Why Data Has Become One of the World's Most Valuable Resources

Discover why data has become strategically important to businesses, governments and technology companies and how it is shaping the global economy.

Data has become one of the most strategically important assets in the modern economy.

Every online purchase, search query, payment, social interaction, connected device and business transaction can generate information.

Companies use that information to understand customers, improve products, forecast demand and automate decisions. Governments use data to plan infrastructure and deliver public services. Artificial intelligence systems depend heavily on large quantities of data for training, evaluation and operation.

But describing data simply as "the new oil" can be misleading.

The OECD points out that data can function as a factor of production, an intangible asset, an intermediate input and a form of capital. Unlike oil, however, data is not consumed when it is used, can be copied and can be reused or shared in different contexts.

That difference is central to understanding why data matters.

What Makes Data Valuable?

Raw data is not automatically valuable.

A spreadsheet full of disconnected numbers may have little practical use.

The value emerges when data can be:

Collected

Organized

Analyzed

Connected

Interpreted

Applied to decisions

For example, a retailer may collect millions of transactions. The raw records become much more useful when the company can identify which products are frequently purchased together, when demand changes and which customers are likely to return.

The journey is often:

Data → Information → Insight → Decision

Businesses Became Data-Driven

Companies have always collected information.

What changed is the scale and speed.

Modern digital businesses can collect information from:

Websites

Mobile applications

E-commerce

Customer-service systems

Advertising

Connected devices

Payments

Search behavior

Social platforms

This allows companies to make decisions based increasingly on observed behavior rather than intuition alone.

Data Powers Personalization

One of the most visible uses of data is personalization.

Streaming platforms recommend music and movies.

E-commerce websites suggest products.

News platforms recommend stories.

Advertising systems identify likely audiences.

Travel websites customize recommendations.

Personalization can make digital services more useful because users encounter information that is more relevant to their interests.

However, personalization also creates questions about privacy, transparency and the amount of information companies should collect.

Data Is Fueling Artificial Intelligence

Artificial intelligence has made data even more strategically important.

Modern AI systems need data for:

Training

Evaluation

Testing

Fine-tuning

Retrieval

Personalization

Real-world feedback

The quality of the data can influence the quality and reliability of the resulting system.

This is one reason companies with access to large, high-quality proprietary datasets can have an advantage in particular AI applications.

Not All Data Is Equal

More data is not always better.

A dataset can be:

Incomplete

Duplicated

Outdated

Biased

Incorrect

Poorly labeled

Collected without appropriate consent

High-quality data can be more valuable than enormous quantities of low-quality information.

Businesses increasingly need data-governance systems to determine what information should be collected, where it comes from, how long it should be retained and who can access it.

First-Party Data Became More Important

Companies increasingly value information they collect directly from their own customers and users.

This is often called first-party data.

Examples include:

Customer purchases

Website interactions

Newsletter subscriptions

Product preferences

Customer feedback

Support conversations

Account activity

This information can help businesses understand their audiences while reducing dependence on data purchased from outside sources.

Data Became a Competitive Advantage

Two companies may use similar software but have very different levels of business intelligence because one has better data.

A retailer with detailed purchasing information can forecast demand more effectively.

A streaming platform with extensive listening behavior can improve recommendations.

A financial institution with high-quality transaction data can improve fraud detection.

A logistics company can use historical transport information to optimize routes.

The advantage is not simply possessing data.

It is knowing how to turn it into useful decisions.

Cloud Computing Made Data Easier to Store

The growth of cloud computing made large-scale data storage and processing more accessible.

Instead of maintaining all computing infrastructure internally, organizations can use cloud services to:

Store information

Process large datasets

Run analytics

Train models

Build applications

Create backups

This reduced some of the technical barriers associated with handling large quantities of information.

The World Bank notes that data generated through payments, digital services and AI is increasingly stored and processed through data centers and cloud infrastructure around the world.

Data Centers Became Strategic Infrastructure

The importance of data has also increased the importance of the physical infrastructure supporting it.

Data centers require:

Electricity

Cooling

Networks

Physical security

Land

Specialized equipment

As AI and cloud services grow, demand for data-center capacity is increasing.

That means data is not actually "weightless."

Although digital information appears abstract, storing and processing it requires physical infrastructure.

Governments Are Becoming More Concerned About Data

Data has economic and strategic implications.

Governments regulate how certain information can be collected, stored, transferred and used.

Important categories can include:

Personal data

Financial information

Health information

Government records

Business information

Critical infrastructure data

Cross-border data transfers have become particularly important because digital businesses routinely move information between countries.

Privacy Became a Major Issue

The more data organizations collect, the greater the potential impact of misuse.

Privacy questions include:

What information is collected?

Why is it collected?

How is it used?

Who can access it?

How long is it retained?

Can it be deleted?

Is it shared with third parties?

These questions have become central to technology regulation.

The OECD emphasizes that data governance involves balancing economic benefits with privacy, security and other social considerations.

Data Breaches Can Be Extremely Expensive

Data is valuable to legitimate organizations, but it is also valuable to criminals.

Cyberattacks can target:

Customer databases

Payment information

Passwords

Business records

Intellectual property

Employee information

This makes cybersecurity part of data management.

Protecting data requires more than storing it in a database.

Organizations need:

Access controls

Authentication

Encryption

Monitoring

Backups

Incident-response plans

Employee training

Data Can Become a Liability

Data collection has costs.

Organizations must pay for:

Storage

Security

Compliance

Management

Integration

Quality control

A huge database that nobody understands can become expensive rather than valuable.

The goal should therefore be purposeful data collection.

Data and Advertising

Digital advertising became one of the largest commercial applications of data.

Advertisers use information to understand:

Audience interests

Content engagement

Conversion behavior

Purchase intent

Geographic patterns

Campaign performance

This can help businesses show ads to more relevant audiences.

But advertising data practices have also contributed to wider debates about tracking, privacy and consumer control.

Data Helps Businesses Forecast

Historical data can help organizations identify patterns.

A business may use previous information to estimate:

Future sales

Inventory demand

Staffing needs

Customer churn

Seasonal changes

Financial requirements

Forecasts are not guarantees.

Unexpected events can make historical patterns less reliable.

Still, better information can improve decision-making.

Data Is Changing Healthcare

Healthcare systems increasingly rely on data for:

Medical records

Population health

Diagnostics

Research

Hospital management

Drug development

AI-assisted analysis

However, health data is particularly sensitive.

The potential benefits of better analysis must be balanced against privacy, security, consent and ethical concerns.

Data Is Changing Finance

Financial institutions depend on information to evaluate:

Transactions

Risk

Fraud

Credit

Markets

Customer behavior

Regulatory compliance

Real-time data can help detect suspicious transactions quickly.

Again, accuracy matters.

Incorrect information can create false alerts or unfair decisions.

Data Is Changing Cities

Smart cities use data to monitor:

Traffic

Public transport

Energy

Water

Air quality

Waste

Infrastructure

Public services

This can help governments make urban systems more responsive.

But connected cities also increase questions about privacy and surveillance.

The Rise of Data Brokers

Some companies build businesses around collecting, combining or selling information.

This created a large data-market ecosystem.

But consumers may not always understand how information about them is gathered or combined.

This has increased regulatory scrutiny around data brokers, targeted advertising and consent.

AI Makes Data Quality Even More Important

Generative AI has increased the demand for enormous datasets.

At the same time, it has made data provenance more important.

Organizations increasingly need to ask:

Where did this data come from?

Do we have the right to use it?

Is it accurate?

Does it contain personal information?

Could it create bias?

Can its origin be documented?

These questions will become increasingly important as AI becomes embedded in more industries.

The Future of Data

The next stage of the data economy is likely to involve more:

AI-generated information

Real-time analytics

Connected devices

Digital twins

Automated decision systems

Data marketplaces

Synthetic data

Privacy-enhancing technologies

Organizations will need to balance speed with responsible governance.

Final Thoughts

Data has become extremely valuable because modern organizations can use information to improve decisions, personalize services, automate processes and develop new products.

But data should not be treated as an unlimited version of oil.

Unlike physical commodities, data can be reused, copied and shared, and its value often depends on context, quality and how effectively it is analyzed. The OECD's analysis emphasizes precisely these differences.

As artificial intelligence and cloud computing expand, the strategic importance of data will continue to grow.

The organizations that benefit most will not necessarily be those collecting the most information.

They will be those that can turn high-quality data into useful insights while protecting the rights and trust of the people behind that information.

Tags: Data, Technology, Global Economy