Big Data Marketplace: Buy, Sell, and Share Big Data


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Companies are sitting on gold mines of data and don’t know what to do about it. As the costs of collecting, managing, and warehousing data increase, so does the pressure to generate ROI. Terabytes turn into petabytes, 5-figure infrastructure bills become 6-figure bills, and data collection strategies transform into data governance initiatives. More data, more problems. Companies are rightly turning to data sharing and commercialization as a countermeasure. Sharing data internally across the organization leads to product and service innovations, new revenue streams, and increased collaboration. Commercializing data products leads to new lines of business and transitions data from a cost center to a profit center. Harnessing insights hidden in data offers quantifiable value for organizations. By 2030, it’s estimated over 1 million companies across the world will monetize data, generating over $3.6 trillion in value. 

What is a Big Data Marketplace?

Data marketplaces enable purchasing, selling, sharing, and exchanging data. Organizations put their data before an audience and outsource the data transfers to the marketplace platform. (Pro tip: there’s no difference between “data marketplace and big data marketplace.” The terms are synonymous.) Here are a few examples of big data marketplaces: 

  • Government Data Marketplace: Data marketplaces provided by national or regional governments. For example, is the home of the U.S. government’s data. Consumers can find tools and resources for research, development, and design. 
  • IoT Data Marketplace: Aggregated third-party data from smartphones, electric vehicles, smart home devices, etc. Typically, consumers utilize real-time data streams from IoT marketplaces. For example, an agricultural company may use field data from connected devices to optimize harvests. 
  • Databricks Data Marketplace: This marketplace is proprietary to Databricks, the data lake/lakehouse provider. Their marketplace enables the exchange of Databricks-specific data products like notebooks, dashboards, and machine learning models.
  • CME DataMine: This self-service cloud platform allows external parties to access CME group data. It’s a data source for cash, futures, and options information like interest rates, equities, foreign exchange, and more. 
  • B2B Data Marketplace: Many data marketplaces provide B2C and B2B data solutions. Others, like Revelate, focus solely on B2B tools for organizations and enterprises.  

Types of Big Data Marketplaces

There are four types of big data marketplaces: internal, external, hybrid, and multi-layered. Each marketplace type’s risk profile, architecture, and technical requirements are unique. 

An internal data marketplace (aka “private data marketplace”) is typically found in enterprises or other data-rich organizations. A single organization uses an internal marketplace to manage and share data assets internally with minimal risk. Internal data marketplaces allow teams to make more informed, data-driven decisions. 

An external data marketplace (aka “public data marketplace”) serves external organizations and individuals. External data marketplaces enable data consumers to access a wide range of data sets from one or more providers. 

A hybrid data marketplace combines the requirements and considerations of both internal and external marketplaces. Hybrid data marketplaces provide secure access to external parties and streamline internal data sharing. For example, a data product on a hybrid marketplace may have separate versions for internal and external consumers.

A multi-layered data marketplace enables access to multiple “layers” of data. These layers include raw, processed, or derived data that has been modified. Because multi-layered data marketplaces serve different types of data buyers, each consumer may have additional permissions and security rules. 

Challenges of Big Data Marketplaces

Common operational challenges for big data marketplaces include limited data set information, prolonged sales processes, and proprietary data formats. Standardization is often limited to brief descriptions of data sets. This leaves data buyers to determine whether the product is worth their time. Sometimes the consumer must download sample data for evaluation, which can incur additional costs. This complex sales process can frustrate prospects and lower sales. Many data marketplaces are closed ecosystems that require data providers to load data in a specific, proprietary format. Big data providers must then replicate data for different clouds and regions, which increases compute and operational costs. Evolved data marketplaces address these challenges with more flexible requirements to reduce friction between data consumers and providers. Revelate offers a centralized data fulfillment tool that simplifies data product cataloging, segmentation, and marketing. Organizations can effectively aggregate large data sets to provide products that consumers can easily access and understand. Revelate allows data providers to manage data purchasing, sharing, and exchanges between data buyers. This streamlines the sales process and makes the end-to-end experience easier and more efficient. 

Features of a Big Data Marketplace

Ultimately, data marketplaces generate and fulfill data orders between providers and consumers. Providers use marketplaces for packaging, marketing, pricing, and delivering data products. Consumers use them to discover, access, and purchase these data products. The features that allow extensive data marketplaces to operate and manage transactions include: 

  1. Data Productization: Create and maintain data solutions for purchase by data buyers. 
  2. Data Discovery: All data products exist within a searchable catalog where consumers can find and preview data. 
  3. Data Access: Access to data tools and sets is secure and controlled through APIs, access control integrations, etc.
  4. Data Licensing: Data providers can sell and license data products by designing a specific language for its use and transaction types.  
  5. Data Integrations: providers and data consumers can easily send and receive data from other platforms and sources. 
  6. Data Distribution: options for data product shipment for related target groups (i.e., direct download, API, etc.). 

What Data Can I Buy on a Big Data Marketplace?

Big data marketplaces offer real-time data streams, massive data sets from various industries or regions, and more. Some examples of data products you can find on a big data marketplace include: 

  • Data from sensor signals in IoT devices, such as electric vehicles and smart devices in homes and buildings. 
  • Toolboxes for big data analytics to help data providers deal with large data sets. 
  • Aggregated international data from specific countries or regions.
  • Cross-industrial data streams that encourage data consumers to create innovative products. 
  • Government information includes nautical charts, monthly house price indexes, healthcare provider data, manufacturing and trade inventories, etc. 

Explore Data Fulfillment with Revelate

Ensuring your organization has the right technology and processes to manage and share data safely and effectively can be challenging. With the right tools, your team can provide your data processes that are sophisticated and secure. Revelate’s data fulfillment platform reduces the distribution burden for IT and data analytics teams. See our product pricing data examples to understand how to best market data. Revelate easily integrates into your existing data ecosystem to prepare, package, and share your data from anywhere to anyone. Ready to revolutionize your data-sharing capabilities? Get Started.

Frequently-Asked Questions about Big Data Marketplaces

How can big data be helpful for a business? 

Big data analytics help organizations improve business operations, make data-driven decisions, and better understand customers. Some common ways modern companies are using big data analytics include: 

  • Customer segmentation and profiling: Segment customers by demographic, purchase history, or behavior to create targeted marketing campaigns and improve customer service.
  • Operational efficiency: Identity processes that improve supply chain management, inventory, and production.
  • Predictive analytics: Predict future outcomes related to sales, customer behavior, market data trends, and more. 
  • Risk management: Proactively identify risks and vulnerabilities and mitigate issues before they become significant problems. 
  • Product development: Use customer preferences to develop new products and services and improve UX. 

What is the future of big data? 

The future of big data will likely be defined by continued growth and innovation. The prevalence of AI and machine learning will impact data analysis. Interpreting data with algorithms and automation allows businesses to derive insights faster and more efficiently. The focus on data privacy and security will continue to increase as more data is stored and shared. As more organizations and individuals collect and analyze big data, there will be more opportunities to collaborate and share data. Finally, IoT will continue to evolve, and the number of connected devices will scale significantly, generating more data than ever. This data will be used to improve product design, create new business models, and optimize daily operations.