5 Keys to Successful Data Governance in Snowflake
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Over 4,000 organizations have adopted Snowflake's data cloud. It's a safe, easy place to externally place business information for data warehouse use cases. Once your data is stored, you need to manage it. You must create a plan, hire a team, stay compliant, ensure data quality, and choose the right technology solution.
Data governance sets a plan in motion for completing these tasks. Read on for 5 tips for effective Snowflake data governance.
Data governance begins with a data governance framework. This sets the rules and processes for how you'll manage enterprise data.
Start with the snowflake data quality itself. Use metadata and modeling to examine what you have, where it is, and who has access to it.
Next, look at your organization. See what tools you have and how you can use them. With this do a skills check of the team against what your Snowflake data governance needs are.
Consider regulatory compliance as a driver for data governance. Check what regional, industry, and global regulations you must meet. More on this in section 3.
Look at what gaps you have in terms of skill, tools, and processes. Think of what challenges need to be solved with data governance and classify by type and importance.
Your framework needs to outline a specific method for each data governance process.
Common needs of a data governance plan are:
Your data governance framework should answer these questions. It should create a set of standards used throughout the organization that makes these goals easier to achieve.
Your data governance team is known as a Governance Council or Committee. They'll use cross-functional rules and procedures to perform tasks. Each member should have clearly defined roles, including:
86% of workers cite a lack of collaboration as the main cause behind most workplace data failures. Your team needs to work together to master data governance.
48% of companies ranked regulatory compliance as their primary driver for data governance.
Three of the most important, wide-ranging regulations are GDPR, HIPPA, and CCPA. While you may be in an unregulated industry or not be directly involved in process management there are many scenarios where regulatory compliance can impact your Snowflake data warehouse governance strategy.
GDPR is a set of regulations for data privacy and protection within the EU and EEA and applies to data transferred outside of its area. It’s significant because it’s a regional or zonal set of regulations that impacts many global organizations and Snowflake data governance because of data centralization.
To meet GDPR standards, you'll need to have some functional roles covered in your organization:
Any vendors or 3rd parties will need to be classified into groups such as custodians, processors, and sub-processors.
Sign a data processing agreement with any third parties that process personal data for you. Examples include providers of analytics software, data integration, and cloud servers such as Put It Forward.
To ensure your customers' privacy, make it easy for them to:
Conduct a data protection impact assessment whenever needed. Conduct regular information audits that show what information you're using and who can access it.
Be sure to notify the relevant authorities and all your customers and stakeholders of any data breaches.
GDPR violations come with penalties of up to €20 million or 4% of your annual global revenue. Avoid this serious financial burden by using data governance to ensure compliance.
Many regions and countries have specific laws and regulations for types of data management such as personal health information management. A country and industry example is if you’re an organization working in the United States that uses electronic personal health information, you’ll need to consider the impact of HIPAA's Privacy, Security, and Breach Notification Rules.
The Privacy Rule requires safeguards to protect the privacy of electronic personal health information. It sets limits on who can use this information. It also gives patients the right to examine, correct, and obtain copies of it.
The Security Rule is also not a single requirement but a set of rules that require further administrative, physical, and technical safeguards. It's meant to keep health records confidential, accurate, and secure.
The Breach Notification Rule applies to all HIPAA-covered entities. They must notify their customers and business associates if they're a breach of unsecured protected health information.
For organizations working with health records management in the United States, HIPPA compliance is not optional. Violation penalties include:
The California Consumer Protection Act is an example of a regional set of data regulations that impacts any organization doing business with citizens' data of that region.
This is beyond the scope of this article to dive into but is a solid example of how data classifications schemes within Snowflake are incredibly important for data governance to function properly.
Data quality components include completeness, accuracy, credibility, timeliness, consistency, and integrity. Beyond the scope of this article are the processes that go into ensuring data quality.
Completeness is the level at which data attributes are supplied. Credibility is how true the data is.
Accuracy refers to the data's real-world status.
Timeliness is the age of the data and whether or not it's updated adequately.
Consistency refers to whether various dataset facts match. Integrity looks into whether or not multiple datasets maintain quality when joined together.
Data quality is an essential part of data management. It ensures your information is up-to-date and accurate. This helps you prioritize and perform all business functions in an efficient, effective manner.
Data quality maintenance involves updating and standardizing data. You must also deduplicate records to create a single data view.
A Snowflake data warehouse or database is a centralized storage unit. This is where your current and historical data is located and updated.
These data warehouses don't include tools to help you trace data history, lineage, or intelligence functions that surface hidden data governance issues. Spotting hidden patterns are essential for understanding the business impact and compliance-based decisions.
Snowflake's multiple-point vendor solutions are difficult to layer together, creating data governance and traceability issues.
38% of businesses struggle with manual coding and the platform's 3rd party integrations are almost impossible to manage on your own. They aren't scalable and require you to pay more to grow.
A single configuration-based solution that provides a unified integration, governance, and management solution is a single offering that allows you to scale easily.
Data governance is essential for keeping your business information accurate, secure, and compliant.
Begin by making a data governance framework that outlines how each data-related task should be performed. Hire an effective team to carry it out. Ensure they keep your data compliant and monitor its quality.
Put It Forward provides an all-in-one data platform for Snowflake data governance solutions that include no-code integration, data lineage, and governance with AI-powered intelligent insights.
Snowflake cloud data platform is a cloud-based data management platform designed to connect businesses globally, across any type or scale of data and many different workloads, and unlock seamless data collaboration.
The data governance definition says it’s the process of overseeing and managing data assets within an organization. It ensures that data is accurate, consistent, and compliant with organizational policies and external regulations.
Why is data governance important for tech companies? It helps organizations to ensure that data is usable, accessible, and protected. Effective data governance leads to better data analytics, better decision-making, and improved operations support.
The Snowflake data governance solution helps organizations manage and govern their data more effectively.
It provides a centralized data repository, a set of data management techniques, policies, roles, standards, metrics, and procedures that make it easier to govern data more efficiently and comply with regulations.