Sharing Data Outside of Your Organization

Why Share Data Externally?

Sharing data improves organizational performance. Data is knowledge, and knowledge is power; sharing it with others empowers them as well. Symbiotic relationships benefit all parties, and sharing your data with partners strengthens your organization and your partners if done correctly.

New opportunities can be created by sharing data, but making data available externally has unique implications compared to providing access only within your organization. Typically, sharing data internally means exchanging data across departments or groups, such as sharing sales data with marketing and vice versa. Sharing data externally usually means trading data with customers, suppliers, regulators, or partners. When data moves outside of the organization, the stakes increase, and potential risks and rewards are multiplied.

While sharing data with external entities presents governance and security challenges, it also offers many opportunities. According to the results of the latest Gartner Chief Data Officer Survey, “data and analytics leaders who share data externally generate three times more measurable economic benefit than those who do not.”

Sharing data with partners, customers, and suppliers across an industry or ecosystem can lead to industry-wide efficiencies. Sharing data about how and when products move through your organization can help streamline the entire supply chain, reducing costs for all and improving end-customer experiences.

Exchanging data with partners also enables each participant to enhance the value of current data in their data stores. Combining data from multiple sources that originate outside an organization provides broader perspectives and richer insights into market trends or customer preferences. This is just one of an infinite number of opportunities to enrich data. Sharing market data with third parties can also attract new partners, and new go-to-market opportunities can emerge. In that same respect, sharing data can also be a revenue source. Third-party players may be willing to pay for access to the data that you are capturing, resulting in a profitable new line of business.

Use Cases

Supply Chain Efficiency

Improved velocity drives down costs in supply chains. The faster a product can move from raw materials to manufactured products to distribution to retail to end customers, the faster companies get paid. This increased cash flow creates new opportunities for investment. When retailers share data with suppliers, they can more effectively get products to market that best-fit customer needs. With consumer trends changing so rapidly, quick insights into what consumers are buying and not buying can help suppliers get products to retailers while demand is still strong. This improves revenues and margins. Some large organizations have invested significant resources into automating data exchange across the supply chain, but these systems are very rigid and expensive.

Fraud Prevention

Competitors can also share data. Competing banks, for example, can share fraud data. Bank fraud drives up insurance costs that impact risks for every bank. Sharing ways to mitigate fraud will help reduce costs for every market participant. To be effective, data must be shared in real-time to preempt criminals before they can cause too much damage. Sharing market data with competitors can benefit the entire sector by driving demand for a whole category of products, expanding the entire market.

R&D

Sharing data to support research can lead to innovations that support better performance and consumer outcomes for an entire industry. For example, pharmaceutical companies, MedTech, and healthcare providers can share data to help researchers develop better clinical practices.

AI Model Improvements

AI models thrive on diverse data sets. More of the same or similar data will not improve the performance of an AI model. Diverse data sets provide AI models with more context and a better world understanding. If AI relies too much on homogeneous data, it is more likely to be biased or have hallucinations. In many cases, the data sets needed to provide the required diversity are not available internally within a single organization. Exchanging your data with your partners to diversify AI training data can have an immense impact on your AI strategy and model performance.

Challenges With External Data Sharing

Data Control

Losing control of your data presents a significant risk when sharing data outside your organization. Once it leaves your organization, controlling access and usage becomes difficult. Fine-tuning access rules to make data more accessible to the appropriate users while preserving privacy and security is not feasible. Hence, establishing well-defined policies to manage acceptable users and mitigate this risk is essential.

Privacy Regulations

Violating privacy regulations can result in hefty fines and tarnished reputations. Enforcing privacy and security concerns before data is shared is vital to avoiding costly breaches. Making the mistake of sharing sensitive competitive information with competitors can result in losing your competitive advantage.

Manual Data Sharing

In many cases, to maintain control or reduce the costs of creating an automated system, data is shared manually. Either data is shared in an email, within a spreadsheet, or a shared cloud file. Sharing data manually can be slow with a high potential for errors. Without the appropriate controls, there is also the risk that sensitive data will be inappropriately shared outside the organization.

Partner APIs

Data can also be made available via an API where partners can call a REST API from the sharing entity. This approach is typically used to publish raw data and is accessible by anyone with authorization. Technically, they are quite easy to access using web technology, but integration can be difficult due to different data formats. APIs can also be more vulnerable to hackers and therefore require appropriate maintenance and documentation.

Data Lineage

No matter how data is shared, typically data lineage is not included, so users may not know the history of the data and where it originated from. Users will not understand how trustworthy it is because they do not know how that data was collected. This lack of transparency can lead decision-makers to lose trust in the data, limiting its value.

Consuming Data Cross-Ecosystem

Automating data-sharing processes reduces the risk of errors inherent in any manual process, but automating data-sharing between organizations can be overly complex. Differences in data models from one organization to another can make it difficult to process data effectively. For example, one organization may calculate one metric differently from another, providing different information. How markets are segmented is also usually different across unique organizations, causing confusion. In some industries, there are efforts to standardize data models, but the results of these efforts are mixed.

Sharing data within an organization across departments and systems is complex enough. Once you add a whole new set of technologies, processes, and policies from third parties, managing these variables becomes exponentially more difficult. Organizations will have different technology stacks, data governance, data quality policies, and strategies. This complexity requires significant work to share data automatically between entities.

Data must be mapped between systems onto one another, accounting for each individual data model, data policies, and security protocols. Building and maintaining custom transformation and automation processes is also required to tackle this complexity. Programmers must understand the data models of each organization, the technology stack, and how databases are organized. They also need to be able to write code in the correct programming language to pull data from data sources. Making any changes to how data is shared and with whom requires specialized skills and knowledge, which adds additional barriers to data sharing.

Cultural nuances across organizations also need to be navigated. Data quality in some organizations may be a bigger part of corporate culture than in others. Terminology and metrics variances also lead to confusion. This disparity can lead to conflict and a lack of trust.

If organizations can navigate the complexity of effectively sharing data, the return on investment could be limited due to a lack of awareness. Without a way to inform potential users that data is available and a way to authorize access, the full potential of your investment will not be realized. While sharing data outside your organization has many risks and challenges, the benefit is great. To be more effective, a shift in mindset is needed.

Instead of dumping data onto partners or building complex integrations, a data product mindset shifts the focus to delivering value, not just data. Providing data with limited quality checks or governance does not provide optimal value to the ecosystem. Building data products that are designed to deliver greater utility when shared presents a different approach. These data products are targeted at certain business outcomes, merge data from multiple sources for greater insights, and deliver secure, high-quality data. Organizations need to think less about sharing or controlling data and more about stewarding and enhancing it to benefit the industry.

Sharing Data With Data Products

When you think about sharing data as a data product, you begin by considering the needs of your users. How can data be enhanced, and how can multiple data sets be combined to provide the greatest amount of value to the highest number of users? How can datasets be filtered and curated to support a certain outcome and actionable insights? This approach provides much more value than simply loading raw data into a partner’s data lake. Instead of investing all your resources into a single integration for one partner, make enriched data sets available to all your partners.

Data products allow you to package data from multiple sources with data quality and governance into a single API. This process abstracts data sets from the complexity of the numerous databases and the protocols and formats of the underlying data. Partners do not need to understand the underlying databases and data stacks or guess about data quality.

When data products are packaged with built-in governance controls, the guidelines for sharing can be more effectively controlled. For successful data sharing, establishing terms for how data can be used is essential. With data products, entities can subscribe to data products on agreed terms of use. With the ability to dictate access rules, domain managers can also define terms of use. This enables much greater flexibility in sharing data because domain managers, who better understand the value and risk of sharing data, have more authority.

When data product platforms leverage virtualization, they retain much more control of the data. In this type of system, data is pulled into a virtual environment and then shared with third parties. Data is not directly transferred to partners, and every time data is refreshed, a new data set is created. This provides the sharing entity with much more control of the data. This technology can also support authorization for data access at the column level and attribute-based access controls so only people or systems with certain attributes can access data.

Data products are also easy to distribute through a data product marketplace. Listing available data products in a central marketplace enables partners to review available data products and request access. Marketplaces can also allow user feedback and quality ratings. By capturing this feedback and making it available to other users, partners can more easily find the most popular data products. Data lineage, documentation, and business glossaries can help users better understand the origins of data, how to use it correctly, and what the data represents.

The more data is available and usable, the more valuable it becomes. In the future, as data becomes more important, working with partners to exchange data will be a must-have for successful partnerships. Data products provide a solid platform to support secure sharing of quality data.

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