Why a Data Marketplace Isn’t Enough

Imagine walking into a bustling farmers’ market. Stalls overflow with fresh produce, artisanal cheeses, and handcrafted goods. If you want to create a gourmet dish, merely having access to these diverse ingredients at the marketplace isn’t enough. You’d need a recipe, the right kitchen tools, and an understanding of cooking techniques to bring it all together.

Similarly, a data marketplace might provide an abundance of datasets and sources. In order to convert that data into actionable insights or a refined product, businesses need more than just access. They require the right tools, methodologies, and expertise to analyze, process, and integrate this data effectively.

Public data marketplaces like the Snowflake Data Marketplace and Databricks Marketplace have revolutionized data access among enterprises; however, a data marketplace is not enough to unlock the full potential of your organization’s data. The answer lies in adopting a holistic data strategy that goes beyond the marketplace and encompasses the entire data ecosystem.

The evolution of data needs and the data marketplace

In the business world, data is increasingly recognized as a valuable resource. In response, data marketplaces have emerged to connect data vendors with potential buyers, allowing them to monetize their customer data and providing businesses with a cost-efficient way to access various types of data.

How data marketplaces emerged as a solution

Data marketplaces address the growing demand for accessible and diverse data sources. They exist as a two-sided data ecosystem that helps data providers monetize data products and generate revenue. Enterprises generate revenue by providing a platform for data providers to sell their data. In turn, this platform also enables the transfer and sharing of data. In this context, marketplaces are vehicles for meaningful data sharing. It’s “a way of doing things”

Currently, it’s the most meaningful way.  

Various types of data marketplaces exist, such as:

• Public data marketplaces
• Private data marketplaces
• Partner data marketplaces
• Multi-layered data marketplaces

Each type has its own unique features and benefits. For example, B2B data marketplaces allow companies to sell data to anyone who wants to buy it—even competitors. These marketplaces feature a variety of data sets, from consumer Internet of Things (IoT) data to stock market data from decades ago.

Case study: Snowflake Data Marketplace

The Snowflake Data Marketplace exemplifies the most meaningful way to address the growing demand for accessible and diverse data sources. The platform offers both structured and unstructured data, and offers a unified platform for data sharing and collaboration. Additionally,  Snowflake allows buyers to post data requests visible to providers, ensuring that they can access the most relevant and up-to-date information. 

In addition to its extensive range of data sources, Snowflake Data Marketplace also emphasizes the importance of trust between data providers and consumers. By offering a platform that prioritizes data quality and authenticity, Snowflake ensures that users are able to access reliable and accurate data, fostering a sense of trust and confidence in the data marketplace.

The Snowflake Data Marketplace promotes data assets by:

• Providing a platform for data providers and consumers to connect and exchange data
• Offering Snowflake Secure Data Sharing, which makes data sharing more efficient and secure
• Providing customizable data cataloging, segmenting, and marketing for data assets tailored to target audiences
• Streamlining the experience for purchasing, selling, sharing, or exchanging data assets from multiple data providers

By offering these features, Snowflake sets itself apart from other data platforms in the data marketplace landscape.

The advantage of a data marketplace

Data marketplaces offer the following benefits:

• Easy access to datasets
• On-demand availability
• Collaboration opportunities
• Self-service
• Data discoverability
• Potential cost savings compared to traditional data acquisition methods

Despite the numerous benefits of data marketplaces, they also have certain limitations that hinder their effectiveness. 

Easy access to datasets

One of the main advantages of data marketplaces is the convenient access they provide to diverse datasets from multiple sources. By offering a wide range of data from various providers, data marketplaces help businesses to:

• Find the information they need quickly and efficiently
• Make informed decisions based on reliable data
• Drive innovation by exploring new datasets and uncovering insights

Enterprises that rely on their own data can benefit from having access to other datasets, which can help them to make better decisions and drive innovation.

On-demand availability

On-demand availability of data is another key advantage of data marketplaces, allowing businesses to:

• Access data without the need to wait for processing or delivery (e.g. filing an access request ticket with the data team and never hearing back)
• Make informed decisions based on the most current data
• Remain competitive in the market and make informed decisions in a timely manner
• Reduce costs associated with data acquisition and processing

Despite these advantages, there are potential limitations associated with on-demand data availability, such as the narrow scope and limited quantity of data available, as well as questions regarding data quality and authenticity. To address these limitations, enterprises should consider combining external and internal data sources. By doing so, they help to increase the scope and quantity of data available, improve the quality and authenticity of the data, and create a more holistic view of the business.

Collaboration and community-driven data curation

Collaboration and community-driven data curation enhance their value for users. While collaborative efforts are largely a feature of internal and partner marketplaces, not external marketplaces, a few data marketplaces make collaborative efforts to validate and enhance the data. 

For example, the Protein Data Bank (PDB) is a data marketplace that relies on community-driven data curation. The PDB is a database of protein structures that is curated by a community of scientists. The PDB provides a central repository for protein structures, and the community works together to ensure that the data is accurate and up-to-date.

Collaboration and community-driven data curation foster a sense of ownership and engagement among participants, creating a sustainable and evolving ecosystem for data management and usage. 

Potential cost savings compared to traditional data acquisition methods

Data marketplaces offer potential cost savings compared to traditional data acquisition methods, such as manual data collection and processing. It’s often cheaper to buy data than it is to collect, organize, and transform it. Additionally, data marketplaces can offer discounts for bulk purchases or long-term contracts.

Limitations of a data marketplace

While data marketplaces offer numerous advantages, they also have certain limitations that hinder their effectiveness. In brief, data marketplaces make it difficult to: 

• Find data products/sets that have the right balance of depth and breadth
• Verify the quality and authenticity of data products/sets
• Customize data products/sets to meet your specific needs
• Integrate data products/sets with your existing systems.
• Find data that is relevant and helpful for your needs.

Additionally, data marketplaces typically offer data access rather than curated data products. As a result, they often lack the ability to tailor or optimize data products for direct business applications. Many data marketplaces sell raw data assets and not curated data products. This is great for businesses who have the time and expertise to sift through large datasets, but not ideal for many other use cases. Without curated productization, users might face challenges integrating data, understanding data lineage, and ensuring consistent quality. 

Finally, there is the challenge of data licensing. When data is purchased from an online marketplace, it’s not always clear how data can be used and to what extent. Some data marketplaces, such as Revelate’s, make it clear how data licensing works once the data is purchased. That’s not always the case with other marketplaces.

Depth vs. breadth of data

One limitation of data marketplaces is the trade-off between depth and breadth of data available. While an emphasis on depth may lead to a lack of variety and diversity in the data available, an emphasis on breadth may lead to a lack of depth and detailed insights. Therefore, data products should be intentionally designed for usage and consumption by a specific set of data consumer personas.

Consider a data marketplace specializing in e-commerce analytics. If it focuses deeply on shoe sales, offering minute details on every transaction, it may lack broader insights on other product categories, limiting its appeal to diverse retailers. Conversely, if it provides a wide array of general e-commerce trends without deep dives, niche businesses might find the insights too superficial for their specific needs. It could make sense for both products to be sold on a marketplace, but marketed to two different consumer personas.

Data quality and accuracy

Ensuring data quality can be challenging in data marketplaces, as there may be limited means of assessing and quantifying the value of datasets. If a consumer has concerns about data quality, they may not trust the data provider or the data marketplace itself. To address these challenges, enterprises should prioritize data governance to ensure data quality and accuracy. 

By implementing robust data governance policies and processes, businesses can:

• Guarantee the accuracy, integrity, and security of their data
• Ensure compliance with data privacy regulations
• Protect their data from unauthorized access

Lack of customization

Data marketplaces are often a valuable resource for enterprises that need to access a wide variety of data. However, data marketplaces may not always offer the customization options that enterprises need for their specific business requirements. 

For example, enterprises may require data that is formatted in a specific way, meets certain quality standards, or complies with specific security regulations. If a data marketplace cannot provide the data in the format, quality, or security standards that the enterprise needs, it may not be able to meet the enterprise’s requirements. For the enterprise, a lack of relevant and tailored data may result in inefficiencies and missed opportunities.

To address this lack of customization, enterprises can:

• Combine external and internal data to create a more comprehensive data set
• Ensure that the data is relevant and tailored to the specific needs of the organization
• Invest in analytics and data science capabilities to get the most out of their data, regardless of the level of customization available in the marketplace

Integration and interoperability issues

Integration and interoperability issues frequently hinder the effective use of data from marketplaces. Data that can’t easily work within multiple systems is much less useful.

Potential solutions to integration and interoperability issues may include:

• Using APIs to bridge disparate systems
• Employing data integration tools to translate data formats
• Implementing data governance and security protocols to guarantee data privacy and security

By adopting a holistic data strategy that addresses these issues, enterprises ensure that they are maximizing the value of their data investments and making the most informed decisions possible.

Lack of Useful Products

Many enterprises appear to seek curated data tailored for immediate business applications. What they may find instead is raw data. While rich in potential, raw data often lacks the refinement and focus of a formal data product. Without pre-processed information, businesses must spend time refining the data to meet their specific needs and challenges.  

There’s an art and a science to making a data product. Know the data consumer. Package up the data they need. Then make it both easy to find and easy to buy.

Why a data fulfillment platform is the answer

While data marketplaces offer numerous advantages, they also have certain limitations that must be addressed in order to fully leverage the potential of data. Data marketplaces:

• Sell access to data, usually in raw form
• Are not built for easy data discovery or self-service
• Don’t curate or format data for any particular use case
• Don’t provide clear data licensing
• Don’t indicate clear data sources
• Can vary significantly in price

Given the inherent challenges they present, relying solely on data marketplaces constrains the potential of enterprises. It’s crucial for businesses to adopt a holistic data strategy that encompasses the entire data ecosystem, not just the marketplace. 

Revelate, as a data fulfillment platform, champions a holistic approach to data management. While Snowflake’s Data Marketplace is undeniably potent, its exclusivity poses challenges. Only those within the Snowflake ecosystem can access its data, creating accessibility barriers for many businesses.

By seamlessly integrating with Snowflake, Revelate broadens data discoverability and visibility, even to those outside Snowflake’s circle. This integration ensures that any individual or organization can quickly transfer data beyond the Snowflake environment. Revelate’s platform-agnostic nature facilitates data movement from any source to any desired endpoint as long as the receiving platform or software is compatible with the data format.

Revelate elevates data management as a cloud-driven solution, making it secure and user-friendly. Embark on your data journey with Revelate today and harness the full potential of your data assets for your organization.

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