% of the world’s electricity.
White & Case report on data center energy consumption
Fast forward to today, and a new kind of rush is underway, only this time, it’s for data. Hailed as the “new oil,” data is the lifeblood of artificial intelligence, personalized services, predictive analytics, and digital transformation. Companies, governments, and start-ups are in a frenzy to collect, store, mine, and monetize it. But just like oil, the very infrastructure that powers this data explosion, i.e. data centres, cloud networks, AI training models, they are now beginning to reveal its own environmental and ethical costs.
Are we repeating history in digital form? Are we fuelling short-term innovation at the expense of finite natural resources, soaring electricity demands, and rising carbon footprints? And as we race to build ever-larger data lakes and smarter algorithms, are we asking the right questions about where this ends and what it will cost us in the long run?
Let us explore these and other concepts as part of this article
The Rise of Data as a Critical Commodity
The valuation of the global data economy has been the subject of various studies and reports, each focusing on different aspects of the data landscape. While specific figures can vary depending on the scope and methodology of each study, several reputable sources provide insights into the significant growth and projected value of the data economy:
Big Data Market Projections: According to Grand View Research, the global big data market size is estimated to reach $862.31 billion by 2030, This growth is attributed to the increasing volume of data generated across organizations and the need for effective data management solutions. Grand View Research
Data Analytics Market Forecast: GlobalData forecasts that the global data analytics market will reach $190 billion by 2028,he exponential growth of data, driven by IoT, cloud computing, and AI advancements, is transforming business operations and decision-making processes. GlobalData
Earth Observation Data Value: The World Economic Forum reports that the global value of Earth observation (EO) data is predicted to swell from $266 billion today to over $700 billion by 2030, EO data plays a crucial role in climate and nature conservation efforts. World Economic Forum
EU Data Economy Valuation: According to Statista, the value of the data economy in the European Union and the United Kingdom was estimated to exceed €440 billion in 2020. This figure encompasses the generation, collection, storage, processing, distribution, analysis, elaboration, delivery, and exploitation of data enabled by digital technologies. Statista
These projections underscore the growing significance of data as a critical economic asset, influencing various sectors and driving digital transformation globally.
Additionally, the emergence of data marketplaces facilitates the buying and selling of anonymized datasets, treating data as a tradable commodity. These platforms enable organizations to monetize their data assets and access diverse datasets for various applications, further emphasizing the economic value of data in today’s digital landscape
Monetisation of Data
In today’s digital economy, data has emerged as a pivotal asset, driving innovation and revenue across various industries. Companies are increasingly recognizing the value of data, not just as a by-product of operations but as a core component of their business strategies. This shift has led to the development of new business models cantered around data monetization, where organizations leverage data to create additional revenue streams, enhance customer experiences, and improve operational efficiencies.
The monetization of data manifests differently across sectors, reflecting the unique opportunities and challenges within each industry. Below is a table highlighting how various industries are harnessing data for revenue generation:
| Industry | Data Monetization Strategy | Revenue Impact / Example | Source / Reference |
|---|---|---|---|
| Retail | Customer data used for targeted advertising via Retail Media Networks (RMNs). | Walmart’s advertising revenue grew by 30% YoY, outperforming core business units through Walmart Connect. | Financial Times |
| Automotive | Connected vehicle data for usage-based insurance, predictive maintenance, and subscription features. | Mercedes-Benz aims to earn $1.2 billion in digital service profit by 2025 via data-driven experiences. | Accenture Report |
| Aerospace | Predictive maintenance using real-time flight sensor data. | easyJet cut delays by 20%, saving millions annually by using data from aircraft sensors. | Medium Article |
| Utilities | Smart grid and smart meter data monetized via Utility Data-as-a-Service (UDaaS). | Utilities exploring UDaaS models to offer insights to third-party vendors, unlocking new value chains. | Indigo Advisory |
| Healthcare | Selling anonymized patient data for R&D and building AI diagnostic tools. | Healthcare data monetization solutions market projected to grow from $865M (2024) to $3.78B by 2033. | GlobeNewswire |
| Manufacturing | IoT sensor data for predictive maintenance, yield optimization, and operational insights. | Data-driven manufacturing increases productivity and opens secondary revenue from analytics services. | CloudSufi Blog |
| Consumer Packaged Goods (CPG) | Partnering with retailers to access consumer behavioral data for co-marketing and NPD (new product development). | CPG firms improve targeting and product innovation by participating in consumer data ecosystems with retailers. | EY Report |
These examples underscore a fundamental truth of modern business: data isn’t just infrastructure—it’s inventory. Whether it’s optimizing costs, creating new services, or increasing lifetime customer value, the monetization of data is now a universal strategic lever across industries.
Unlocking the value of data: Information to Income
Data has moved from being an operational by-product to a strategic asset arguably the most valuable one many companies hold.
In the age of digital transformation, businesses across sectors are discovering that their data holds untapped potential for generating entirely new revenue streams. Whether it’s through targeted advertising, smart product recommendations, or AI-powered services, data isn’t just fuelling decisions—it’s becoming the product.
Industries like retail, automotive, healthcare, and utilities are leading the charge, integrating data-driven business models that capitalize on real-time insights, consumer behavior, and machine-level telemetry. For many, monetizing data doesn’t just mean selling it—it means embedding it into new experiences, services, and ecosystems that deliver value to customers and partners alike. This shift isn’t theoretical; it’s already transforming balance sheets and boardroom strategies.
Business Models Emerging from the Data Economy
The surge in enterprise data has led to a wave of new business models, each tailored to how data is collected, processed, and delivered:
- Data-as-a-Service (DaaS): Organizations sell access to curated, structured datasets (e.g., Nielsen, Experian).
- Retail Media Networks: Retailers monetize shopper data by offering targeted ad inventory to brands.
- Freemium-to-Premium Models: Platforms like Google or Facebook offer free services, monetizing through ad targeting based on user data.
- Insights-as-a-Service: Companies analyze data on behalf of clients, delivering actionable insights rather than raw data.
- Synthetic Data & Data Augmentation: AI firms generate privacy-safe data for training models without exposing real user information.
- Data Marketplaces: Platforms like Snowflake and Dawex allow third-party data exchange and monetization.
- Subscription Analytics: Services like Bloomberg or CB Insights monetize through ongoing data-driven insights.
- Embedded AI Services: OEMs (e.g., automakers) bundle AI features like predictive maintenance or driving behavior scoring as premium services.
A Step-by-Step Methodology for Monetising data
Many organizations, especially those just beginning their digital transformation journey, struggle to understand how to unlock its true value. Data monetization isn’t reserved for tech giants; with the right mindset and approach, any company can turn its data into revenue, operational efficiency, or competitive advantage.
A few practical steps of how to turn data into a valuable asset has been outlined here, however we need to ensure we are aligning to the evolving regulatory and sustainability standards
Inventory & Classify Data Assets – Start with a data audit. Identify all types of data your company generates—from customer interactions and sales to IoT sensor data and operations logs.
Determine Value & Use Cases – Evaluate which datasets are unique, high-quality, or time-sensitive. Consider whether the data supports internal efficiencies, customer experience, or external partnerships.
Define Monetization Paths – Choose appropriate models: internal efficiency (cost savings), indirect monetization (better targeting), or direct monetization (data sales or services).
Invest in Infrastructure – Set up the right tech stack for storage, security, anonymization, and compliance (especially with GDPR, HIPAA, etc.).
Build Trust & Governance – Create policies for data governance, ethical use, and transparency. Data monetization fails without trust.
Pilot, Measure, Scale – Start small with a single product, service, or partner. Measure outcomes and iterate before scaling across business lines.
The next question that comes along is New data vs data saturation, organizations must decide, when is it time to optimize what we already have, and when should we invest in new data-generating capabilities? The future belongs to the businesses who can balance both.
Saturation vs. Net-New Data: Diverging Paths
As data-driven innovation matures, organizations face a critical duality in how they think about data: are we approaching a saturation point, or are we just scratching the surface with streams of net-new data? These two realities are seemingly at odds and are in fact shaping the way businesses approach decision-making, model development, and competitive differentiation.
The Saturation Point
Hypothesis suggests that we may be nearing a phase where most actionable human and system behaviours have already been captured. In such a scenario, the incremental value of collecting more data diminishes, and AI systems begin to rely heavily on recombining existing information rather than learning from truly novel patterns. This raises important strategic questions: Can decisions still improve if the underlying data is increasingly recycled? Will AI become a remix engine, reflecting past insights more than discovering new ones? Industries such as finance, e-commerce, and media are already observing diminishing returns in mature datasets where historical modelling dominates future prediction.
New New Data
This continues to emerge at an accelerating pace, especially from edge environments and emerging technologies. Wearables, IoT sensors, AR/VR systems, and smart cities are generating real-time, context-rich, and previously unmeasurable forms of data. This data is not just more, it’s differently structured, often hyper-local, biometric, or behavioural in ways that unlock fresh insights.
For example, in healthcare, wearable tech is enabling proactive intervention based on live data instead of static medical history. In manufacturing, edge computing offers on-the-fly decision-making using micro-data from machines to improving efficiency and reducing downtime.
Together, these forces are driving a more nuanced approach to data strategy. Organizations must decide: when is it time to optimize what we already have, and when should we invest in new data-generating capabilities? The future belongs to businesses that can balance both, harnessing the power of vast historical datasets while continuously integrating novel, real-time inputs that push decisions beyond what yesterday’s data could ever predict.
Data Saturation vs. Net-New Data Across Industries
Industries at the saturation point must innovate in how they interpret and reframe data, while those tapping into net-new sources must invest in infrastructure and governance to manage velocity, variety, and veracity.
| Industry | Saturation Point Data | Net-New Data Opportunities | Decision-Making Impact |
|---|---|---|---|
| Retail & E-commerce | Extensive customer purchase history, loyalty programs, browsing behavior already deeply mined. | Real-time in-store sensors, dynamic pricing reactions, geo-based mobile behavior. | Shift from reactive personalization to proactive micro-moment marketing. |
| Automotive | Telematics and vehicle diagnostics over years have been deeply analyzed for safety and maintenance. | EV charging data, autonomous driving edge cases, driver biometrics, traffic interaction patterns. | Improves predictive models with context-aware driving and infrastructure data. |
| Healthcare | EMRs, claims data, and clinical trials data are heavily modeled. | Continuous health tracking via wearables, genetic sequencing, real-time vitals from remote care. | Enables preventive care and real-time alerts, rather than post-incident diagnosis. |
| Manufacturing | Years of ERP, supply chain, and QA data already used for forecasting and lean processes. | Edge computing, digital twins, microsecond telemetry from machines and robotics. | Enables instant fault detection and adaptive manufacturing lines. |
| Utilities & Energy | Historical grid loads, billing, and outage logs used extensively in forecasting. | Smart meters, home IoT devices, distributed energy storage systems producing real-time microgrid data. | Supports dynamic grid balancing, decentralized energy markets, and prosumer engagement. |
| Aerospace | Maintenance and flight logs well-documented over decades. | Aircraft sensors, pilot biometric feedback, weather-adaptive systems, satellite IoT feeds. | Real-time safety responses, predictive rerouting, and adaptive in-flight systems. |
| Consumer Packaged Goods (CPG) | Decades of consumer panels, POS data, and seasonal trend analyses. | Smart packaging, direct-to-consumer feedback loops, sentiment data from social/voice assistants. | Enables hyper-personalized product R&D and instant brand sentiment adaptation. |
| Finance & Banking | Transactional data, credit history, and fraud models are mature. | Behavioral biometrics, decentralized finance (DeFi) activity, blockchain transaction metadata. | Enhances fraud detection and enables new underwriting models based on real-time behaviour. |
Data has become both the fuel and the foundation of the next digital age. But unlike oil, data is infinite, reusable, and democratic, at least in theory. The real challenge ahead isn’t just collecting more of it. It’s managing it responsibly, extracting meaningful insights, and doing so without mortgaging our planet’s future.
Balancing Act: The Cost of Data
The explosive growth of data is powering business models, AI capabilities, and industry transformation—but it comes with a hidden cost: sustainability. Behind every personalized recommendation, real-time insight, or predictive model is a vast infrastructure of servers, cooling systems, and compute-intensive algorithms that consume enormous amounts of energy. Data centres alone account for 2–3% of global electricity usage, and the carbon footprint of training large AI models rivals that of multiple passenger vehicles over a lifetime.
As we march toward a future where data is the new oil, the industry faces a paradox: the very fuel of digital transformation could also be a major contributor to the climate crisis. And it’s not just the tangible cost of energy—there are intangible costs too: increased latency due to data gravity, infrastructure sprawl, and rising data governance complexity.
Future of Data Capture and Sustainability?
Across industries, a handful of major players are both enabling the data capture boom and investing in the infrastructure to manage it sustainably:
| Company | Role in Data Economy | Sustainability Initiatives |
|---|---|---|
| Amazon Web Services (AWS) | World’s largest cloud platform powering data pipelines, storage, and AI for millions of businesses. | Committed to 100% renewable energy by 2025; building solar and wind farms globally. |
| Microsoft Azure | Powers enterprise-scale AI and IoT applications; key player in hybrid data strategies. | Underwater data center (Project Natick), carbon negative by 2030 goal. |
| Google Cloud | Advanced data analytics, AI/ML services, and real-time insights at scale. | 24/7 carbon-free energy goal by 2030, geothermal power initiatives. |
| Snowflake | Data sharing and warehousing across industries; key player in multi-cloud data ecosystems. | Promotes efficient data storage and compute elasticity to reduce waste. |
| NVIDIA | Hardware leader in powering AI training and edge compute. | Designing GPUs for efficiency and supporting edge AI that reduces centralized compute demand. |
| Cloudflare | Offers real-time data processing at the edge, reducing need for centralized data movement. | Runs on 100% renewable energy; focuses on lowering the carbon footprint of the internet. |
| IBM | Focused on federated learning, AI governance, and quantum computing for sustainable data models. | Leader in AI ethics and green IT; developing frameworks for carbon-efficient AI deployment. |
The next decade of data-driven transformation must be one of conscious innovation. Capturing, storing, and analysing massive amounts of data remains critical; but how we do it will define not just profit margins, but planetary outcomes. Businesses that balance data ambition with ecological responsibility will lead the next era of sustainable digital growth.
Conclusion: Sustainable Future Through Data
As the data economy evolves, one truth becomes increasingly clear: the power of data is immense, but so is our responsibility in how we use it. We’ve entered an era where data doesn’t just inform decisions. This will shape economies, drive innovation, and redefine what’s possible across industries. But with great volume comes great complexity: energy consumption, environmental strain, and ethical concerns.
To harness data’s full potential without compromising the planet, we must pivot toward sustainable data practices which will involve things like building green data centres and optimizing AI models, to adopting edge computing and prioritizing privacy-preserving architectures. When captured and used responsibly, data becomes more than a tool for business, it becomes a catalyst for solving humanity’s most pressing challenges, from climate change to healthcare access and urban efficiency.
In the end, the value of data is not just in how much we have, but in how wisely we use it for profit, for people, and for the planet.
Is there a risk that we’re generating too much data?
While more data generally leads to better models and insights, we are approaching a saturation point in some industries where new data offers diminishing returns. This makes it critical to focus on data quality, contextual relevance, and responsible storage to reduce environmental and operational costs.
How can companies start monetizing data sustainably?
Audit existing data assets. Identify high-value use cases (internal optimization, partner collaboration, direct monetization). Invest in efficient infrastructure like cloud elasticity, edge computing, and renewable-powered data centres. Adopt privacy-first and energy-conscious AI models.
This ensures that data monetization doesn’t come at the expense of long-term sustainability or trust.
