Cloud Data Governance – Only 19% of organizations have completed creating a single source of truth for all critical data domains. More than half of respondents have not yet launched or are still building and testing solutions.
Forty-two percent of organizations do not believe that all or most of their data is used by application developers, data managers, data engineers, data analysts, data scientists and analysts commercial.
Cloud Data Governance
Why is there a huge gap between the need for reliable data and its use by data consumers? In many organizations, the information value chain for data consumers is slow, cumbersome and requires heavy IT involvement. Data access policies create additional barriers for businesses to access the data they need. When they do eventually receive data, it is often of poor quality and lacks the context data consumers need to understand and make better business decisions.
How The Cloud Impacts Data Management
The key to removing these barriers for your business is a comprehensive, end-to-end, automated approach to data management that directly connects your data consumers to your data producers for data intelligence that accelerates today’s business results. today. This allows your customers to quickly discover trusted data assets with the context they need to determine if a given dataset is suitable for their business use.
Intelligent Data Management Cloud™ (IDMC) is the industry’s first and most comprehensive end-to-end AI data management solution. With Cloud Data Management and Catalog services, data is accessible through our Cloud Data Marketplace. Data consumers can easily find and request data based on their organization’s data handling and usage policies.
Powered by the CLAIRE® AI engine, IDMC leverages industry-leading metadata capabilities with cloud data management and cloud data marketplace services to accelerate and automate core data management processes. IDMC enables data producers to catalog their data with rich context, including semantic meaning, and improve data quality.
However, there is a “last mile” operational data management for the data delivery component. It depends on who can access what data (access controls) and data protection, which is usually set at the cloud data warehouse or cloud data lake level. In a platform such as Snowflake Data Cloud, which can serve as both a data warehouse and a data lake, these types of controls are defined using Snowflake data management policies and tokens. But these features are configured through SQL statements in Snowflake. This requires IT to be in the midst of data demands from data producers and data consumers.
Data Governance Solution
Today, I am pleased to announce that Snowflake has released several new features that significantly accelerate and improve the delivery of data in the Snowflake Data Cloud to data consumers.
IDMC’s cloud and catalog data management services now open up a new way to define and enforce operational data management policies in Snowflake without writing a single line of code or requiring SQL knowledge. These native integrations help customers seamlessly implement policy-based data governance to provide an automated end-to-end “supply chain” for data.
IDMC’s cloud data catalog and management services are designed to provide data discovery as well as comprehensive data management across your entire data estate – from site to cloud to multi-cloud . Synchronizing and mapping Snowflake tokens allows enterprises to use Snowflake-specific access control and masking policies. They allow you to tie your policies to global data management policies and company controls. This link provides a clear view of Snowflake’s data management configuration. It makes it easy to manage all data management policies and controls—inside and outside of Snowflake—in a single-service user experience by IDMC.
Figure 2: Automatic linking of management and segmentation policies to Snowflake data assets based on Snowflake metrics with IDMC’s cloud catalog and data management services.
Overview Of Data Security And Governance
One of the biggest challenges facing enterprises today is to securely scale the delivery of trusted data to the right users in real time for advanced data intelligence. New integrations between IDMC’s cloud data management and catalog services and Snowflake represent a major improvement in productivity and availability for customers. This is done by bringing data consumers and producers together around the Snowflake Data Cloud, so they can use the power of trusted data to help transform their organizations.
Helping hundreds of Snowflake customers effectively manage and manage their data. As a key partner, Snowflake leverages solutions like IDMC cloud data management and catalog services to enable and align people.
To learn more and follow the latest +Snowflake partnership news, visit our partnership page.
1Source: IDC White Paper, edited by , “The Current State of Modern Data Management: Building an Intelligent Data Enterprise to Improve Business Results”, (Document # US49666322, September 2022) Data Migration to the Public Cloud for Enterprises ; Data teams can easily access their data, write and test data science models, evaluate new data platforms and test applications, run POCs, and deploy to production. But with the benefits of migrating to the cloud and cloud platforms, companies need to understand that when they put data in the cloud, they become responsible for adhering to strict regulations such as GDPR, CCPA, LGPD, PPO, SOC and HIPAA.
State Of Cloud Data Governance Report
The most important question companies ask us when planning their data migration is, “How do we secure and manage data in the cloud?” From an operating model perspective, data ownership and management remain the same. Data quality can be applied in the same way in an online environment. However, ensuring data security, collecting metadata and audits, tracking queues, and evaluating multiple technologies for data teams present new challenges that must be considered before migrating to the cloud.
Before moving data to the cloud, organizations should first determine how to enable analytics, as well as data security, which includes: encryption of sensitive data, implementation of data access controls accuracy and monitoring of all data access to report suspicious activity.
Data in the cloud typically resides in a cloud data lake running on an object store (eg, S3, ADLS, GCS). All of these cloud data stores support a few basic security requirements:
However, there are several other security requirements that organizations should consider to ensure that once data is migrated, it is secure, used for analytics, and compliant with regulations:
How Does Cloud Security Work Governance Cloud Data Protection
As data in the cloud is replicated by data scientists and analysts for testing and new products, companies must ensure that data usage is tracked, tagged, and stored securely without violating the requirements of compliance. Collecting and sharing metadata ensures data is easier to find, reducing duplication, making management easier, and simplifying auditing and reporting. Most importantly, metadata collection and traceability ensures that all data is traceable, so it can be reliably deleted when customers require it under GDPR, CCPA and industry regulations or confidentiality. Privacera integrates with all major metadata and compliance technologies, such as Collibra, Informatica, Alation, and more, to transform sensitive metadata to enforce policies to ensure compliance with data regulations.
Cloud platforms provide a strict access control mechanism through Identity Access Management (IAM) roles; however, IAM is complex, difficult to scale, and lacks granular access control to adequately address security requirements. There are many data processing engines used to access data in the cloud data lake (e.g. Databricks, Snowflake, Presto, Redshift, Synapse, as well as custom internal applications used for data analysis and processing ). Some of these platforms provide the necessary security controls, such as column-level access control, data masking, and row-level filters; However, these features are not supported consistently across all of these platforms. Additionally, these security controls must be managed separately in each platform, requiring more resources and increasing the risk of human error, which can lead to unauthorized access to data. To ensure consistent data access management across all cloud platforms, enterprises need a data access management platform that simplifies data access control and provides single view of all data access policies in the cloud.
As data migrates to the cloud, companies should consider cloud-native technologies to support their management efforts. While some online technologies have a corresponding cloud version, many don’t provide the same support or address new security and management issues in the cloud. If your company is considering migrating to the cloud or has already migrated data to the cloud and discovered management issues, modern cloud-native technologies should be evaluated to provide end-to-end governance (metadata management, sequencing, control access, data dictionaries and cataloguing, auditing and reporting).
Learn more about the automated data discovery, granular access control, and encryption capabilities of the Privacera Platform here, or sign up for a free 30-day trial of PrivaceraCloud, a fully managed SaaS solution for cloud data access management and records security. Data management includes people, process and technology. Together, these principles enable organizations to achieve across dimensions such as:
From Data Chaos To Data Governance To Mitigate Risks And Harness Results
While prioritizing investment in people to achieve the desired culture transformation and processes to increase operational efficiency and effectiveness will help businesses, the technology pillar is needed to connect people to data and for organizations can truly implement their data initiatives. The lead is an important tool.
Financial services organizations face unique data management needs for security, regulatory trust, and overall robustness. Once
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