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Data Governance Tools, Benefits and Best Practices

Chandan Gaur | 28 July 2022

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What is Data Governance?

Data Governance is the process and management of data availability, usability, integrity, and security of data used in an enterprise. It includes all the steps from storing the data to secure it from any mishap. It is not just only about technology. Responsible for the particular data asset along with the technology.

It is also used in an organization at a maturity level to make sure critical and vital data is managed and protected. This gives clarity of the information which helps in defining the Decision-Making processes around data. It is a strategic, long-term process. It is essential for Finance and Insurance organizations especially those that have regulatory compliance. These organizations are required to have formal data management processes to govern data throughout its life cycle. Data governance can also enable the authorization on the based of classified data to particular users.

Big Data Architecture helps design the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. Click to explore about, Big Data Architecture

What are the Benefits of Data Governance Adoption?

The below highlighted are the benefits of Governance Adoption:

  • To improve quality of insights.
  • It helps in understanding the data and shows the data lineage.
  • Helps in adopting Regulatory Compliance.
  • Improve the capabilities of Decision-Making and communication.
  • Reduction of IT costs with centralized policies and systems.
  • Effortless audit management.
  • Controlled and organized data growth.

Why Data Governance Matter?

The organization also needs to make sure the safety of all data called Data Security, effective data masking of personal data (like SSN, passwords), and compliance with new data protection and privacy laws like GDPR (General Data Protection Regulation).

An effective Governance can provide a solution to handle this kind of problem. It also provides a complete audit report of who did what with which data. Easier for the organization to trace if something went wrong.

Data Governance is no longer optional because it underpins data security, compliance and privacy. Source- The Evolution Of Data Governance

How to adopt Governance?

Before beginning with the  Governance, the organization needs to find where improvements required in the system. Firstly, choose some specific dataset and then further implement for all the dataset.

After choosing the dataset and the problem, define roles, responsibilities, and processes for different teams. The duties can be understanding data, cleaning the data, data transformation or enrichment, and at the end monitoring. There should be one team for each of the processes. Initiating this step on the Big Data platform also helps in improving data quality. Any particular dataset and dataset owner will be responsible for the data integrity and provide the technology to ensure the integrity of the assets remains high.

After the integrity and all process, an organization must change the culture of the organization to be master data-based rather than transaction data-based. Finally, a feedback mechanism which helps in the improvement of the process, the users using have the right to raise any feedback.


What are the best Practices for Data Governance?

The best practices for Data Governance are mentioned below:

  • Target big start with small: It is an iterative process, so everyone needs to define the phases or iteration which requires in the very first go.  It starts with the people, data policies, and culture and data stewardship can be targeted. It can take many steps to reach a maturity scale. Start by highlighting a few issues or problems moving it to a more significant level.
  • To choose data stewardship wisely: Choosing a data steward depends on the stage of the underdevelopment Governance program, so the organization needs to select this carefully.
  • When Data governance and quality are integrated, trust is built on data. Some of the essential things organizations should keep in mind are - Is the source of data trustable?, Is it accurate? and Does the data have multiple meanings? 
  • Organizations should have data quality and reliability checks on new data sources to keep the big data environment trustworthy.
  • Large data volumes and various data types stress the controlled big data environment. Test your governance for big data to drive success 

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What are the best tools for Data Governance?

Here is the list of six best data governance tools are below:

  • Apache Atlas
  • SAP Master Data Governance
  • Alation Data Catalog
  • Informatica
  • IBM Watson
  • Collibra

Some of the tools are described below:

  • Apache Atlas - Apache Atlas is the governance and metadata framework for Hadoop. It supports several Hadoop components to manage metadata in a central repository. The metadata events are captured and stored in the metadata store then these metadata events can be classified using tags. These tags can be further used to enforce security policies by Apache Ranger.
  • Alation Data Catalog - Alation data catalog provides users with a single source of reference for the multiple data sources which helps in discovering and finding the data which users need. Alation data catalog helps in automating governance tasks, like updating data dictionaries and educating users on good governance practices, providing collaboration features for sharing information.
  • SAP Master Data Governance - It is a repository-oriented Data Governance tool designed to support an enterprise to meet needs like data quality and data policy management. An organization can identify and manage critical data assets by using metadata and glossary terms used to establish data policies and rules, define data ownership, and easily trace data lineage.

What is the difference between Data Management and Data Governance? 

What is Data Management?

It is designing and executing architectures, policies, and procedures that manage an organization's entire data lifecycle needs. 

  • Data preparation is the process of cleaning and transforming raw data for accurate analysis. 
  • Data pipelines are used to transfer data from one system to another automatically.
  • Data warehouses consolidate data from various sources.  
  • Data catalogs manage metadata and make data easier to find and track.
  • Data extract, transform, load (ETL) are automated processes. ETL transforms data to load in an organization's data warehouse. 
  • Data governance determines policies for maintaining data security and compliance.
  • Data architecture is the formal structure for managing data movement.
  • Data security consists of the methods to protect your data from unauthorized access. 

What is Data Governance?

It is a critical component of data management—managing how the data is processed and used throughout the enterprise. It can help answers the below questions

  • Who owes the data?
  • Who accesses what data?
  • What security measures are in place to protect enterprise data and privacy?
  • How much of the data is compliant with new regulations?
  • Which data sources are authorized to use?

Governance has four pillars. 

  • Data quality is the pillar of data-source management. The high quality of data is crucial for any data-driven organization.
  • Data security and compliance are defining and labeling data sources by their levels of risk and creating secure access points, keeping harmony between user interaction and security.
  • Data stewardship helps monitor how squads use data sources, and custodians lead by example to ensure access, security, and quality of data. 
  • Data transparency matters because every piece of the process and the procedures you put in place should work within a model of transparency. 

A Data Management Approach

Data Governance helps Enterprises to make sure essential data is governed and protected. To know more data management we recommend taking the following steps -