Data & Analytics (D&A) leaders need to demonstrate the tangible business value from their D&A and AI initiatives, including the rapidly evolving field of Generative AI (GenAI). As organizations strive to maximize the potential of their data assets, many are turning to innovative solutions like data marketplaces and exchanges. These platforms offer a powerful means to accelerate both tangible and intangible financial value from data use while meeting the growing demands for expansive data sharing and monetization.

The Data Value Dilemma

D&A leaders are under increasing pressure to show concrete returns on investment in data and AI technologies. However, quantifying the value of data assets and AI outcomes can be rather challenging. Traditional metrics often fall short in capturing the full spectrum of benefits that data-driven initiatives bring to an organization.

Enter Data Marketplaces – The Storefront for Data Consumers

Within data marketplaces, data is exchanged between providers and consumers. Data providers aim to share data, data products, or data services with users. Data marketplaces and exchanges provide a structured framework for organizations to share, trade, and monetize their data assets. These platforms typically offer a wide variety of information, ranging from market and business research and intelligence to demographic data, marketing and advertising data, scientific data, and much more.

Data providers often seek to monetize their data assets. Consumers enter data marketplaces looking for data that can benefit their business. For example, a GPS navigation company could be a data provider offering traffic-related data such as historical congestion and emissions reports to consumers on public data marketplaces. Data consumers can then use this traffic data to meet their specific business needs, such as helping a retail business optimize traffic planning or gain better insights into their sustainability indicators.

Considering who provides the data, these platforms come in two primary forms:

    • Internally managed
      Internally managed data marketplaces facilitate data sharing and collaboration within an organization. While primarily set up for internal use, many of these marketplaces can also consume data from external data markets and exchanges to some degree. Today, over 70% of internally managed marketplaces serve only internal consumers. About 30% of these marketplaces are already monetizing their data and commercializing it on the external market. For example, retailers use their internal data marketplaces to commercialize consumer data to their FMCG suppliers.
    • Externally managed
      These data marketplaces, also referred to as data exchanges, enable data transactions between different organizations. Examples of data exchanges include the Nielsen Marketing Cloud, Dun & Bradstreet, Precisely and Experian. These platforms offer a wide range of data types, including demographic and psychographic information, consumer behavior and preferences, purchasing history, and credit information. In addition to these commercial platforms, more public and open data is becoming available. Examples include the portal for European data, as well as numerous national and local gov, market-specific, and even organizational initiatives like i.e. the Infrabel Open Data Portal , which can be integrated in your data initiatives.

Unlocking the Advantages

By leveraging these platforms, businesses can unlock several key advantages:

  1. Enhanced Data Discovery and Access
    Data marketplaces make it easier for users across an organization to find and access relevant data sets. This improved discoverability can lead to faster decision-making processes, reduced duplication of efforts, and increased cross-departmental collaboration.
  2. Data Monetization Opportunities
    External data exchanges open up new revenue streams by allowing organizations to monetize their data assets. This can include selling anonymized customer insights, offering industry-specific datasets, and providing real-time data feeds. The same principle can also be applied to internal data sharing efforts where departments or sister companies also agree on an inter-company cost compensation mechanism.
  3. Improved Data Quality and Governance
    To participate in data marketplaces, organizations must adhere to certain quality standards and governance practices. This drive towards better data management can result in enhanced data accuracy and reliability, stronger compliance with data regulations, and increased trust in data-driven decision making.
  4. Accelerated Innovation
    Access to diverse datasets through marketplaces can fuel innovation, especially in AI and GenAI applications. Benefits include more comprehensive training data for AI models, novel insights from combining internal and external data sources, and faster development of data-driven products and services.

Overcoming Implementation Challenges

While the potential benefits are significant, implementing data marketplaces and exchanges comes with its own set of challenges. These include ensuring data privacy and security while enabling sharing, establishing common data formats and exchange protocols, determining fair pricing models for data assets, and fostering a data-sharing mindset within the organization. To address these challenges, D&A leaders should invest in robust data governance frameworks, collaborate with legal and compliance teams to navigate regulatory landscapes, develop clear data valuation methodologies, and promote a culture of data sharing and collaboration through change management initiatives.

Measuring Success

To demonstrate the value of data marketplaces and exchanges, D&A leaders should focus on both quantitative and qualitative metrics. These can include revenue generated from data monetization, cost savings from improved data access and reduced duplication, time-to-insight measurements for data-driven projects, user adoption rates of internal data marketplaces, and innovation metrics such as new products or services developed using shared data.


As the demand for data-driven insights continues to grow, data marketplaces and exchanges offer a powerful solution for organizations looking to maximize the value of their data assets. By facilitating easier data sharing, enabling new monetization opportunities, and driving innovation, these platforms can help D&A leaders demonstrate clear business value from their initiatives.

The journey to implementing successful data marketplaces and exchanges may be complex, but the potential rewards – in terms of financial value, operational efficiency, and competitive advantage – make it a worthwhile endeavor for forward-thinking organizations. As we move further into the age of AI and GenAI, those who can effectively leverage these data-sharing ecosystems will be well-positioned to thrive in an increasingly data-centric business world.



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Databricks‘ acquisition of Tabular puts pressure on Snowflake and Confluent as cloud data management becomes crucial for AI initiatives. Databricks recently acquired Tabular for an estimated $1 to $2 billion and was strategically announced during main competitor’s Snowflake annual conference. This move highlights the growing importance of cloud data management for AI applications, and how Tabular’s role in the open-source project Apache Iceberg makes them a strategic asset.

Iceberg: A Key-component in Data Management for AI

Iceberg is an open-source project that simplifies data sharing across cloud platforms and on-premises infrastructure. As AI applications become widespread, managing the data they require becomes a critical challenge. Iceberg acts as an abstraction layer, allowing data to flow seamlessly between various cloud storage services and analytics engines.

Tabular: The Iceberg Leader

Tabular’s founders played a key role in developing Iceberg and are the project’s largest contributors. Their acquisition by Databricks positions Databricks as the leader in Iceberg development. This strategic advantage could significantly impact the future of cloud data management.

Snowflake under pressure?

Snowflake, a major competitor of Databricks, has also developed tools for working with Iceberg. The bidding war for Tabular indicates companies see Iceberg as a strategic asset and potential threat. Snowflake’s recent stock price decline and leadership changes further highlight the pressure they face. Snowflake is BTW not the only relevant competitor with Iceberg connected solutions. Confluent, also mentioned as a Tabular M&A candidate, Microsoft, and others can also push data into Iceberg use Apache Flink.

The Future of Cloud Data Management

Databricks’ acquisition of Tabular presents a significant challenge to Snowflake and other competitors. How Databricks leverages Iceberg will be crucial in determining the leader in cloud data management for the AI era. This situation underscores the ever-evolving nature of the technology landscape, where younger startups can quickly disrupt established players.


  • Cloud data management is critical for AI applications.
  • Iceberg is a key open-source project for data management.
  • Databricks’ acquisition of Tabular gives them a strategic advantage in Iceberg development.
  • Competitors face pressure to adapt to the changing landscape.



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