Data Governance Frameworks for Sharing and Governing Data Efficiently

By:   |   Updated: 2023-02-21   |   Comments   |   Related: > Cloud Strategy


Problem

Sharing and governing data efficiently is a common challenge organizations face in today's data-driven world. As organizations generate and collect more data from a variety of sources, it becomes increasingly difficult to share and govern this data in a way that is consistent, transparent, and compliant with relevant regulations and standards. This can lead to several problems, including:

  • Inefficient data sharing: When data is not shared efficiently, it can be difficult for organizations to make the most of their data assets. This can lead to silos of data that are not easily accessible or understandable to non-technical users, reducing the value organizations can derive from their data.
  • Poor data quality: When data is not governed effectively, it can be difficult to ensure that it is accurate, complete, and up-to-date. This can lead to poor data quality, which can undermine the credibility and reliability of the data and make it difficult for organizations to make informed decisions.
  • Lack of transparency: Without effective data governance, it can be difficult for organizations to ensure that data is being used appropriately and in compliance with relevant regulations and standards. This can lead to a lack of transparency around data usage, undermining trust and credibility with stakeholders.

Overall, the problem of sharing and governing data efficiently is a significant challenge for organizations looking to derive value from their data assets and drive insights and decision-making throughout the organization. It is critical for organizations to find ways to share and govern data in a way that is consistent, transparent, and compliant with relevant regulations and standards to make the most of their data assets and drive insights and decision-making throughout the organization.

Solution

In today's data-driven world, organizations must be able to share and govern data efficiently. Data sharing practices and data governance frameworks play a key role in ensuring that data is shared and governed in a way that is consistent, transparent, and compliant with relevant regulations and standards. Data governance frameworks provide a set of principles, policies, and procedures for managing data within an organization. They help ensure that data is used appropriately, kept secure, and up-to-date. Data governance frameworks can also help ensure that data is shared transparently and consistently, enabling organizations to use their data assets better and drive insights and decision-making.

Data sharing practices are the policies and procedures organizations follow when sharing data with external parties. These practices can help to ensure that data is shared in a way that is consistent, transparent, and compliant with relevant regulations and standards. By adopting effective data sharing practices, organizations can help to build trust and credibility with their stakeholders, enabling them to derive more value from their data assets and drive insights and decision-making throughout the organization.

In this article, we will explore the importance of data governance frameworks and data sharing practices in enabling organizations to share and govern data efficiently. We will discuss the key principles and practices organizations should consider when designing and implementing data governance frameworks and sharing practices. We will also look at some examples of how these frameworks and practices have been implemented in different industries and contexts. Overall, this article aims to help organizations understand the importance of data governance and data sharing and to provide them with the knowledge and tools they need to share and govern data efficiently and effectively.

Data Governance Frameworks

Data governance frameworks and practices are key enablers of democratizing data analytics and business intelligence (BI) on the cloud. These frameworks and practices help organizations to establish clear policies and processes for managing and sharing data on cloud platforms, ensuring that data is accurate, consistent, and secure. This enables a wider range of users to access and work with data, improving data-driven decision making and fostering a culture of data-driven innovation.

Cloud data governance frameworks are sets of principles, policies, and procedures for managing data within an organization in the cloud. These frameworks are designed to ensure that data is used appropriately, kept secure, and kept up-to-date, enabling organizations to derive more value from their data assets and drive insights and decision-making. Some examples of cloud data governance frameworks include:

  • Data governance policies: Establishing policies and procedures to define how data is used, managed, and shared within the organization.
  • Data governance roles and responsibilities: Assigning roles and responsibilities for data governance within the organization, including data stewards, owners, and users.
  • Data governance processes: Establishing processes for data governance activities such as data quality management, data protection, and data security.
  • Data governance tools: Using tools and technologies to support data governance activities, such as data catalogs, data dictionaries, and data lineage tools.
  • Data governance training and education: Providing training and education to data governance stakeholders to ensure that they are aware of their roles and responsibilities and understand how to use data in a way that is consistent with the organization's data governance policies.

By adopting these and other elements of a cloud data governance framework, organizations can ensure that their data is used appropriately, kept secure, and updated, enabling them to derive more value from their data assets and drive insights and decision-making throughout the organization.

One cloud technology that can support data governance frameworks and practices is data catalogs, such as AWS Glue Catalog or Azure Data Catalog. Data catalogs provide a centralized repository of metadata about data assets, including descriptions, definitions, and lineage information. This can help organizations better understand and manage their data assets and facilitate more widespread access and use of data.

Another cloud technology that can support data governance frameworks is data lineage and data governance tools, such as AWS Lake Formation or Azure Purview. These tools provide visibility into the flow of data within an organization and help them track and manage data assets throughout its lifecycle. This can help organizations ensure data quality, compliance, and security and facilitate more widespread access and use of data.

Cloud-based data and analytics platforms, such as Azure Synapse, Google Cloud BigQuery, and Amazon Athena, can also support data governance frameworks and practices. These platforms provide a range of tools and services for accessing, storing, and analyzing data and often include features such as data catalogs, visualization tools, and machine learning capabilities to make data more accessible and understandable. By adopting cloud-based platforms, organizations can enable a wider range of users to access and work with data.

Overall, data governance frameworks and practices are essential to enable organizations to democratize their data analytics and BI efforts on the cloud. By establishing clear policies and processes for managing and sharing data on cloud platforms, organizations can enable a wider range of users to access and work with data, driving better insights and outcomes.

Data Sharing Practices

Data sharing refers to the ability to access and use data from multiple systems or platforms. Data sharing can simplify the process by allowing users to access and work with data from multiple sources without the need for data movement. Here is an example of how data sharing can simplify ELT: suppose an organization has data stored in multiple systems, such as a database, a file system, and a cloud storage service. They want to build an ELT pipeline that combines and processes this data for analysis. Without data sharing, they would need to extract the data from each system and load it into a central repository for processing. This can be time-consuming and require additional storage capacity. With data sharing, they can simply access the data from each system as needed, without the need for data movement. This simplifies the ELT process and allows for faster, more flexible analysis.

Data sharing is supported by many modern cloud platforms, such as Databricks, as a way to improve the efficiency and speed of ELT processes. It can also be useful for other purposes, such as enabling collaboration and sharing data across different teams or organizations. In the modern business world, it's often necessary for organizations to share data with partners, clients, and other stakeholders. However, sharing data can be challenging, especially when maintaining security and control over the data.

Some examples of cloud data sharing practices include:

  • Data access controls: Setting up access controls to regulate who can access data and how they can access it to ensure that data is only shared with authorized parties.
  • Data encryption: Encrypting data to ensure that it is protected and secure when shared with external parties.
  • Data masking: Masking sensitive data, such as personal information or trade secrets, to protect it when it is shared with external parties.
  • Data classification: Classifying data based on its sensitivity and criticality to ensure that it is shared appropriately with the right level of security and protection.
  • Data governance policies: Establishing data governance policies and procedures to ensure that data is shared in a way that is consistent with relevant regulations and standards.
  • Data sharing agreements: Establishing agreements with external parties to define the terms and conditions under which data will be shared, including any restrictions or limitations on the use of the data.

By adopting these and other cloud data sharing practices, organizations can ensure that their data is shared consistently, transparently, and compliant with relevant regulations and standards, enabling them to derive more value from their data assets and drive insights and decision-making throughout the organization.

Data Sharing Options

There are several options for sharing data in the cloud, which can help organizations overcome these challenges and collaborate more effectively.

Clean Rooms

A clean room is a secure environment where data can be shared and analyzed without the risk of unauthorized access or data leaks. Clean rooms are best suited for industries such as healthcare or finance, where data privacy is of the utmost importance. Many major cloud providers such as AWS, Databricks, and Snowflake offer clean rooms to help their customers and partners securely match, analyze, and collaborate on combined datasets without needing to share or reveal the underlying data.

Marketplaces

Data marketplaces are platforms that allow organizations to buy and sell data. These marketplaces can be useful for organizations needing access to specific data sets or monetizing their data by selling it to other users. AWS, Azure, Snowflake, and Databricks are cloud providers that offer data sharing marketplaces. The following chart lists some of these features across the major Cloud Marketplaces.

Feature AWS Data Exchange Azure Data Share Snowflake Marketplace Databricks Delta Sharing
Data Sharing
Data Publishing
Data Subscribing
Delta Table Format Support      
CSV, Parquet, ORC, & Avro Data Format Support
Data Governance ✔AWS Glue and AWS Lake Formation ✔Azure Policy and Azure Data Catalog ✔Support through Snowflake's ability to track data lineage, and the ability to restrict access to data at the individual column level. ✔Support through Databricks’ capabilities like data versioning, data auditing and compliance.
Pay-as-you-go pricing based on usage and data transfer costs.
Pay-as-you-go pricing based on data storage costs.    

Blockchain

Blockchain is a distributed ledger technology that allows users to securely store and share data in a decentralized manner. Blockchain can be useful for sharing data when trust is an issue, such as in supply chain management or real estate transactions. Below are examples of cloud blockchain technologies that can be used for data sharing.

Feature Amazon Managed Blockchain Azure Blockchain Google Cloud Anthos
Supports Hyperledger Fabric  
Supports Quorum Networks  
Supports Ethereum
Supports pre-configured Kubernetes Clusters
Offers Managed Network creation
Peer node management   ✔Through Kubernetes
Certificate authority management  
Supports multiple consensus algorithm ✔Kafka-based RAFT and Solo. ✔Raft and Istanbul BFT. ✔use of open-source blockchain frameworks.
Supports horizontal scalability through the addition of peer nodes.
Uses native security features to secure the network ✔Uses AWS security features like Amazon VPC, IAM policies, and KMS to secure the network. ✔Uses Azure security features like Azure Active Directory and Virtual Networks to secure the network. ✔Uses Google Cloud security features like Kubernetes RBAC and Google Cloud IAM to secure the network.
Integrations ✔Integrates with other AWS services like Amazon S3, Amazon DynamoDB, and AWS Lambda. ✔Integrates with Azure services like Azure Event Grid and Azure Key Vault. ✔Integrates with Google Cloud services like Google BigQuery and Google Cloud Storage.
Pay-as-you-go pricing based on usage, network size, and data transfer costs.

Distributed Ledger

A distributed ledger is a database that enables secure, transparent, and tamper-evident record-keeping of transactions among multiple parties. Many cloud-based ledger technologies provide tools and services that allow organizations to build, deploy, and manage distributed ledger-based data sharing solutions in the cloud. Below are examples of cloud-based ledger technologies for data sharing.

Feature Amazon Quantum Ledger Database (QLDB) Azure Ledger Google Cloud Bigtable
Document-based data model with rich data types
Column-family based NoSQL data model.
Supports multiple data models including key-value and graph
Provides an immutable ledger of changes to the database
Supports strongly-consistent reads and writes ✔allows for transactional processing
Supports eventually-consistent reads and writes,   ✔allows for lower latency but weaker consistency guarantees.
Supports read and write scalability using horizontal scaling
Supports read and write scalability using partitioning
Supports read and write scalability using read replicas
Integrates with other Cloud native services ✔Amazon S3, Amazon DynamoDB, and AWS Lambda. ✔Azure Event Grid and Azure Key Vault. ✔Google Cloud Storage and Google Cloud Functions.
Pay-as-you-go pricing based on usage, data storage, and data transfer costs

Summary

Sharing and governing data efficiently is critical for organizations looking to derive value from their data assets and drive insights and decision-making. By adopting effective data governance frameworks and data sharing practices, organizations can share and govern data in a way that is consistent, transparent, and compliant with relevant regulations and standards. Some key strategies for sharing and governing data efficiently include implementing data governance frameworks, adopting data sharing practices, and using tools and technologies to support data governance activities. By following these strategies and adopting best practices, organizations can ensure that they can share and govern data efficiently and effectively, enabling them to derive more value from their data assets and drive insights and decision-making throughout the organization.

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About the author
MSSQLTips author Ron L'Esteve Ron L'Esteve is a trusted information technology thought leader and professional Author residing in Illinois. He brings over 20 years of IT experience and is well-known for his impactful books and article publications on Data & AI Architecture, Engineering, and Cloud Leadership. Ron completed his Masterís in Business Administration and Finance from Loyola University in Chicago. Ron brings deep tec

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Article Last Updated: 2023-02-21

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