Monetising, Managing, and Measuring Data as an Enterprise Asset

Introduction

It’s almost trite today to note that data has emerged as a critical component of business strategy, driving innovation, enhancing customer experiences, and optimising operations.

Yet, despite its pivotal role, many organisations have yet to fully embrace the concept of treating data as an enterprise asset.

This oversight is where the discipline of infonomics comes into play, offering a framework for businesses to monetise, manage, and measure data with the same rigour applied to traditional assets.

The Foundation of Infonomics

At its foundation, infonomics is an economic theory that treats data as a separate asset class. Data is intangible, as opposed to tangible assets like machinery or real land. Data also contains both prospective and realised value, which can have a major impact on an organisation’s bottom line.

Infonomics presents a set of rules and procedures for accounting, managing, and deploying data, similar to how financial, human, and physical assets are handled.

Recognising data as a genuine asset is one of infonomics’ fundamental ideas. This recognition is both intellectual and practical. For example, data-savvy organisations frequently have market-to-book ratios nearly twice as high as the market average, demonstrating the economic benefits of treating data as an asset.

By recognising data’s asset status, firms may begin to apply asset management concepts to their data, ensuring it is appropriately inventoried, evaluated, and utilised for optimal profit.

Data is NOT “The New Oil”

The comparison between data and oil is intriguing and often comes up in discussions about the value and utility of data in the modern economy.

At a macro level data is an important economic driver, much as oil has been for the past century. However, several key differences between data and oil underscore why data is not just “the new oil,” but rather a unique asset class with its own set of economic characteristics. The main differences include:

• Non-rivalrous vs. rivalrous: Data is non-rivalrous, meaning it can be used by multiple entities simultaneously without diminishing its value or utility to any single user. Oil, on the other hand, is a rivalrous resource; once it is consumed by one party, it cannot be used by another.
• Non-depleting vs. depleting: Data does not deplete through use. The more data is used, the more valuable it becomes as it generates insights, efficiencies, and further data. Oil is a depleting resource; its quantity diminishes with use, and it cannot be reused once consumed.
• Progenitive vs. non-progenitive: The use of data often leads to the creation of more data, making it a regenerative resource. For example, analysing data can lead to new insights, which generate more data for further analysis. Using oil does not naturally generate more oil.

Organisations that take advantage of these unique attributes thrive in today’s economy.

Monetising Data as an Asset

Another key infonomics concept is maximising data’s net realised value. This involves not only enhancing the revenue-generating potential of data but also minimising the costs associated with managing and securing it.

There are dozens of ‘data monetisation patterns’ that organisations have successfully leveraged to employ their data in measurably accretive ways. These include developing new data-driven products or services, licensing their data to third parties, or improving operational efficiencies using data and analytics.

These patterns can be broadly categorised into two main types: direct data monetisation and indirect data monetisation.

Direct Data Monetisation

Direct data monetisation involves generating revenue directly from data assets. This can be achieved through various means, including:

Selling or licensing data: This is the most straightforward form of direct data monetisation. Companies can sell their data outright or, more commonly, license it to others. This allows the data provider to maintain ownership and control over the data while generating revenue.

Data-enhanced products or services: Organisations can enhance their existing products or services with data, making them more valuable to customers. For example, a fitness tracker company might sell insights and analytics about health trends based on user data.

Data as a service (DaaS): Data is offered as a service, with customers able to subscribe to access datasets, analytics, and insights. This model provides continuous access to updated data, analytics tools, and insights as a service over the Internet.

Bartering or trading with information: In some cases, organisations might find value in exchanging data with other entities rather than selling it. This can be beneficial when both parties have data that can enhance the other’s business operations or offerings.

Specialised data and report subscriptions: Similar to DaaS, this involves creating subscription-based access to specialised reports or datasets that are regularly updated, providing ongoing value to subscribers.

Selling analytics solutions using data: Beyond selling data itself, companies can develop analytics solutions or platforms that leverage their data, providing customers with tools to derive insights relevant to their specific needs.

Indirect Data Monetisation

Indirect data monetisation involves using data to enhance business processes, improve decision-making, or create new products or services, thereby generating value indirectly. This includes:

• Improved decision-making: Leveraging data and analytics to make better business decisions that lead to cost savings and increased revenue.
• Enhanced customer experience: Using data to personalise customer interactions, improve service delivery, and increase customer satisfaction and loyalty.
• Operational efficiency: Using data to streamline operations, reduce waste, and optimise resource allocation.
• Product and service innovation: Using insights gained from data analysis to develop new products or enhance existing offerings.
• Risk management: Applying data and analytics to identify, assess, and mitigate risks, thereby protecting revenue and reducing costs.
• Data-enabled business models: Creating new business models or transforming existing ones by embedding data and analytics into the core of business operations.
• Each of these patterns represents a different approach to extracting value from data assets. The key to successful data monetisation lies in understanding the unique characteristics of your data, the needs of your potential customers or users, and the broader market dynamics. By adopting these patterns, organisations can unlock new sources of value and gain a competitive edge.

Managing Data as an Asset

Managing data as an asset requires a strategic approach to data governance, quality, and lifecycle management. Infonomics emphasises the importance of establishing clear policies and processes for data collection, storage, access, and disposal, ensuring that data remains accurate, relevant, and secure throughout its lifecycle.

The emergent and nearly mainstream role of the Chief Data Officer in leading these efforts underscores the need for executive-level oversight and the coordination of data management practices across organisations.

Adopting strategies from other asset management approaches to improve data asset management is a strategic move that can markedly enhance an organisation’s practices. Here are several ways to apply traditional asset management principles to the realm of data assets:

• Asset classification: Assets are categorised based on types (e.g., fixed assets, current assets, intangible assets) to manage them effectively. Similarly, classifying data based on its type, sensitivity, or usage (e.g., personal data, transactional data, reference data) can help in applying appropriate management and security practices.
• Inventory management: Regularly taking stock of traditional assets helps organisations know what’s available, where it’s located, and its condition. Similarly, maintaining a data catalogue or inventory allows organisations to know what data exists, its source, quality, and how it’s being used. Adopting inventory management practices helps in optimising data utilisation and avoiding data silos.
• Lifecycle management: Traditional assets go through stages such as acquisition, maintenance, and disposal. Similarly, data assets have a lifecycle from creation, usage, and archiving, to eventual deletion.

Each stage requires specific management strategies to ensure the data remains accurate, relevant, and secure. Adopting lifecycle management practices from physical asset management can help organisations optimise the use and value of their data over time.

Maintenance practices: Regular checks, repairs, and upgrades are performed to keep traditional assets in good condition. For data assets, this translates into regularly cleaning, verifying, and updating data to maintain its accuracy, relevancy, and quality. Implementing a routine maintenance schedule for data, akin to that of physical assets, ensures that data remains a valuable and reliable resource for decision-making.

Audit and compliance: Just as traditional assets are often audited for accounting purposes and to ensure regulatory compliance, data assets should also be periodically audited. This involves assessing data quality, accuracy, and compliance with relevant data protection and privacy regulations. Borrowing audit and compliance practices from traditional asset management can help organisations mitigate risks associated with data mismanagement and non-compliance.

Insurance and risk management: While traditional assets are insured against potential risks or damages, data cannot be insured in the same way. However, organisations can adopt risk management practices to protect data assets from cyber threats, breaches, and other risks. This includes implementing robust data security measures, data encryption, and regular security audits.

By adapting these asset management practices from non-IT-related fields, organisations can enhance the management of their data assets, and improve their potential and realised economic value. This approach not only elevates the strategic importance of data within organisations but also aligns data management practices with proven methodologies from other domains.

Measuring Data as an Asset

A critical aspect of infonomics is the ability to quantify data’s value. This process involves assessing both the potential and realised value of data. Potential value refers to the future benefits data can bring to an organisation, such as identifying new market opportunities or enhancing customer experiences.

Organisations can assess a data asset’s quality and scarcity characteristics, its relevance across a range of business processes, and its likely impact on non-financial key performance indicators to appreciate its potential value.

Realised value, on the other hand, is the tangible benefits already derived from data, such as increased sales from targeted marketing campaigns. Various traditional asset valuation methodologies can be applied to value data, including market-based, cost-based, and income-based approaches, enabling organisations to make informed decisions about data investments and management.

However, as data is a multipurpose and non-depleting asset that is more often licensed or in which its usage rights are exchanged for goods or services, valuation methods must take certain nuances into account.

Conclusion

Infonomics provides a comprehensive framework for recognising, valuing, and managing data as an enterprise asset. By adopting these principles and practices, organisations can unlock the full economic potential of their data, and drive innovation, competitive advantage, and financial performance.

By embracing infonomics, businesses acknowledge the intrinsic value of their data and commit to a strategic approach to its management and monetisation. The journey toward treating data as an asset may require a shift in mindset and organisational culture, but the potential rewards are significant.

This article was featured in the second Edition of our Driven by Data Magazine. You can download the magazine and read more articles like this by clicking here.