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Data & Intelligence

The Ins And Outs Of A Data Marketplace

data marketplace

In the recent Data & Analytics Summit organized by, the emphasis on data as a valuable asset by many leaders across the globe clearly proves that companies are looking into new revenue opportunities to monetize data. In a Forbes article, Doug Laney pointed out several ways to monetize data, including digital products and services, licensing data, inverted data monetization, and trading with data. Industry leaders across healthcare, financial services, retail, manufacturing, and many more have prioritized data as an organizational asset. They have been thinking about the right quality data, at the right level, to the right people, at the right time, for the right decisions.

A data marketplace is an architecture in which data sharing and data monetization are enabled. According to the Cloud data warehouse company Snowflake, “A data marketplace is an online transactional location or store that facilitates the buying and selling of data.” This is driven by the increasing speed of volume, variety, and velocity of big data as first-party data within an organization or third-party data such as Google Analytics. A data marketplace can also be an internal-facing platform supporting business process optimization and new revenue opportunities by saving money.

Here are some statistics revealing the economic motivation behind data marketplaces:

• IDC estimates that by 2022, big data and analytics revenue will reach $274 billion worldwide.

• Accenture research estimates that by 2030, over 1 million organizations worldwide shall monetize their data assets, unlocking more than $3.6 trillion in value.

Consider this example. Mayo clinic created a massive digital health patient data marketplace called the Clinical Data Analytics platform. This platform includes details about patients, including disease patterns, diagnosis, digital tracing, and any complications during treatment and their corresponding care plan. With appropriate security and compliance in place, Mayo clinic has had many healthcare organizations, payers, providers, and life science companies alike gain access to de-identified patient data using this marketplace approach, through appropriate licensing deals in efforts to do new drug discovery, understanding treatment patterns for specific therapeutic areas and save lives.

Here are some key considerations of a data marketplace:

A data marketplace is built within an organization using a variety of data products or data as a service (DaaS), including queries (typically SQL), reports, data services, application programming interfaces (API), machine learning models, and physical or virtual views, among others. Appropriate pricing and incentive models are created by data providers.

If an organization is building an ecosystem of data providers and consumers (similar to the Clinical Analytics platform example from Mayo), a platform strategy may be appropriate. This is where producers and consumers have a common value proposition that is shared by the platform.

Interoperability is a key function of the data marketplace enabled by an API — which, in addition to lightweight data sharing between data producers and consumers, will offer up business models such as crowdsourcing and cross-industry use cases.

An organization building a data marketplace should consider its role as a data provider. McKinsey has a good article that can provide additional details on the types of data marketplace providers and their functions.

Key features that enable a data marketplace include data lineage, a smart data catalog to define the meaning of critical data elements, an AI-driven metadata engine, data products using APIs, data quality scorecards, information security, and robust yet agile data governance with well-defined policies and procedures.

While organizations should strive to build smart, AI-driven data marketplaces, human-centered design plays a key role in consuming data for meaningful use. Therefore, a user interface that is easy to use and can deliver data through multiple form factors (i.e., smartphone, tablet, computer) is critical for marketplace adoption.

A data marketplace can also be an internal-facing tool serving analytic use cases such as customer segmentation, data quality scorecards that track the quality of an internal system in dimensions, such as completeness and accuracy, and a data catalog that can effectively serve as a “Google” for the enterprise to search and discover enterprise data assets.

Here are some examples of data marketplaces:

  • Certified and governed open data marketplaces from the United States government such as OECD and and open financial datasets by World Bank.
  • International data marketplaces such as Nikkei Asia.
  • Niche data marketplaces such as geospatial or consumer identity vendors.
  • The Google data marketplace offered in Google Big query that’s licensed by Google data partners such as Dow Jones, AccuWeather, and Xignite.
  • The Snowflake data marketplace.
  • The Adobe data marketplace.

Here are some next steps for an organization looking to get started with a data marketplace:

  1. Identify the data value you provide with your first-party data and the category of data marketplace provider you want to play (raw data provider, data aggregator, platform provider, etc.).
  2. Identify two main categories or personas that will support the data marketplace, including data consumers who will use the data and data partners who will enrich the data.
  3. Create a reference architecture for the data platform enabling the data marketplace, appropriate control planes and data governance, and the technologies that will enable the marketplace.
  4. Put the appropriate licensing models and contracts in place before exposing the data to consumers.
  5. Build the data marketplace components, including data collection, data curation, data governance, APIs serving as data products, and appropriate data security and compliance levels.
  6. Build ongoing functions to monitor, measure, and continuously improve data products and features.

According to Forbes Senior Contributor Gil Press, the previous decade saw growth of almost 5,000% in “the amount of data created, captured, copied, and consumed in the world.” As a result, organizations are noticing that data is a key asset that can generate revenue, optimize business processes and innovate digital products. My recommendation is for businesses to consider assessing this need and identify how to enhance their existing architecture to establish a data marketplace.

This article originally appeared here.

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Arvind Murali, Chief Data Strategist

Arvind Murali is the Chief Data Strategist for Data Governance with Perficient. His role includes defining data strategy and governance to deliver transformative data platforms. Arvind has served as an executive advisor for data strategy and governance to organizations across several industries. Arvind’s dedication to solving challenges and identifying new opportunities has provided valuable business-focused results for clients, such as providing self-service access to data for global sales teams; helping physicians create informed wellness plans; and delivering insights about current supply chain inventories. He is a passionate Vlogger on YouTube and discusses real-world insights, data platform trends, and the importance of governance as big data continues its exponential growth.

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