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Infonomics in 2024: What it is, Case Studies & Best Practices

Cem Dilmegani
Updated on Jan 12
6 min read

Though IT and business leaders regularly state information is their most valuable asset, they fail to value or manage it like one. Data can be sold to monetization or organizations can use it to provide value-added services to their customers. Therefore businesses should measure the value of their data to know how much it would value to your organization.

What is Infonomics?

Infonomics is the organizational approach that accepts the value of information by recognizing, accounting and handling data as a business asset. Businesses are measuring the value of information using infonomics. Organizations should change their architecture, hierarchy and governance strategy to be able to leverage infonomics.

Infonomics is growing in popularity because data is quite different than services, software products, physical products or any other thing that businesses are used to selling. Data is valuable, can be consumed extremely fast but it is very easy to transfer and can depreciate quickly. Organizations are working on finding ways to monetize the insights buried in the data in an optimal way.

Gartner defines the terms as follows:

Infonomics is the emerging discipline of managing and accounting for information with the same or similar rigor and formality as other traditional assets and liabilities (such as financial, physical and intangible assets and human capital). Infonomics posits that information itself meets all the criteria of formal company assets, and, although not yet recognized by generally accepted accounting practices (GAAP), it is increasingly incumbent on organizations to behave as if information were a real asset.

Why is it important now?

Information drives business processes and decisions.  According to Gartner, 30% of leading organizations will adopt infonomics practices and value their information assets, maintaining a balance sheet for internal purposes by 2022. As business and IT leaders recognize the importance of information, taking advantage of it gets easier because the potential of generating revenue from information can be higher than traditional assets due to two limitations of traditional assets:

  •  Traditional assets have transactional limitations which means they cannot be used simultaneously across multiple use cases. For example, an airplane can only fly one route at a time.
  • Traditional assets’ value decreases with usage. Secondhand assets are always cheaper than first-hand ones.

However, data as an asset doesn’t have these limitations, in fact, its value increases as it is processed more.

What are the steps of information monetization?

Organizations want to implement infonomics so that they can monetize information. A successful information monetization has these steps:

  1. Create an information product role within the company to develop the market for the information and to productize it. Data becomes a business asset rather than an IT asset if the information product role is well-established.
  2. Record all available information assets such as first-, second-, third-party data. Using a modern data catalog can help organizations gain such a view while improving compliance of their data. If organizations don’t know what data they have, they cannot benefit from the information as an asset. Some types and sources of information that businesses can leverage are as follows: operational data, enterprise data, commercial data, public data, social media and web content.
  3. Select your approaches for direct and indirect monetization:
    1. Direct monetization consists of selling or trading data to third parties. Organizations can use this method to get the exchange value of information immediately with a tangible asset. There are important aspects to decide before companies can go ahead with direct monetization. These include
      1. Pricing: One-off or subscription based
      2. Categorization: What are the ways to split the company’s information sources to derive the most value from them?
      3. Additional services: What will be the UI for the data selection? Depending on the number of customers, there may be need to build a flexible UI for data selection and manipulation. For example, export functionalities are key. They empower users to process the data but they can also enable users to pull all the data they may ever need from your platform and stop using it.
    2. Indirect monetization is using data to perform analysis on it. Analytics enables organizations to improve efficiencies in processes, to reduce risks and to develop new products. Organizations need to measure the results of analytics projects so that they can understand the economic benefits they get from monetization.
  4. Test your direct and indirect approaches to understand whether they are feasible or not.  If they are, launch your information product into the market.

These are the steps that an established company would follow to monetize its information assets. For a startup or smaller company, such a process is rarely necessary. Smaller companies are far more aware of the value of their limited assets. Therefore, in the case of a smaller company challenge is not finding the valuable data but finding the right go-to-market approach to derive value from the data.

What are the benefits?

  • Improved data management: You can’t manage what you don’t measure. Implementing an infonomics approach into the organization enhances data management.
  • Additional revenue streams due to data sales
  • Better management of 3rd party data sources: Legal contracts may fail in indemnification when data is misused or lost. Approaching data as an asset and bringing it into balance sheet can empower organizations’ rights when they face such a situation.
  • Smarter supplier management: How can an organization understand that they are overpaying for data security tools if they don’t know the value of the data? If the value of data is less than the value of the security tool, then paying for it may not worth it.

What are the best practices?

  • Compliance with privacy regulation is crucial for data monetization as companies will not pay for data that will bring them legal problems down the road.
  • Establish a data security budget: When you measure the value of available information, it is a good time to create a data security budget. If the budget is more costly than the value of information assets, it may not worth investing in expensive security tools.
  • Consider separating information and technology in your technology function so that company culture can easily shift to a data-centric culture.
  • Focus on use of data within the organization: Your organization doesn’t need to generate economic benefit only by selling or trading data. You can gain insights from data that will enable you to increase the overall performance of business processes.

What are example case studies?


Dawex is a data marketplace that connects data suppliers and buyers. Suppliers set the price for the data they own or ask for an opening price from Dawex’s estimations. The company makes money from per-transaction commissions, a subscription, or a set of variable fees based on optional services.


Pirelli is a tire manufacturing company that launched the Connesso system in 2017. Connesso systems collect data from tires and provide key information and real-time data about pressure and temperature, use and maintenance of every tire to drivers. It also allows fleet managers to enhance replacement scheduling and efficiency optimization. Connesso’s business model is a pay-per-use fee: the driver pays to access the data obtained by tires and to use the services provided by Pirelli.

What are the leading data monetization companies?

Direct Monetization

Direct monetization can be achieved through launching your company’s data marketplace or featuring your data on data marketplaces. Feel free to check the vendor list in our data marketplace article which includes vendors that offer both services.

Indirect Monetization

The core of indirect data monetization is getting value from data by data analysis tools which inform the business. Organizations gain insights from data and analytics tools to generate value within the enterprise. Check out our 200+ business intelligence software list for a comprehensive list of business intelligence vendors.

For more on Infonomics

The below video is a webinar of Doug Laney who is a senior analyst and advisor with Gartner’s Chief Data Officer research group. He is sharing his thoughts on infonomics, methods for applying asset management best-practices to information, and how to monetize information, including real-world examples of how companies and government agencies have monetized their information.  Here is a table of content for the video: 3:52 – Background of Infonomics 6:16 – Why is information a valuable asset? 8:30 – How do information-driven companies look like? 9:31 -What is infonomics and how it works? 15:50 – Methods and examples of data monetization 18:14 – Managing Information as an Asset 21:54 – Valuation of Information and examples of information valuation models 28:44 – Recommendations and Q&A  

Once you know the value of available data, it is time to decide which data security tools you will use for data protection efforts so that it will be worth investing in. We have multiple articles that can help you choose a data security solution:

If you still have questions about infonomics, don’t hesitate to contact us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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