Data Sharing

Data sharing

Data sharing is the practice of making data resources available between entities, such as organizations, applications, or individuals, while preserving data integrity. Sharing data facilitates collaboration and information exchange between different parties, enabling them to access and utilize data for various purposes.

Benefits of data sharing

Data sharing offers numerous benefits, including enhanced collaboration, innovation, efficiency, and decision-making.

  • Sharing data enables new business opportunities and the evolution of the data-driven business model.
  • Data sharing allows collaboration with partners and the establishment of new partnerships.
  • Data sharing can also generate new revenue streams through data monetization.
  • Sharing data eliminates data silos, leading to greater efficiency, transparency, and collaboration within an organization and with partners.
  • Data sharing provides organizations with faster time to insights, improving performance.

Types of data sharing

Data could be shared in a variety ways. Organizations often choose one or more of the below methods depending on their specific needs.

  • Sharing within an organization
  • Sharing with customers, suppliers, and partners
  • Sharing with individuals
  • Public sharing
  • Private sharing

Data sharing challenges

While sharing data offers many untapped opportunities, it also poses many challenges, such as ensuring data security, privacy concerns, regulatory compliance, and establishing trust between parties.

Data security

One of the primary challenges in data sharing is ensuring that security is prioritized to protect the shared data from unauthorized access, breaches, or misuse. It is important to share the right amount of data with the appropriate audience in a secure manner.

Privacy and Compliance

Ensuring the privacy of shared data is crucial, particularly when it involves sensitive or personal information. Ethical considerations are of utmost importance in the process of data sharing, including respect for data ownership, consent, and adherence to regulations and standards.

Data interoperability issues

When shared data is transferred to a different environment, it may need to undergo additional processing. This presents a challenge as it requires the implementation of additional Extract, Transform, Load (ETL) components on the receiving end. This can result in increased complexity and delays in effectively utilizing the shared data.

Cost of data sharing

Setting up data in a data sharing platform typically involves the need for additional processing and storage. This, in turn, may require the allocation of extra resources, leading to the challenge of increased costs.

Data sharing techniques

Data-sharing solutions could be broadly categorized into three formats: legacy and homegrown (custom-built) solutions, proprietary vendor solutions, and sharing data using modern cloud storage. Each of these approaches has its pros and cons.

Traditional methods

Organizations often use traditional methods such as SFTP, Email, or custom APIs for sharing data. Although these methods use well-defined protocols for data exachange, there are several challenges associated to these methods:

  • Custom-built solutions for data sharing often involve complex architectures due to replication and provisioning. This complexity adds time to data-sharing activities and can result in outdated data for end consumers.
  • Securing and governing homegrown and legacy technologies becomes increasingly challenging as modern data requirements become more stringent. This poses a risk to data security and compliance.
  • Also, managing and maintaining these solutions is costly, and they lack the scalability to accommodate large datasets.

Proprietary vendor solutions

There are several commericial data-sharing solutions available for sharing data. These solutions allow users to easily share data with others who use the same platform. However, there are limitations to these solutions. Users cannot share data with users of competing solutions, and scalability is often limited by the vendors. Additionally, data must be loaded onto the platform, requiring extract, transform, and load (ETL) processes and creating data copies. These restrictions result in complexity, version control problems, and higher costs when sharing data with recipients on different cloud platforms.

Delta sharing

Delta Sharing utilizes an organization’s existing cloud object storage and enhances them with ACID compliance. It is an open-source protocol designed to enable secure sharing of large datasets stored in Delta Lake. With Delta Sharing, data consumers can directly access the shared data using their preferred toolsets, including Spark, Rust, Power BI, and more, without the need for extra components. Importantly, this protocol facilitates seamless data sharing across various cloud providers, eliminating the need for custom development.

Conclusion

In today’s data-driven world, the need for extensive data sharing within organizations and with customers has become crucial. However, traditional methods and proprietary vendor solutions for data sharing may not always yield the desired business outcomes. To achieve efficiency and ensure immediate data availability, organizations should explore open-source methods like Delta Sharing for data sharing.