Guide: Data Governance

Cloud Data Governance

Cloud data governance gets managed at the accessibility, integrity, use, and security of cloud computing systems to meet important business objectives. In multi-cloud or hybrid cloud computing settings, where data gets stored in various locations, and data governance rules differ between databases, cloud data governance takes on a new level of complexity.

In cloud data governance, you should meet the following:

  • Increased privacy and security of data
  • Updated data analytics for the improvement of operations and better decision-making
  • Sensitive data access is monitored and regulated
  • Data privacy and security protocols are regularly obtained and maintained
  • Cyber security risks and data breaches get avoided

Modern tools that permit data engineers and compliance teams to automate data governance, data access rules, and privacy protection from a web application interface can help data teams manage the complexities of cloud data governance.

Within this article, we will discuss:

Steps for Data Governance in Cloud Computing

Listed below are the steps for data governance in cloud computing.

Control both On-Premises and Cloud-Based Data from a Single Location

You may house data in on-premises data centers and various public or private clouds for organizations using a hybrid cloud architecture. Efficient cloud data governance begins with centralized data governance and establishing control over all data sets, independent of their location in the network.

 

Conflicting authorizations, metadata, or security regulations can lead to non-compliance when data access gets managed from a centralized platform. This option allows data teams to migrate data between public cloud platforms while remaining compliant with data governance requirements.

 

By eliminating rework and allowing data engineers to apply data governance standards more uniformly throughout their IT infrastructure, centralizing data control yields huge efficiency gains. Data engineers may approve data access requests and define rules that change access requirements for all data sets from a single interface by implementing a single platform for data access control.

Self-Service Data Access Motivate Data Consumers

Modern data catalogs consolidate all data into a single, accessible platform, making it possible for data consumers to access, study, and analyze it. Instead of individually requesting access from each data owner, self-service access allows data consumers to access any available data set they have the appropriate permissions. Although all data consumers have access to the same catalog, data architects and engineers can limit access to specific data sets based on user rights, ensuring that sensitive information is protected.

Sensitive Data Discovery is Automated

Certain data access governance platforms can identify, categorize, and tag sensitive data across different platforms without the need for human intervention. Sensitive data discovery helps data workers spend less time manually categorizing data and decreases the risk of errors with manual data entry. Sensitive data is immediately marked whenever recognized, allowing the proper access control measures to be implemented.

Save Time

Manual data governance procedures are becoming increasingly time-consuming and inefficient as organizations’ ability to acquire and retain data grows. Furthermore, manual operations are more vulnerable to human error and hence pose a greater risk.

 

Organizations that use a unified cloud-based data governance platform can save time and effort by enacting global policies that control data availability and usage across the network rather than just within a particular database or application. This option makes maintaining uniform data governance principles across multi-cloud compute platforms much easier for data teams.

Accelerate the Certification Process for Sensitive Data

Even if sensitive data discovery is automated, data governance teams must verify that it has been correctly recognized, categorized, and labeled. Data architects and engineers should create workflows for checking, assessing, and approving the findings of automated discovery and tagging to meet these needs.

 

When data consumers need access to a data source or table, the authorization process must take seconds or minutes, not longer, as it frequently does when data owners, IT, security, and other stakeholders are engaged. A consolidated data governance platform allows for a more efficient data request procedure, allowing data teams to quickly approve access requests and connect customers to their required resources.


You can find more in-depth information here.

Data Lineage in Cloud Data

Data lineage reveals the life cycle of data by displaying the entire data flow from beginning to end. Data lineage is known as understanding, recording, and expressing data flows from data sources to consumers. This lineage covers all the data’s changes along the journey, including how, when, and why the data changed.

 

Users can work with data lineage to ensure that the data comes from a reliable source, is converted accurately, and loaded to the correct location. When it comes to making strategic decisions based on accurate data, data lineage is crucial. If data operations don’t get properly tracked, data verification becomes nearly impossible, or at the very least, extremely costly and time-consuming.

 

Additionally, data lineage evaluates data consistency and accuracy by allowing users to search upstream and downstream, from source to destination, for inconsistencies and repair them.

 

Companies can use data lineage to do the following:

 

  • Keep track of data-processing errors
  • Implement process changes with less risk
  • With confidence, migrate existing systems
  • Combine data discovery with a thorough view of metadata to construct a data mapping framework

 

A lineage gets provided at the dataset and field levels, and it is time-bound to illustrate lineage across time. The data lineage provided by Cloud Data allows users to:

 

  • Find out what is causing the negative data events
  • Conduct an effective study before making any data changes

 

Lineage at the dataset level depicts the relationship between datasets and pipelines through time. Moreover, the operations done on a group of data in the source dataset to produce different fields in the target dataset get shown at the field level.

Importance of Data Lineage

Knowing the origins of a data set is not always enough to comprehend its significance, resolve errors, understand process changes, or undertake system transfers and enhancements. Data quality gets improved by knowing who modified it, how you updated it, and the process employed. It enables data stewards to maintain data integrity and confidentiality all through their lifespan.

 

The following areas are where data lineage can have a big impact.

 

  • Strategic Data Reliance – Good data keeps businesses afloat. Data lineage provides detailed information that aids in understanding the meaning and authenticity of the data.
  • Data Governance – Tracking data lineage information can help with compliance auditing, risk management, and ensuring data is maintained and processed following corporate policies and regulatory requirements.
  • Data Migration – When IT has to migrate data to new storage facilities or software systems, they must first understand the location and lifecycle of data sources. Data lineage makes migration initiatives faster and less hazardous by providing this information quickly and readily.
  • Data In Flux – It refers to data that evolves throughout time. Management must pool, evaluate, and utilize new means of gathering and aggregating data to produce corporate value. Data lineage provides tracking features that allow old and new datasets to be harmonized and used to their full potential.

Data Lineage and Data Classification

The practice of categorizing data into categories based on user-defined characteristics is known as data classification. Data classification is a critical component of an information security and compliance policy, especially when significant amounts of data are stored. It helps understand where sensitive and regulated data is stored, both locally and in the cloud, giving a solid foundation for data security plans.

 

When paired with data lineage, data classification becomes even more powerful:

 

  • Data classification aids in the discovery of delicate, confidential, business-critical, or compliance-related data.
  • You can use data lineage tools to explore the whole lifecycle of a dataset of this type, uncover integrity and security issues, and address them.

Sensitive Data in the Cloud - A Reality

Data management requires the protection of sensitive data. This duty is much more critical in cloud-based situations, where there is no external control over who has direct access to the cloud infrastructure. The need for protection suggests that the cloud service provider could be a major attack channel. Furthermore, even a non-malicious user can unintentionally expose private data by running queries that do not consider the privacy of the people shown in the results, exposing sensitive material to the open.

 

In this context, data protection can get divided into two categories: data access and data privacy. Data access involves ensuring that no unauthorized individuals have access to sensitive data in any form. In contrast, privacy prevents personally identifiable information from appearing in the results of queries on sensitive data by appropriately encrypting it.

 

When dealing with sensitive data in the cloud, the major problem is that it must never be accessible by a third party in the cloud system. This option means that it will never be present in its raw, readable form until the sensitive data reaches the intended user. While this is not a problem for simple object retrieval systems, many systems require more sophisticated data processing, frequently involving data manipulation in memory. The employment of algorithms to run queries on encrypted data is one solution for this problem. The key advantage of these methods is that they do not require sensitive data to get decrypted to make such queries.

Cloud 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 cloud data governance are:

 

  • Increased privacy and security of data, by allowing security teams to monitor data access activity across all your data platforms, as well as set security policies universally.
  • Satori continuously discovers sensitive data access, without the need for manual or periodical scans. This way, you can meet compliance requirements in a straightforward way, and monitor all access to sensitive data.
  • Satori builds a continuously updated data inventory, including the locations of sensitive data within your data stores.

 

Read here about what Satori does, or schedule a demo now.

Conclusion

Datasets are becoming increasingly huge, limiting the ability to handle data locally, particularly in resource-intensive operations like data gathering. The necessity for trustworthiness and assurance from data management systems is even more crucial in this age of ever-increasing cloud resources for computing. It is only important to keep up with these changes while remaining credible.

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