Top Data Governance Tools and Technologies:

The Backbone of Governance Programs


Companies that build data-driven business models and analyze information across organizations are increasingly faced with a multitude of data management and utilization challenges. Integrating multiple data sources, duplication, poor data quality, inconsistencies of standards, inaccuracies, and, most of all—trust issues present challenges. Business units and functions operating in silos use different methods for storing data. Customer IDs, supplier information, and product descriptions can all be retained for future use. Implementing a strong cadre of Data Governance Tools becomes critical – unless utility issues are resolved, stored data holds no business value and cannot be used for analytics, insights, and business strategies.

Data governance is the process by which enterprises establish formal policies and guidelines to ensure that the data being used throughout an organization is accurate and of high quality, available when needed, presented in a usable format, and can be transmitted securely in compliance with regulations. Once governance policies are in place, how can they be enabled and supported, given the data issues mentioned? Organizations need to transform data into comprehensive, consistent, and accurate information that can be governed.

In recent years, new product developments have made it evident that data governance programs can be optimized. Associated challenges can be overcome by using supportive data governance tools and solutions. Cumbersome, handcrafted, and micromanaged solutions of previous decades can be replaced with efficient processes, such as automated consolidation, integration, discovery, and reporting to enable the success of an enterprise-wide big data governance program. Governance tool vendors, such as IBM and SAP, have been improving their products to support and sustain Data Governance 2.0—the use of data to promote business strategy and drive outcomes while removing data risk.

Data governance tools generally address three issues: data quality, program and policy management/workflows, and traditional data management. These tools and software can, more specifically, help address data integrity, usability, risk reduction, security, and preservation.

Data Quality Tools

Data needs to be trusted to deliver business value. Use the right tools correctly, and the outcome supports business growth.

Data Catalogs

Catalogs and Inventories comprise a first critical step to improving data quality. With the proliferation of open-source technologies such as Hadoop and Hive, many companies do not know what data they have and where it is located. This renders data stores useless to end users who waste time sifting and searching through raw data sources for what they need.

A data catalog enables any type of user with any skillset to find and use data from multiple sources. Catalogs also allow for crowd-sourcing of metadata for all users to contribute their knowledge. Microsoft’s Azure enterprise-wide metadata catalog is one such software that allows data to be used in any tool. The Azure catalog also helps identify dark data, so organizations can put it to better use. Another example is the IBM InfoSphere Information Governance Catalog tool, which facilitates identification, storage, and management of data assets used in daily operations. Employees can easily access up-to-date and trustworthy business information. To maintain data integrity, users are assigned a workflow and security role before they can access a catalog.

Top Data Glossary Tools

Many organizations create and document their business data in Excel spreadsheets. However, the sheer number of terms eventually renders this method unmanageable. Glossary software tools provide ways to establish properties for data categories, labels, terms, and associated metadata. Companies are able to define relationships between policies, rules, categories, and terms. Tools offering glossary functionality are often part of a data governance suite. Alation Data Catalog is one example. This software automates updating the business glossary by using all data sources. It can also map data entries to underlying data objects, such as reports, and allows room to post usage policies and data quality warnings. Different glossary tools have different emphases. Some may focus on definition discovery and collection, while others may focus on managing semantic complexities. There are also industry- or compliance-specific glossary offerings. IBM, Collibra, and Diaku cater to financial services requirements, while Information Builders Omni-Gen offers products for healthcare providers and insurers.

Governance Program Management/Workflow Tools

If data governance extends to only a small portion of the data available in an organization, beneficial impact is minimal and much risk still exists. Workflow and program management software support enterprise-wide collaboration, assists with governance processes, and integrates governance rules with business team needs, such as customer service and financial reporting functions. Users can establish policies and dictate how data should be handled across the enterprise. Since managing governance programs is important, large vendors have developed software such as SAP’s Master Data Governance and IBM’s Information Governance Catalog to allow central governance and consolidation of master data. IBM Information Governance Catalog enables users to easily explore, understand, and analyze information in compliance with governance rules. They can create, manage, and share a common business language, document and enact policies and rules, and track data usage for compliance and other purposes. Erwin DG is a new tool that focuses on building in linkages and rigorous workflows between different roles in the organization to empower users to better protect data as it moves across the organization. Its role-based access and defined workflows enable data governance policies to more easily reach across the entire enterprise.

A common workflow use for governance software is the replacement of document- and email-driven data oversight processes, which have become unmanageable due to current governance requirements. Vendors such as Profisee allow data stewards to define and authorize workflows via drag-and-drop authoring. Core master data management capabilities are also included to fully automate master data management (MDM) processes across multiple business functions.

Traditional Master Data Management (MDM) Tools

Top Data Lineage Tools

The ability to account for data lineage is a necessary competency in responsible governance. Data lineage is the ability to document, visualize, and identify the sources, uses, and interdependencies of data. Without this, data cannot be trusted and is essentially of no value to a business. Moreover, in today’s highly regulated world and in considering GPDR, a lack of data lineage capabilities creates major business problems with reporting and compliance if a business cannot prove how data has moved through its organization. Governance software, such as Collibra Data Lineage, Solidatus, Informatica Master Data Management, and SAS, is designed to demonstrate end-to-end lineage internally and to regulators. These tools range in their ability to assist with lineage discovery, recording, and extraction. Consulting companies like StrategyWise can be a solution for organizations to trace data elements with data lineage tools.

Master Data Management Tools

MDM software tracks essential company-wide data points from multiple sources related to company operations, clients, and other relevant core data. MDM platforms consolidate, organize, and present data in a clean, consistent format that users can implement as they see fit. Oracle MDM and SAP’s Master Data Governance Product are well-known solutions that blend data quality, governance, and policy management with master data management.


Considerations for Implementing Data Governance Tools

Given diverse data and governance needs, most companies will likely use more than one data governance technology solution, as no single governance data tool can manage all needs. Usually, multiple vendor products need to be integrated. Large vendors, such as IBM, SAP, and Informatica, have significant integration capabilities. However, technology alone does not ensure governance success. Internal staff, governance processes, and standards must be supported by technology and its functions. Additionally, an organization must assess its readiness to purchase and implement governance tools. This starts by determining specific end goals and what, exactly, an organization needs governance technology to automate.

Companies can drive centralized consolidation in data governance, rendering it trustworthy and useful. The right knowledge, combination of solutions, or even a single solution will reduce an organization’s total data governance cost as realized in a combination of time, money, and value lost. If your company would like assistance with assessing its readiness and need for data governance tools or would like advice on how to go about identifying and implementing such tools to support business strategies, StrategyWise has Data Governance and Master Data Management experts ready to help. Call us today at 888-623-3282.