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The enterprise data advantage: Turning information assets into strategic value

New York | Published in AI, Board and Data | 15 minute read |    
The image features a sophisticated office setting with futuristic holographic data displays and digital streams. In the background, there are subtle visuals of historical newspapers and documents, including folded stacks and old headlines, seamlessly integrated with the modern tech environment. The image uses vibrant blue and white tones for the tech elements and sepia accents for the newspaper visuals, symbolizing the convergence of past and present in digital transformation. (Image generated by ChatGPT 4o)

In 1998, I walked into the headquarters of our newly acquired newspaper group in Leeds, a result of a private equity-backed management buyout of United News & Media’s regional newspaper and magazine titles. Tasked with ‘doing the internet’, I found a small semblance of online activity in the form of a basic advertising website for the Yorkshire Post. A couple of weeks of getting my feet under my desk yielded a digital goldmine - 271 years of content waiting to be transformed into digital assets.

We had acquired 110 newspaper titles, with the oldest being the Leeds Intelligencer (now the Yorkshire Post) dating back to 1754. While others saw dusty archives, I saw untapped digital assets. Over the next four years, we embarked on an ambitious digitisation project, transforming centuries of newsprint into structured, searchable content that we could syndicate to platforms like EBSCO and Factiva. I used to call it “Money for Old Rope” - turning long-forgotten historical content into new revenue streams.

A quarter of a century later, enterprises face the same fundamental challenge - identifying and unlocking value from their information assets - but at a vastly different scale. Today’s organisations generate more data in a day than our newspapers produced in a century. The tools, technology, and opportunities have evolved dramatically, but the core principle remains: every organisation sits on potential data assets that, with the right vision and execution, can be transformed into strategic value.

Think of Bloomberg GPT, which represents the modern evolution of this journey. Where we were digitising static content for basic search and syndication, Bloomberg has built an AI model that can actively analyse financial statements, decode market trends, and generate sophisticated market insights in real-time. The fundamental principle - turning information assets into value - remains the same, but the sophistication and speed of value creation have increased exponentially.

Yet many organisations still struggle with the same question we faced in 1998: How do you transform information assets into strategic value? The difference is that modern enterprises have far more sophisticated tools at their disposal. What took us years of manual digitisation can now be automated in days. What once required massive physical data centres can now be spun up in the cloud in minutes. The barriers aren’t technical anymore - they’re strategic and organisational.

This shift has profound implications for how enterprises think about and value their data assets. It’s no longer just about storage and accessibility; it’s about treating data as a product, understanding its market value, and building the capabilities to monetise it effectively.

Understanding Your Data Asset Portfolio

The first step in unlocking data value is understanding what you have. Through the Well-Advised Framework I developed while leading AWS’s Professional Services Global Advisory practice, I’ve found that organisations need a structured way to evaluate all their potential sources of value - including their data assets.

A common pattern seen across industries is organisations fixating on customer data as their primary data asset, while overlooking the potential value in their other data streams. For instance, manufacturing companies often possess rich operational data - from machine performance metrics to quality control measurements - that could provide valuable benchmarking insights for their entire industry or allow them to implement predictive maintenance with the goal of increasing machine efficiency.

Today’s enterprise data assets typically fall into five categories:

1. Created Data

This is the intellectual property and content your organisation deliberately produces - whether that’s transactional records, created content, or research outputs. My experience with United News & Media illustrates this perfectly: those 271 years of newspaper content represented an immense archive of deliberately created data. Each article, editorial, and advertisement was intellectual property with potential value. In today’s world, created data might include everything from financial market analysis reports at Bloomberg to the transaction records that companies like Visa or Mastercard generate. This created data often becomes more valuable over time, especially when it captures unique historical moments or trends.

2. Operational Data

This includes everything from process metrics to performance data - the ‘digital exhaust’ of your day-to-day operations. When properly structured and analysed, this can provide insights not just for your organisation but potentially for your entire industry. Looking back at the newspaper business, our operational data around circulation, distribution patterns, and advertising effectiveness contained valuable insights about regional economic trends and consumer behaviour.

3. Customer Interaction Data

Beyond basic transaction records, this encompasses everything from service interactions to product usage patterns. The value here often lies in the aggregate insights about market trends, behaviour patterns, and emerging needs. The Bloomberg GPT example shows how customer interactions with financial data and news can be transformed into an intelligent system that understands and predicts market behaviour.

4. Market Intelligence

This is data you collect about your market, competitors, and industry trends. While individual data points might have limited value, the comprehensive view of market dynamics you build over time can be incredibly valuable - especially when combined with AI tools that can analyse patterns and predict trends.

5. Synthetic Data

This is artificially generated data that mirrors the statistical properties and patterns of real data. Whether it’s creating training data for AI models, testing scenarios that would be too risky or expensive to run, or augmenting limited datasets, synthetic data is becoming a strategic asset in its own right. For example, financial institutions use synthetic data to test fraud detection systems, and autonomous vehicle companies generate millions of simulated driving scenarios to train their systems. Organisations increasingly turn to synthetic data to overcome data scarcity challenges or to protect sensitive information while maintaining analytical capabilities. The value of synthetic data lies in its ability to scale beyond the limitations of real-world data collection, enable testing of edge cases and rare scenarios, protect privacy while maintaining data utility, accelerate development cycles, and reduce dependencies on production data.

From Data Assets to Value Creation

Understanding your data portfolio is just the starting point. The real challenge - and opportunity - lies in turning these assets into tangible business value. When we syndicated our newspaper archive, the path to monetisation was relatively straightforward - digitise, structure, and license. Today’s landscape offers far more sophisticated opportunities for value creation.

1. Direct Monetisation

The most obvious path is turning data directly into revenue streams. While our newspaper digitisation project demonstrated this through content syndication, modern approaches go far beyond simple licensing:

2. Operational Enhancement

Sometimes the greatest value comes from using data to improve your own operations:

3. Market Positioning

Data assets can also create strategic advantages:

Building Value Creation Capabilities

The technical ability to store and process data is just the foundation. I’ve found that organisations need three interconnected capability areas to successfully monetise their data assets.

1. Data Foundation

When we digitised the newspapers, we learned a fundamental lesson: the quality of your foundation determines everything that follows. A robust data foundation begins with clear governance and ownership structures. You need to know who’s responsible for data quality, who can access what, and how data flows through your organisation. This foundation must include scalable infrastructure that can grow with your ambitions, along with security and compliance architectures that protect your assets. As organisations increasingly work with synthetic data, this foundation must also include capabilities for generating, validating, and tagging artificial datasets that maintain statistical validity while protecting sensitive information.

2. Value Extraction

This is where many organisations stumble, and it’s not just about having the right tools. Success in value extraction requires a blend of technical expertise and domain knowledge. Your teams need to understand both the possibilities of advanced analytics and AI/ML applications, and the specific business contexts where these insights create value. The rise of synthetic data has added another dimension to this challenge - organisations now need the capability to create and work with artificial datasets that can augment their real-world data. This isn’t just about technical skills; it’s about understanding how to design data products that solve real business problems and deliver value to users.

3. Commercial Execution

While this was relatively straightforward in our newspaper digitisation project - we had clear customers in EBSCO and Factiva - today’s data monetisation requires a more sophisticated commercial approach. Successful organisations build strong capabilities in market analysis and segmentation, develop nuanced pricing strategies that reflect the value of their data assets, and create effective partnership networks for distribution. They also invest in customer success functions that help customers extract maximum value from their data products and services. This requires a deep understanding of customer use cases, the ability to demonstrate clear ROI, and the flexibility to evolve offerings based on market feedback.

Building vs. Buying Capabilities

The decision to build internal capabilities or access them through partnerships is one of the most significant strategic choices organisations face when monetising their data assets. My experience at AWS showed that success depends not on choosing one approach exclusively, but on understanding where each approach delivers the most value.

Building internal capabilities makes most sense when data assets form the core of your competitive advantage. This typically applies when you need deep integration with existing operations or have unique security and compliance requirements that demand direct control. Long-term data monetisation strategies often benefit from in-house capabilities, particularly when you’re developing proprietary approaches to data generation or analysis.

However, the partner approach often proves more effective when speed to market is critical. When I advise customers evaluating this option, I emphasise that partnerships can provide immediate access to sophisticated capabilities without the lengthy process of internal capability building. This is particularly valuable when the required expertise lies far from your core competencies or when you want to validate opportunities before making significant investments.

The most successful organisations I’ve worked with typically adopt a hybrid approach. They build critical capabilities in-house while leveraging partnerships to accelerate growth and fill capability gaps. This balanced strategy allows them to maintain control of strategic assets while benefiting from external expertise and innovation.

Implementation Roadmap

When I established the digital division for our newspapers, we had to move quickly despite the technological limitations of 1998. In a recent keynote at Monday.com, I emphasised a critical lesson learned since then: aiming for data perfection often becomes the enemy of value creation. The key is to start creating value with the data you have, even if it’s not perfect.

The newspaper digitisation project illustrated this perfectly. Our scanned articles had imperfect OCR and minimal metadata tagging, but we recognised that waiting for perfection would cost us more in lost opportunity than moving forward with ‘good enough’ quality. EBSCO and Factiva valued the content’s historical significance more than its technical perfection, and their customers were already equipped to handle varying quality levels in archival material. This early lesson showed that market opportunity often matters more than technical perfection.

Phase 1: Discovery and Value Identification (1 Week)

Focus only on what matters for initial value creation:

Phase 2: Pilot Launch (2-3 Weeks)

Start creating value immediately:

Phase 3: Iterate and Improve (Ongoing)

Build on what works:

Measuring Success

Success metrics should align with the Well-Advised Framework’s pillars:

Financial Metrics

Innovation Metrics

Customer Value Metrics

The Data Value Imperative

When I look back at that newspaper digitisation project from 1998, it feels almost quaint by today’s standards. We took centuries of content and turned it into a simple syndication revenue stream. But the fundamental lesson remains relevant: every organisation has untapped value in its data assets.

The key differences today are speed and scale. We have the tools to move faster and create more sophisticated value propositions, from real-time analytics to synthetic data generation. But this also means the opportunity cost of delay is higher. Organisations that wait for perfect data before acting risk falling behind more agile competitors.

The opportunity is clear: organisations that can identify their valuable data assets and quickly turn them into strategic advantage will thrive. Those that wait for perfect conditions will find themselves playing catch-up. Here are three key actions every board and executive team should take:

  1. Start with the data you have - perfection is the enemy of value
  2. Move quickly with focused pilots to prove value
  3. Build sophistication based on measured outcomes, not assumptions

The question isn’t whether your organisation has valuable data assets - it’s how quickly you can turn them into strategic value. I encourage every executive to look at their organisation’s data assets with fresh eyes, identify a clear opportunity, run a pilot, and take the first step toward unlocking their enterprise’s data advantage.

About the Author

Mario Thomas is a transformational business leader with nearly three decades of experience driving operational excellence and revenue growth across global enterprises. As Head of Global Training and Press Spokesperson at [Amazon Web Services](https://aws.amazon.com) (AWS), he leads worldwide enablement delivery and operations for one of technology's largest sales forces during a pivotal era of AI innovation. A Chartered Director and Fellow of the [Institute of Directors](https://www.iod.com), Mario partners with Boards and C-suite leaders to deliver measurable business outcomes through strategic transformation. His frameworks and methodologies have generated over two-billion dollars in enterprise value through the effective adoption of AI, data, and cloud technologies.