Data Sharing Explained: Best Practices and Challenges

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data-sharing

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Imagine a busy city government with different departments in charge of transportation, healthcare, and public safety. Each department collects useful data related to its expertise, but there is no good way for these departments to share data with each other. Consequently, this valuable data remains inaccessible to the city’s planning department, crippling the city’s ability to make good decisions and improve public services. 

Data providers experience the same roadblocks when sharing their data with consumers. They work in a fragmented environment where valuable information is frequently kept in inaccessible silos. Data inaccessibility diminishes the value a business can offer consumers, discourages collaboration, and hinders crucial insights into consumer behavior and market trends.

What is data sharing?

Data sharing entails making the same data resource, such as a data warehouse, available to multiple individuals, systems, applications, or organizations. Enterprises with the right tools can share their data regardless of format, platform, or geographic location. This article explores the benefits of data sharing, best practices, and challenges, then provides strategies for overcoming those challenges.

Benefits of data sharing

Enterprises that recognize the benefits of data sharing enable them to stay competitive, drive growth, and make data-driven decisions.These enterprises will achieve greater collaboration, increased efficiency, enhanced innovation, and improved data quality.

Greater collaboration

Data sharing can significantly enhance the exchange of information, insights, and resources between companies. When companies share relevant data, they gain a deeper understanding of shared challenges, market trends, and customer preferences. This enables companies to collaborate more effectively on joint projects, research, and development initiatives. 

Consider the pharmaceutical company Pfizer and its collaboration with BioNTech when developing the COVID-19 vaccine. Their ability to share research and clinical trial data led them to better understand the vaccine’s effectiveness. This collaborative data sharing enabled them to make better decisions and accelerate vaccine development.

Increased efficiency

Sharing relevant data eliminates the need for redundant or repetitive data collection efforts. By embarking on data-sharing initiatives, enterprises reduce the time and resources spent gathering and verifying data from scratch.

Credit bureaus leverage data sharing capabilities to centrally collect, store, and disseminate credit data. They allow financial institutions to access and share credit data, so individual banks so not need to independently collect and verify extensive credit information.

Data sharing provides alternative expertise and perspectives, leading to more innovative solutions. The Human Genome Project, launched in 1990, is an excellent example. By openly sharing genomic data, scientists from various disciplines and organizations could access a wealth of genetic information, resulting in groundbreaking discoveries.

Enhanced innovation

Gaining access to new data enables companies to identify new opportunities and solutions. Companies that pool their data resources are more likely to gain comprehensive insights, make better-informed decisions, and collectively address complex problems.

When Apple and IBM decided to collaborate and exchange customer data and combine datasets, they identified unmet customer needs in the healthcare industry. The insights they gathered helped them develop several innovative applications geared toward nurses and hospital technicians.

Improved data quality

When enterprises share their data, they also cross-validate that data. When different sources verify and validate the accuracy and consistency of data, they reduce the chances of errors and omissions. Enterprises that share data also enrich their own datasets with additional information from external sources. This type of initiative also allows collaborators to provide feedback and corrections. 

The OpenStreetMap (OSM) project is a prime example of how data sharing can improve data quality. Individuals and enterprises can contribute geographic data to improve the map’s accuracy and completeness. Contributors to this collaborative effort can also enrich the data by accessing and integrating data from other sources.

Data sharing use cases

Data sharing also extends to the public sector. Airbnb recently shared anonymized host data with New York’s city administration to help address concerns about illegal short-term rentals. This collaboration allowed the city to analyze the data and identify potential violations of rental regulations more efficiently. 

More famously, NASA and SpaceX entered into a collaboration agreement. SpaceX shared its proprietary data and expertise in space transportation systems with NASA. In return, NASA provided access to its extensive knowledge, research, and data on space exploration.

 

Best practices of data sharing

Guidance is scarce on specific data sharing best practices. However, our best practices can serve as a starting point. 

Security: 

Implement robust security measures to prevent unauthorized access, breaches, or misuse of shared data. When sharing sensitive information, abide by privacy laws and consider anonymization (removing personally identifiable information) as a proactive measure. If the data pertains to research, consider de-identification (modifying data to reduce the likelihood of identification) to ensure privacy.  

Documentation and Metadata: 

Keep detailed records and metadata about shared data, including information about source, structure, meaning, and limitations. This approach will improve the ability to use and understand the shared data.

Standardization and Interoperability: 

Encourage interoperability and seamlessly integrate shared data across organizations by adopting common data standards, formats, and protocols. This practice will make data exchange and compatibility more efficient by establishing consistent data representation and transmission rules.

Data Quality: 

Ensure data is accurate, complete, and reliable. Data validation processes will reduce errors and inconsistencies and improve the overall data quality. Validation involves automated checks and rules to verify data integrity, such as validating data types and ranges.

Data Governance: 

Create explicit data governance policies and procedures that specify roles, responsibilities, and rules for data sharing to ensure adherence to ethical standards, data security laws, and privacy laws.

Data sharing challenges and risks

Enterprises that recognize the challenges and risks of data sharing can take proactive measures to solidify their reputation and trust with customers. Here are some common challenges and risks associated with data sharing:

Common challenges and risks:

Security: 

Data sharing requires close attention to privacy and security risks. Enterprises must guard against unauthorized access and the possibility of data breaches, identity theft, and misuse of sensitive information. 

There is also the risk that shared data may unintentionally identify individuals or re-identify anonymized data, compromising privacy. This type of misuse famously occurred in 2018 when the media revealed that a British political consulting firm named Cambridge Analytica obtained personal data from millions of Facebook users without their consent.

Inconsistency:

Data sharing can introduce the risk of inconsistent data quality, potentially leading to flawed analyses and inaccurate decision-making. This inconsistency can hamper operational efficiency internally by causing confusion, errors, and process delays. 

Inconsistent data may uncover inaccurate customer information like contact details and purchase history. This can undermine effective communication and diminish customer satisfaction if prolific enough and will erode trust and credibility over the long run.

Data governance

When enterprises share their data with multiple partners, maintaining quality and integrity becomes more difficult. Differences in data formats, definitions, and standards can occur, resulting in discrepancies that impede effective data governance.  

Additionally, collaborating enterprises must frequently align their data governance practices to maintain consistent standards for shared datasets.

Technical challenges: 

Enterprises seeking to integrate and consolidate shared data may find it difficult and time-consuming due to the data quality of various sources. Data standards and formats can vary greatly depending on the source.   

The U.S. government’s push to implement Electronic Health Records (EHR) standards exemplifies this issue. The implementation process faced significant challenges due to healthcare providers’ inability to harmonize systems and data formats.

Legal & regulatory compliance: 

Organizations must consider legal obligations and intellectual property rights when sharing data with outside parties. More specifically, they must participate in clear data sharing agreements and contracts that specify data ownership, permitted uses, and security measures to prevent legal disputes and confidentiality violations.

These issues can become highly complex due to varying international regulations and data transfer restrictions when cross-border data sharing is involved. In 2016, the E.U./U.S. Privacy Shield Framework case saw the European Court of Justice invalidate the Safe Harbor Framework. It aimed to address data protection concerns in transatlantic data transfers. 

The court’s ruling highlights the need for enterprises involved in data sharing to consider data sovereignty laws. Failure to heed such rules can result in legal consequences and reputational damage. 

Overcoming data sharing challenges and risks

Security:

Enterprises generally employ layered protection of encryption and access when sharing data. Encryption protects data confidentiality by preventing unauthorized parties from gaining access to the data. Access controls complement encryption by ensuring that only authorized individuals or organizations can access the encrypted data. 

Data Quality:

Enterprises use data profiling and cleansing tools to ensure data quality. Data profiling involves examining data’s content, structure, and quality to identify whether it exhibits any anomalies or inconsistencies. These tools help organizations determine a dataset’s reliability.

Netflix famously developed a data profiling tool named Chaos Monkey to intentionally introduce failures and disruptions in their distributed computing systems. The tool helped them identify vulnerabilities in their infrastructure, allowing them to address potential service reliability issues.

Data cleansing tools to automatically detect and correct problems with data quality. Theyrectify errors, inconsistencies, duplicates, and other data anomalies. Cleansing deduplicates, standardizes, and validates data to improve its accuracy and integrity. 

Data Governance:

Transparent data governance controls are crucial for any enterprise seeking to overcome data sharing challenges. Before data sharing begins, enterprises should create comprehensive data sharing policies that expressly define the rules, processes, and obligations. 

These policies should lay out the process for three major areas:

 

  • Reviewing the roles and responsibilities of data management professionals 
  • Using access controls and permissions to regulate who can access and share data 
  • Employing data profiling and data cleansing to improve the quality and consistency of shared data 

 

While implementing such oversight is instrumental when mitigating conflicts, as the aforementioned Human Genome Project exemplifies. 

How Revelate enables data sharing

Data marketplaces effectively break down data silos and enable seamless data delivery to different organizations or parts of an organization. This process eliminates the back-and-forth typical of most data fulfillment requests. Employees no longer have to wait for a work ticket and gain managerial approval. An efficient internal marketplace strategy can even automate all data request documentation, support, and chargebacks.

Revelate is a self-service data fulfillment platform that streamlines and consolidates all data sharing and fulfillment processes within a unified environment. Revelate’s data web store allows marketplace operators to establish their own marketplace experience for monetizing or distributing data share products. Revelate can be integrated with data sharing platforms like Snowflake and Databricks enabling the seamless sharing of data across multiple platforms.  

A new paradigm

Enterprises increasingly recognize the importance of data sharing in optimizing their operations, making informed decisions, and achieving overall organizational success. Until recently, however, their ability to share data has been somewhat limited due to technological constraints. 

The emergence of data marketplaces has opened exciting new avenues for data sharing. These marketplaces are a driving force for greater collaboration, innovation, and value creation. They attract organizations intent on monetizing and distributing their data assets and ensure a seamless data exchange.

We can expect even more remarkable data sharing opportunities as the data marketplace ecosystem evolves. The emergence of such marketplaces enables businesses to tap into the community’s collective intelligence and shape a future based on data-driven insights.

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