Data Governance Tools: Managing Enterprise Data the Right Way

Data Governance Tools The Key to Unlocking Enterprise Data’s Potential

Table Of Contents

Data governance is a broad term that covers the what, why, who, when, where, and how of data access. This includes:

  • What action should be taken upon data
  • Why should this action be taken
  • Who should take this action
  • Under what circumstances should this action be taken
  • How should this action be taken

Data governance tools are solutions that allow enterprises to control the movement of data and within the organization and how it’s accessed efficiently.

Since data governance is an umbrella term covering a broad sweep of data-related activities, most organizations are already involved in it in some form without realizing it. Data governance overlaps at several points with data management, data catalogs, and data stewardship. For this reason, the functions of data governance software often intersect with those of data management and data catalog tools. This top-level view of the all-encompassing nature of data governance is important to keep in mind as we turn to take a look at data governance, its functions, its components, and the software used for data governance in detail.

What is Data Governance Software?

Data governance software leverages technical capabilities to help evaluate, implement, monitor, and enforce data governance policies in the pursuit of specific business objectives.

Data governance software is sometimes confused with data management software since their scope and functions often overlap. Despite this, however, the two diverge from their common functions at several points.  The table below summarizes the key differences between the two.


Data Governance Software Data Management Software
Used by both business leaders and users. Designed keeping the users, which is to say, IT teams in mind.
Capabilities include:

  • Workflow visualization
  • Data curation
  • Data classification
  • Access management
  • Impact analysis
  • Implementing data stewardship
  • Policy enforcement
  • Data security
Primarily used for data policy execution
Integrated with business end-goals Fragmented, command-and-control based solution with little to no integration with business use-cases
Low technical barriers to usage Requires some technical expertise to use
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The Importance of Data Access Governance in the Enterprise

Data is the most important asset of any enterprise operating in the digital space. However, if the usage of data is not linked to tangible business outcomes, if data exists in departmental silos with little consensus over access rights, and if there isn’t adequate awareness of risks involved in handling the data, businesses will fail to leverage it to its full potential.

Here’s why data access governance is critical in the enterprise:

Aligns Data Usage With Business Goals

Data is of little use if it is not tied to tangible business outcomes. Traditional data management tends to be heavily centralized and IT-centric, meaning that the full value of the data can’t be extracted by those within and outside of the organization. It also results in enterprise data being confined to individual business units, isolated from the rest of the organization, leading to the formation of what are known as data silos. Data access governance ensures that data governance is leveraged from being a mere defensive, compliance-oriented strategy to an essential business capability in its own right. It also helps break down data silos and ensures data is accessed and used as and where needed across the organization with appropriate access controls in place.

Improves Data Lineage, Data Curation, and Data Quality

Two important parameters of data quality are data lineage and data curation. By putting in place definitions, policies, and people to improve both data lineage and data curation, data governance ultimately results in better quality.

Bolsters Data Security

A critical aspect of data governance is compliance with policies, both internal and external. Internal policies are rules framed by an organization relating to data usage and access. External policies include government-legislated regulation as well as industry-wide accepted best practices.

The Evolution of the Data Governance Tool

The Evolution of the Data Governance Tool

The earliest form of data governance in the modern workplace involved manually documenting data definitions, granting access privileges, conceptualizing the flow of data, and assigning data stewardship responsibilities, all using primitive office management tools such as spreadsheets.

With the increased adoption of technology, IT departments began to view data governance as a compliance-oriented responsibility. Which is to say, data governance had to be undertaken because regulations relating to data processing, management, and security had to be complied with and not because it had any inherent business advantage in and of itself.

Such an approach had two main disadvantages:

  • It led to an increased schism between how the business and the IT units within the enterprise viewed data. In other words, it led to data siloing.
  • With rapid increases in the size, scale, and complexity of data sets, primitive data governance tools such as spreadsheets began to become rapidly obsolete.

These led to the rise of the vendor-based tools approach to data governance.

From Spreadsheets to Vendor-based Tools

Vendor-based tools reconciled the diverging business and IT needs of organizations, allowing for singular, unified views of data and its workflows. As a result, data governance for the first time began to be viewed not as a defensive, compliance-oriented liability, but rather as a business tool. Data with data governance is also more consistent, more precisely defined, more clearly delineated, and more easily accessible by everyone in the organization with the requisite privileges.

However, with the unprecedented spread of the internet, smartphones, and social media in the second decade of the twenty-first century, the amount of data available to organizations began to increase at a rate never seen before in human history. This in turn brought two accompanying paradigmatic shifts:

  • Regulation and Compliance — More data meant more regulation. There was now a much more complex and convoluted policy landscape consisting of sweeping, far-reaching regulations such as GDPR, CCAR, and MiFID to navigate.
  • Ease of Use — More data also meant more possibilities, which in turn meant an increased demand to be able to search, access, and use this data more easily and efficiently for better business outcomes, and with lower technical barriers. In other words, users began to demand systems that would allow them to use data like they used an eCommerce marketplace — simply click on all the data elements you want, add them to your cart, and hit checkout to extract all the business insights that the data is capable of yielding.

Big data had truly arrived, and vendor-based tools were nowhere near capable of managing the implications of this exponential increase in data.

Enter the modern data governance tool.

Modern Data and Information Governance Software

The modern data governance platform is distinguished by the following five features that make it stand apart from everything that came before it:

  • No-Code/Low-Code — Since data governance solutions today are expected to be used by several people across different business units each with varying levels of technical expertise, it is a prerequisite that they come with low technical barriers to entry. This also means that they come with easy-to-use interfaces and drag-and-drop capabilities which allow data elements to be searched, accessed, and shared easily.
  • Automated, Intelligent, and AI/ML-driven — Artificial Intelligence (AI) and Machine Learning (ML) have two important implications for data governance:
  1. They automate several routines but critical tasks such as data cleaning and defining user access controls.
  2. They allow data governance SaaS to be scaled exponentially, something not possible by using even the best-trained human teams. This is especially important given the speed with which data is being created every day.
  • Collaborative — Different business outcomes need different blends in different proportions of the various skill sets available in an organization. Modern data governance tools are built to cater to this need. All levels of the organization can now have access to insights from enterprise data. For instance, if the marketing team can have access to the quarterly sales performance data of the sales team, it can lead to better synchronization and seamless coordination between the teams.
  • Adaptive — All the previous data governance solutions became obsolete because they could not adapt to the changing business, technology, and regulatory environment. Modern data governance solutions are a different breed. This is evident in three core capabilities that demonstrate their adaptability:
  1. They are built to be scaled to handle ever larger amounts of data by leveraging AI and ML
  2. They can adapt to different business scenarios.
  3. They are designed to handle the rapidly-evolving regulatory framework developing in response to changed data usage practices.

New Drivers

The push towards data governance today is driven by several factors, the most important of which are:

  • The exponential increase in the quantum of data available to organizations,
  • The fast-evolving regulatory and compliance framework
  • The rapid penetration of AI and ML into all spheres of enterprise data management

Data and Regulatory Compliance

Some of the most important laws relating to data privacy and data sharing include GDPR, HIPAA, and CCPA.

GDPR or General Data Privacy Rules is the European Union’s landmark regulation in force since 2018, which makes it mandatory for organizations to ask permission when sharing certain kinds of consumer data, while also providing consumers certain rights over their data.

HIPAA or Health Insurance Portability and Accountability Act is an American regulation that protects sensitive patient information from being disclosed without their consent. This regulation applies to the health sector only.

CCPA or California Consumer Protection Act which came into force in 2018 gives Californians the right to request, access, and delete their personal information collected without their consent. It also makes it binding upon organizations collecting consumer data to disclose the purpose of doing so.

Steps to Building Data Governance Solutions for Enterprise

Building a data governance solution for an enterprise involves three core phases:

  1. Establishing foundations
  2. Building frameworks
  3. Formalizing operational data management practices

Each of these three phases in turn involves several steps. We examine each in detail below.

Establish Foundational Components

There are five foundational components of any data governance solution, which can be summarized as the 5Ps of data governance — purpose, people, processes, policies, and pipelines.

  1. Purpose — This is the reason an enterprise needs to build a data governance solution in the first place. The first step to building a data governance solution clearly outlines the goals and objectives and pegs them to metrics. A good way of doing this is to use the SMART framework. SMART stands for specific, measurable, actionable, realistic, and timely — all essential attributes of what an ideal business objective should be. Finally, depending on the business objective(s), organizations may decide which data governance model to implement — centralized, decentralized, or hybrid.
  2. People — The next step is to find the right people for each stage of the data access governance project and map their capabilities to expected outcomes. Here’s what the mapping would look like:
Role Expected Outcome
C-Level Assess financial impact and business outcomes
Executives Assess strategy and define performance metrics
Business Process Owners Assess efficiency and effectiveness of business processes and define KPIs
Data Stewards Assess data, data quality, and define metrics for doing so


  1. Processes — This involves defining and documenting the processes through which data flows within the enterprise. This also includes defining the structures used for storing data at each stage in the process,  the people who need to be granted access to the data at each stage, and the transformation that data undergoes at each stage of the process. The foundations for creating data dictionaries, data lineages, and data access protocols are laid down at this stage. Departmental silos are broken down and paths for the seamless movement of data are visualized.
  2. Policies — This involves being clear about the internal and external regulatory framework —  the data is expected to comply with, besides laying down policies for outbound data sharing. A roadmap for monitoring, implementing, and enforcing policies is also envisioned at this stage. An assessment of the resources available for staffing roles such as data stewardship, data governance councils, data custodians, information architects, etc. may also be conducted, as these roles would be critical to implementing data governance lower down the road.
  3. Pipelines — Pipelines establish two things — workflows and relationships. This is the step where organizations set timelines for objectives and assess resources, budgets, and capabilities. If any existing tools and resources for data governance are already in place, strategies are made to integrate these into the newer capabilities that are being planned. Once a pipeline with cost, resource, and time estimates for the workflow is in place, the foundational state of building data governance is complete. The action now moves to put a more concrete framework in place.

Build the Framework

Building the framework of software for data governance involves working with data at the granular level. It involves the following steps:

Data Modeling & Design — Data modeling allows data and its interrelationships to be visualized and communicated easily among all the stakeholders in the data value chain. This helps break down the complexity of data and enforces uniformity upon data definitions and metadata. Data design is the process of transforming the visual depiction of data, and interrelated data elements arrived at through data modeling into concrete data structures. If we were to draw an analogy with building a house, this step is the equivalent of drawing a blueprint and deciding on which materials to use in the construction of the house.

Data Dictionary/Metadata Repository — To be used in data governance, data needs to be FAIR — findable, accessible, interoperable, and reusable. Metadata makes sure that it is. This is why it is essential to create a metadata repository or data dictionary that contains definitions, attributes, and interrelationships of not just the data elements being used by the enterprise but also includes information about how this data is labeled, sourced, classified, and is to be used. Which is to say, it also contains metadata information. The data dictionary is what makes searching, accessing, and using data possible, and is thus the beating heart of a data governance tool.

Data Compliance and Access — This is where usage and access rights to data are defined for internal and external stakeholders. This is also where compliance issues arising out of access to data are addressed. Modern data governance software is moving away from a closed-door, controlled approach to data access driven by a fear mindset, to a trust-based access approach, in which cross-enterprise sharing of data based on situational trust is seen as an asset to unlock greater business value.  In other words, organizations are seeing a paradigm shift from a must-not-share-unless to a must-share-unless approach to data access and compliance.

This is because of two factors:

  • A general technological shift towards IoT and open data which is also making data governance software open source
  • An increasing tendency to view cross-enterprise data sharing as having the potential to create economies of scale when it comes to utilizing data

Data Quality Design and Implementation — Data quality is determined by five parameters:

  1. How accurate it is
  2. How complete it is
  3. How consistent it is
  4. How reliable it is
  5. Whether it is up-to-date or not

Data quality needs to be ensured throughout the planning and building of a data governance platform to ensure business relevance. After all, the business value isn’t just derived from data; it is derived from high-quality data.

Communication and Change Management — Change management with respect to data governance could mean any of the following:

  • Changes to data models
  • Changes to data structures
  • Changes to data definitions, dictionaries, and/or metadata
  • Changes to data flows within and outside the enterprise

In other words, changes to any aspect of data involved in an enterprise need to be factored in when building a data governance solution. Business, technology, and regulatory frameworks evolve fast, and it is important that a good data governance platform has change management capabilities built into it.

Formalize Operational Data Management Practices

Once the framework of the data governance tool is in place, the next step is to formalize its data management practices. This includes:

Master Data Management — Master data is usually an enterprise’s most important data asset. As this master data is shared across an organization, it may be copied, transformed, or shared, leading to inconsistencies in its definitions and usages. Master data management practices include cleaning, curating, defining, standardizing master data, and assigning stewardship roles for monitoring and compliance purposes.

Data-Quality Auditing and Monitoring — Checking that data meets the quality standards in terms of accuracy, completeness, reliability, consistency, and timeliness is data quality auditing. Data quality must be consistently monitored throughout the workflow to ensure it remains relevant and useful for business needs.

Data-Quality Reporting — Once anomalies and exceptions have been identified in the data through data quality auditing and monitoring, these are aggregated to identify patterns throughout the data set. Next, the state of quality of the entire data set is recorded and captured, and shared with stakeholders in real time. This, in turn, allows the exact sites of exceptions to be identified and the requisite remedial measures to be applied. A good data governance platform should provide easy access to data quality auditing, monitoring, and reporting capabilities.

The Benefits of Data Access Governance Software

The Benefits of Data Access Governance Software

A good data governance platform provides businesses with:

  • Higher data quality — Poor data quality causes businesses to lose millions of dollars every year. Good data governance tools can help organizations obtain accurate, complete, reliable, consistent, and up-to-date data.
  • Improved regulatory compliance — Non-compliance leads to data risks which in turn not only create a trust deficit but also lead to direct monetary costs on businesses in the forms of fines and penalties by regulatory bodies. Data breaches alone cost American businesses over $9.44 million in 2022.
  • Increased revenue — We’ve seen how data governance software helps businesses mitigate costs arising out of poor data quality and non-compliance. But data governance is more than just a defensive strategy to save expenses. It is a business capability that allows digital businesses to best leverage their most important asset — data — and increases revenue. Better data governance means higher-quality data which can easily be scaled up. This, in turn, means richer, better business insights at scale, which translates to higher revenue.
  • Better decision-making — Data governance software helps remove silos, provides unified, singular views of workflows, and ensures consistency of data and processes. This means everyone in the organization is on the same page when it comes to viewing and interpreting data. This results in better decision-making.
  • Optimized business performance — With improved workflows, clearly defined processes, and fewer bottlenecks, business performance across the board is optimized.

What to Expect from Data Governance SaaS in the Future?

Here’s how the next few years are expected to transform what data governance SaaS looks like:

  • Data Policy — As the data policy landscape evolves rapidly, so will data governance SaaS. For instance, it is estimated that by 2024, 75% of the world’s population will have some form of privacy control regulation covering the use of their personal data. Data governance SaaS will need to evolve accordingly to adapt to this varied, complex, transnational, and piecemeal web of regulation covering the use of data.
  • Data Intelligence — Data governance tools of the future will become progressively more intelligent. This could open up several possibilities. For one, tools could become capable of analyzing historical data to predict future insights. Tools could also predict the most commonly performed operations on a dataset and learn to perform it themselves.
  • Automation of Data Governance — We entered the Zettabyte era in 2016. One zettabyte is equal to one billion terabytes. Today, most organizations dealing in big data possess data in zettabytes, with the quantum only increasing each year. This means performing routine governance tasks on data using human labor and skill will increasingly become unfeasible, prompting a trend towards increased data automation in data governance.

Revelate for Data Governance — How Do They Work?

Revelate is a platform that fully automates data fulfillment. Like data governance software, Revelate allows organizations to share data both internally and with external stakeholders without having to worry about regulatory or licensing concerns or about the safety and security of the data. However, as discussed in this article, designing and implementing a classic data governance platform in-house is a meticulous, long-drawn-out, and resource-intensive process requiring significant investment in time and capital.

Revelate, on the other hand, allows data to be extracted from systems, cleaned, prepared, packaged, and ready to be shared or sold in external marketplaces while adhering to all the policy and licensing regulations that apply to the data. In other words, high-quality data, ready to be shared and monetized, with complete trust and security, with the whole process being fully automated.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started


Implementing data governance is no longer an option for any enterprise dealing with data. However, given the heavy investment in time and capital that developing in-house data governance capabilities requires, many businesses miss out on the opportunities arising from sharing high-quality data, both internally and externally.

Revelate has been at the forefront of changing the conversation around data sharing, packaging, and fulfillment. Revelate helps businesses make the most of their most valuable asset with its fully automated data fulfillment services. Get in touch with us to know more about how you can monetize your enterprise data safely, securely, and with minimal resource diversion.