Cloud-based SaaS apps and data processing brings in an ever-increasing influx of data. Data is the gold of the internet. The more data a business has, the better off they are in the business world. Access to data necessitates real-time business judgments.
However, businesses also need to have a secure place to store and access their data. To meet these frequent data integration use cases, you may have considered a cloud data warehouse, such as Snowflake.
Snowflake is a popular option whether your firm has elected to transfer its data from its existing data silos or load infinite amounts of raw data from diverse data sources.
Still, it might be challenging to keep track of data from many different sources. For a company, ensuring the accuracy and suitability of its data source is of the utmost importance. This option is in addition to meeting the self-service expectations of all of their users. It’s here where data governance has the most potential influence.
While data security and management are important, a key component of good data governance is making it possible for employees from all levels to access, exchange, and process the rich information that you can glean from the organization’s data.
It safeguards the accuracy, reliability, and consistency of the data sent throughout the company. Cloud-based data warehouses benefit from a well-designed data governance approach. You can magnify the benefits of a cloud-based data warehouse by implementing a well-designed data governance policy.
In this article, we will be discussing the following:
- The Role of Snowflake in Data Governance
- Why is Data Governance Important?
- Challenges of Cloud Data Governance
- Snowflake Capabilities in Data Governance
This is part of our Snowflake Security guide.
The Role of Snowflake in Data Governance
AWS, Microsoft Azure, and Google Cloud Platform serve as the cloud infrastructure for the Snowflake data cloud. It is great for enterprises that don’t want to devote resources to the setup, maintenance, and support of in-house servers. Snowflake offers a solution that does not require hardware or software to select, install, configure, or manage data.
Snowflake’s architecture and data-sharing features are what set it apart from other solutions. Since you may scale storage and processing independently under the Snowflake design, users can utilize and pay for both in the same transaction. Organizations may easily and instantly communicate controlled and secured data thanks to the sharing capabilities.
As a cloud data warehouse, Snowflake enables you to store and analyze your organization’s data in a single location. It provides data storage repositories for reporting and data analysis that you can use. Many data services teams are drawn to Snowflake because of its capacity to absorb massive amounts of raw data in various formats from a wide range of sources. It’s possible to extend your data lake’s storage capacity dynamically without respect to compute nodes because Snowflake separated storage from computing resources. You can then resize your warehouse computing elastically to meet demand only when needed.
Why is Data Governance Important?
“Data governance” refers to the processes and procedures that an organization uses to ensure the efficient use of information to achieve its objectives. An organization’s data governance processes establish their data quality and security policies and practices. Data governance defines who can do what, with what data, when, and with what means.
Having a well-defined data governance strategy is essential for every organization dealing with large quantities of data. The strategy will illustrate how consistent, standard processes and responsibilities may help your business. Using business drivers, you can identify the types of data that need extra scrutiny as part of your data governance plan and the expected outcomes of that work. For your data governance framework, this plan will serve as the foundation.
For example, you could design your data governance strategy to protect the privacy of healthcare-related information so that you can securely manage patient data as it moves through your organization. Compliance with government regulations, such as the GDPR, will be ensured by defining retention requirements (e.g., the history of who modified what information and when).
By implementing data governance, organizations may ensure that roles and responsibilities relating to data are explicitly defined and part of the agreement. A well-planned data governance structure covers strategic, tactical, and operational duties and responsibilities.
Challenges of Cloud Data Governance
Identifying and leveraging genuine commercial value data is fundamental for all organizations. Recent analysis shows that many data-driven companies have become overwhelmed by their data and no longer focus on critical business goals.
One of the top cloud concerns is data governance, independent of an organization’s level of experience. Here are the top three challenges organizations struggle with when managing cloud data governance:
Cloud Data Regulatory Compliance
The consumer and cloud provider share responsibility for cloud data regulatory compliance, just as they do for data security. Larger cloud vendors will supply regulatory frameworks they support with third-party auditor compliance reports and attestations. It is the responsibility of each business to review the documentation and ensure that the contents fit the relevant compliance requirements.
Identity and access management (IAM), data security, and audit trails are all included in the most popular solutions. However, the customer must ensure that the tools are configured and used to meet the framework’s control objectives.
A shared duty between the service provider and the firm itself can help organizations overcome this problem. However, the company, and not the cloud provider, is ultimately accountable for achieving all control objectives of the compliance frameworks.
Rapid data expansion is becoming an ever-increasing concern for cloud platforms, affecting both performance and the ability of users to get real-time business intelligence.
The lag in data access caused by dividing data between cloud and on-premises platforms might hurt performance. Administrators who handle high-performance cloud apps will continue to be plagued by increasing data quantities. Edge and fog computing is primarily motivated by the need to shift computation and storage components closer to the devices and data sources with which the system communicates to enhance performance.
Drowning in Data
The ideal controlled data lake must have the correct data governance strategy, which establishes a more collaborative approach to governance upfront as the number of data sources grows. The most knowledgeable members of your company’s workforce can take on the roles of content creators and curators. From the beginning, it’s crucial to work together with data as a team. Otherwise, the amount of work required to assess the reliability of the data flowing into your data lake may become overwhelming.
Snowflake Capabilities in Data Governance
Snowflake’s cloud-based design addresses many challenges that plague traditional hardware-based data warehouses, such as restricted scalability, data transformation issues, and high query volume delays. Here are some of the capabilities Snowflake has in terms of data governance.
Object tagging enables Snowflake users to add metadata tagging on objects such as tables, views or columns. Tagging use is tracked and can be used in queries. This allows users to create reporting based on the tagging, as well as apply masking such as row access policies and dynamic masking policies based on tags.
Snowflake logs all objects that are accessed in each query, including the original location that was accessed (in case a data was accessed using a view). The data is then stored at the ACCESS_HISTORY view, enabling you to report on source locations being accessed.
- Column-level security: Snowflake allows setting dynamic masking policies and you can also apply tokenization and encryption by using external functions.
- Row access policies: Snowflake allows setting row access policies which determine the access of specific rows within query results according to specific criteria.
- Auditing: the auditing of both users’ access history and the reference of objects by metadata helps to ensure the security and provide data governance.
The amount of information that businesses now have at their disposal about their customers, clients, suppliers, patients, and workers is astounding. An organization’s chances of success increases when this data gets put to good use to understand the market and its target audience better. Using the same Snowflake data governance principles, you can ensure that your company’s data is reliable, properly documented, and simple to locate and use while preserving compliance and confidentiality.
Snowflake Data Governance With Satori
Satori is a platform that is built for enabling DataSecOps, especially in modern cloud based data stacks. Some of the main benefits Satori provides in data governance are continuous discovery, monitoring and auditing of sensitive data, as well as being able to set security policies that apply on all your data platforms.