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Enterprise Data Management

Data Governance Tools, Benefits and Best Practices

Chandan Gaur | 20 December 2022

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. It can also enable the authorization on the based of classified data to particular users.

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The ultimate goal of Data Governance is to establish the set of processes and procedures to integrate, protect, standardize, and store corporate data. 

Apart from these, all the key goals include,

  1. Serve as the focal point for all data risk management issues.
  2. Offer a clear understanding of data ownership, accountability, data users, and the correct data source.
  3. Reduce compliance and regulatory risk and the operational risk associated with internal operations based on inconsistent or erroneous data.
  4. Transmission of accurate, reliable, and timely data contributes to optimal operational and financial performance.
  5. Data reconciliation controls should be automated to improve operational efficiency.
  6. Provide transparency and proof that data is accurate and timely.
  7. Provide business and technical oversight for any data or information definitional changes.
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What is the architecture for Data Governance?

It forms an essential bridge between theoretical strategies and practical implementation within the enterprise.

One must know:

  • It is only a part of Data management. It plans, monitors, and enforces control for quality, security, etc.
  • Data architecture are two different domains still under development and research validation. While data architecture helps with it, it's still important to remember that it's a two-way path.

Based on the DAMA (Data Management Association) Knowledge Areas, the above data management model means that the data architecture describes the information value chain and data flow in detail. Data modeling and design involve developing a data model representing your data requirements. Data security is designed by Data Architecture and implemented in data processing systems. It addresses organizational aspects of data management, such as strategies, policies, processes, and roles. It is an overarching part of all other data management functional areas that intersect with data architecture.

What is Data Governance Framework?

The insights and decisions that data analysis and visualization provide are as valid and accurate as the data they are based on. If the underlying data quality is "dirty," i.e., it is inaccurate, incomplete, or inconsistent, the value that can be derived from it is limited, and effective decision-making is affected. This is where it comes into play.

Building the foundation for effective governance

The methods have become more popular over time, significantly as digital transformation projects have expanded. Several components should be included in data governance projects for them to be successful, including:

Standards for data

To clarify data definitions and business, one should produce data dictionaries, taxonomies, and business glossaries. This documentation clears up any ambiguity in dialogues, specifically regarding metrics and reporting. It also helps stakeholders to view the data architecture that allows the teams to do innovation for automating the processes.

Organizational structure and data processes

Thanks to data governance protocols, end users can see how data is processed within an organization. This can include regulatory data regulations, data refresh cadences, PII limits, and even data access. This form of documentation also aids organizational structure by defining the tasks and responsibilities of various jobs concerning data administration and maintenance.


Data governance technologies, such as metadata management systems, help to support data processes and standards. One can use these tools to store and safeguard data that an organization manages—documentation on business definitions, data logs, data owners, and database information. Data Governance tools can also integrate self-service data analytics tools, allowing analysts to query and visualize various data sets for reporting and innovation projects.

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What are the benefits of its 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 does Data Governance matters?

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.

How to adopt Data 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.

Best Practices of  Data-Governance

best-prctices-data-governanceHowever, the organizational framework alone is insufficient. Six essential best practices are required to ensure that data governance adds value.

Draw the attention of senior management

Its success necessitates buy-in from business leadership. 

  • The DMO's initial step is to meet with the C-suite to learn about their needs, discuss current data difficulties and constraints, and clarify data governance's function. 
  • The next step is to form a data-governance council within senior management. The council will lead the governance strategy toward business needs. They will supervise and approve initiatives to drive improvement in collaboration with the DMO.
  • After that, the DMO and the governing council should work together to identify a set of data domains and choose the business executives who will lead them. These executives lead day-to-day governance efforts, define data items, and establish quality standards. 

Benefits of having top-down business-leadership buy-in:

  • It avoids the usual difficulties of role definition and empowerment. 
  • On the business side, data stewards will recognize that the project is a top priority for the company and set aside time to handle it. 
  • It also allows for fast resolution of data ownership disputes.

Use primary transformation themes

Link governance initiatives to ongoing transformation efforts that already have CEO attention, such as digitization, omnichannel enablement, or enterprise-resource-planning modernization, to guarantee that governance efforts add value. These initiatives are usually reliant on the availability and quality of data.

Senior leadership buy-in is simplified, and the organizational structure is changed when governance is linked to transformation themes. Such approaches push data accountability and governance to product teams, integrating it at the point of production and consumption rather than governance functioning independently.

Determine which data assets should be prioritized and where data leadership should be focused.

Many companies take a holistic approach to data governance, examining all data assets simultaneously. However, relative development in any specific area would be modest with such a broad scope, and there is a risk that will not tie efforts to business demands. Data assets should be prioritized in two ways to be successful: by domains and by data inside each domain.

  • By Domains: To build a road map for domain deployment, the data council, with the cooperation of the DMO, should prioritize domains based on transformational initiatives, regulatory requirements, and other inputs. The organization should then swiftly roll out priority domains, starting from 2 to 3, to fully operational each domain in several months.
  • By data inside each domain: Prioritize data assets within each domain in addition to domains by specifying a level of criticality (and accompanying care) for each data element. Critical elements, such as a customer's name or address, should be given special attention, including ongoing quality monitoring and transparent flow tracking across the organization. In contrast, elements used less frequently in analytics, reporting, or business operations (such as a customer's academic degree) may benefit from ad hoc quality monitoring without tracking. 

Use the appropriate level of governance.

Organizations and industries have a wide range of data governance programs. 

  • Leading companies use a "needs-based" strategy, adopting the appropriate amount of governance sophistication for their business and then altering the severity level by data type.
  • The design of businesses and organizations should be tailored to the level of regulation and data complexity they confront. Organizations with several different businesses across multiple countries have more complicated requirements than those with a single firm; similarly, a high rate of data change or a low level of technology automation increases data complexity.
  • Adjust the level of governance rigor across data sets as you develop the appropriate level of governance for the organization as a whole. Legacy data standards in many organizations impose conservative quality and access limits across the board. While this reduces risk, it can also hinder innovation. Leading companies balance opportunities and risks intelligently and distinguish governance by data set.

Decide on an incremental and focused implementation strategy

Apply iterative concepts to day-to-day governance. If a backlog of known data-quality issues exists, assess and reprioritize daily, working to optimize the benefit to the business as priorities move. Even if the solution isn't flawless, push to enable priority use cases as soon as possible. 

This customized approach should be supported and accelerated by data governance, which should focus on resolving issues of availability and quality and developing effective master-data management.

Inspire enthusiasm for data

People are more likely to help ensure that data is of high quality and safe when they are enthusiastic about and dedicated to the idea of data enablement. Leading companies spend on change management to convert skeptics into data supporters. This can be the most challenging aspect of the program since it requires employees to use data and producers to share it (and ideally improve its quality at the source).

To encourage the appropriate behavior, successful companies employ a combination of interventions:

  • role modeling by the CEO and other top leaders, 
  • acknowledgment for high-quality,
  • responsive suppliers,
  • innovative demonstrated use cases,
  • training and qualifications as well as information about data-related job prospects, and 
  • leveraging data and analytics successes to generate enthusiasm through events, publications, and even data art. 
Knowledge graph and graph analytics
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Top 7 Data Governance Tools

It is the most important part of organizations across the world. A company’s growth is inevitable with its better. Many different tools built by various organizations provide this support to companies.

Several its tools are available in the market, and based on different references and parameters, their ranking varies.


Founded in 2012, Alation initially provided a data catalog platform to help companies manage their inventory and provide access to their data. Alation Data Catalog remains its flagship product, but it released a related tool in September 2021. Alation App software is designed to simplify the process of providing secure access to trusted data across IT systems, including hybrid cloud and multi-cloud computing environments.


Collibra Data Governance automates key governance and stewardship tasks to keep it up to date as your business evolves. It leverages active metadata to keep your organization's data up-to-date across all sources and environments.


Using a “self-driving” approach designed to automate as much as possible to improve efficiency and usability, Ataccama ONE automatically calculates data quality, classifies data, and helps prioritize and focus. Security and privacy policies can be automatically applied to all relevant data assets, so data is available to the people who need it when they need it.


Acquired by Quest Software in December 2020, Erwin is a fully configurable, on-demand Impact Analyst that automatically integrates metadata from disparate data sources into a central data catalog to consolidate key insights. Provide access through role-based contextual views, including dashboards.


IBM Watson Knowledge Catalog is a machine learning catalog for data discovery, data cataloging, quality, and governance. It enables organizations to access, curate, and categorize data, knowledge assets, and their relationships (wherever they are) and share them.


Informatica Data Governance includes data catalog, privacy, and data quality capabilities in an end-to-end governance solution. The company recently launched Cloud Data Catalog. It is a comprehensive SaaS offering that combines data cataloging, data quality, and AI governance with integrated metadata-driven intelligence in the cloud.

Apache Atlas

Apache Atlas is a tool that makes it easy to process and maintain metadata—designed to share data across multiple tools. Provides platform independent governance. It also provides metadata management and governance capabilities for organizations to catalog their data assets.

*There are many other tools like oracle and OvalEdge, and many will debate that they stand in the top 10.

Choose the right tools for your organization.

One of the most important factors is choosing its software that is reliable, flexible, and aligned with your business needs. Although potential solutions have been extensively explored, its tools are difficult to compare based solely on functionality and capabilities.

Some key questions to answer when choosing the right tool for your organization:

  • What are be the primary use cases?
  • How to assure its processes will be followed?
  • How can people search, trust and understand their data?
  • What is available, a cloud-first or cloud-native strategy?
  • What is the importance of data lineage?
  • How to connect natively with underlying data systems?
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What is the difference between Data Management and Data Governance? 

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. 
  • It 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. 

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