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What is data governance?
Data governance is a structured, organizational approach to managing, organizing & controlling data assets and includes compliance, stewardship & data security.
- Overview
- Why is data governance important?
- What are some key components of data governance?
- How to implement data governance
- Data governance and Snowflake
- Customers
- Data governance resources
Overview
Data governance is a structured approach to managing, organizing and controlling data assets within an organization. This includes establishing guidelines and procedures to ensure data quality, security and compliance. Implementing a strong data governance strategy allows businesses to streamline their data management processes, make more informed decisions, improve efficiency and ultimately get the most value from their data.
Designing such a framework requires thoughtful planning and cross-team collaboration. Stakeholders must consider roles and responsibilities and develop appropriate data policies that cultivate a data-centric business culture, as well as assess the right technologies to help do the job. Ideal governance ensures the integrity and safety of sensitive and non-sensitive data and compliance with internal audits and regulatory laws around data residency and privacy, which makes it a high stakes strategy. Most businesses opt to implement data governance on a centralized platform rather than building a system themselves, as bespoke attempts often create silos and loopholes that bad actors can target and compromise. It’s a best practice to implement a platform that offers comprehensive data governance capabilities, empowering organizations to centrally manage data while ensuring data quality, security and compliance.
Understanding Data Governance
Organizations deal with enormous volumes of data every day. They’re creating it, sharing it, storing it — and data governance is crucial for keeping that data safe while also ensuring its quality, security and compliance.
Key data governance principles include:
- Accountability: Assign clear roles and responsibilities for data management so that it’s clear who is accountable for data quality, security and compliance.
- Transparency: Create clear rules for who can access which data, when and from where, and account for that with clear logs, timelines and data lineage.
- Compliance: Privacy and data residency regulatory standards such as GDPR, CCPA, PCI, HIPAA, EU AI Act, DPDP and PIPL are constantly changing and often differ dramatically from state to state and country to country. Many organizations also have internal compliance reviews and audits. It’s critical to have built-in governance capabilities to help meet these standards.
- Data security: Breaches or other unauthorized access can put your customers’ and your company’s data at risk. It’s critical to establish measures to make sure proprietary company information and customer data is safe if a bad actor gains access.
- Data stewardship: Data stewards are custodians, making sure data is accurate, consistent and reliable. Identifying people to serve as data stewards helps establish proper processes for handling, classifying and protecting data assets.
- Continuous improvement: Innovation — and a business’s needs — can move at breakneck pace. The ability to adapt quickly to new technologies or industry landscapes can help give your organization a competitive advantage.
Data governance versus data management
Data governance provides the strategic framework and oversight for data management, which includes operational activities such as data collection, storage, organization and analysis. Governance aligns to your enterprise’s security, privacy and team management objectives and allows you to centrally protect data with role-based access controls, fine-grained authorization and custom policies that fit your organization’s unique needs.
Why is data governance important?
No matter how big or small your organization is, data governance is essential for your data management strategy. Without it, being able to secure, use and access reliable data is more difficult. Breaches are inevitable – what’s important is that no sensitive data is available to a bad actor who tries to access your systems. Implementing effective data governance brings several benefits to businesses:
- More trustworthy data: With a data governance framework in place that sets standards for data entry, validation and maintenance, fewer instances of incorrect or inconsistent data arise. This can result in higher data quality, more accurate reporting and more efficient operations.
- Compliance and risk management: Documented data governance policies are critical for compliance as regulations increase in number and complexity and differ depending on geographical area. Meeting external compliance standards such as GDPR, CCPA, DPDP, PIPL or DORA or successfully performing internal reviews and audits all require a system to ensure data is governed with care.
- Better decision-making: Both of the above can lead to this very important benefit. When data is governed properly, leaders can trust that they’re using reliable information to make decisions, conduct analysis and report metrics to key stakeholders. They can also rest easily knowing that customer data — and customer trust — isn’t at risk.
What are some key components of data governance?
Data integrity and quality management
Throughout its lifecycle, data must be reliable, accurate and consistent. This means it’s important to protect data from unauthorized changes or data duplication, corruption or drift issues. Quality management focuses on keeping data accurate and complete. If data keeps changing because the wrong users adjust it, or if data is being moved frequently between types and locations, producing many versions, the risk of introducing errors rises.
Data security and privacy
Threat actors can present serious risks for our ultra-digitized world. Preventing misuse and unauthorized access to data through measures like encryption, masking, and firewalls protects your customers’ and your organization’s data. Privacy entails controlling how data is collected, shared and used in order to respect user rights and abide by compliance.
Master data management (MDM)
MDM in data governance standardizes critical business data in a single source of truth, aligning data across systems while maintaining quality and compliance.
Compliance and risk management
Compliance establishes frameworks for using data (like how HIPAA clarifies who personal health information can be shared with), whereas risk management identifies and mitigates potential threats to data security, privacy and integrity. Both of the above help with keeping operations running smoothly and maintaining your organization’s positive reputation. Every industry has its own regulatory requirements, and organizations that don’t comply with them may incur penalties or fines, not to mention loss of reputation.
Data lineage
Much like charting a family tree, data lineage tracks the complete lifecycle of data, detailing where it came from, how it has changed and where it has been used across systems. Keeping tabs on data lineage ensures transparency, supports audits and identifies errors, which all help enhance data quality. By being able to visualize how data has evolved, you can run better impact analysis and make more informed decisions.
Data access
Data access controls regulate who can view or use data. By implementing permissions, authentication (like multi-factor authentication [MFA]), role-based access controls (RBAC) and fine-grained authorization, organizations can protect sensitive information and prevent unauthorized usage. Effective controls, such as MFA and RBAC, support accountability and minimize risks.
Data cataloging
A data catalog in data governance is an organized inventory of data assets. This inventory includes details about metadata, context and accessibility, and allows users to find and more clearly understand data.
How to implement data governance
Actually implementing data governance is a step-by-step process that requires buy-in and collaboration with stakeholders across the enterprise, like IT, legal and other business units. Though there is no one-size-fits-all approach, here’s one way to implement it:
- Define scope and objectives: This includes identifying data domains, owners and stewards.
- Assess the current state of data governance: Evaluate existing policies and tools to identify gaps and understand how mature your organization is with its governance strategy.
- Establish a data governance committee: This cross-functional team oversees the development, implementation and enforcement of data governance policies and procedures. Representatives from various departments work together to define objectives and establish standards.
- Implement a platform that allows central governance of data: Make sure you can centrally manage data governance polices, such as role-based access controls and compliance. This is often done inside a data platform where most data management already occurs.
- Create a data governance roadmap and strategy: After auditing and defining your organization's current maturity level, a roadmap outlines specific actions, timelines and responsibilities for executing the governance strategy (which should address data quality, classification, privacy, retention and access controls).
- Continuously monitor and improve: Data governance is an ongoing process requiring regular monitoring and improvement. Establishing metrics and key performance indicators (KPIs) helps track effectiveness. Regular audits and reviews ensure compliance and identify gaps or vulnerabilities.
A note about implementing data governance: Change can be hard. If employees are used to certain ways of working, it may help to communicate the benefits of data governance and provide training so everyone can do their part to follow new processes.
Data Governance and Snowflake
The Snowflake Horizon Catalog provides built-in data governance and data discovery to help data governors, stewards, CISO’s/security admins and data teams both protect and unlock the value of sensitive data, apps and models.
The Snowflake Horizon Catalog has a rich set of capabilities across five areas:
- Compliance: Protect and audit your data, apps and models with tags, data quality monitoring, granular policies and lineage
- Security: Secure your environment with continuous risk monitoring and protections, and RBAC
- Privacy: Unlock the value of your sensitive data with advanced privacy policies and data clean rooms
- Collaboration: Securely collaborate without moving your data
- Discovery: Immediately search, discover and access data, apps and models