Slideshow Evaluation Criteria for Data Governance Tools

  • November 18 2013, 2:23pm EST
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This list of evaluation criteria for data governance software tools will help you in your technology assessment.

Sunil Soares, industry thought leader and founder and managing partner of Information Asset, LLC, compiled a list of 18 evaluation criteria for data governance software tools, which he presented at the recent MDM and Data Governance Summit in New York.

1. Usability of Business Glossary

It is necessary to assess whether the data governance software allows you to create taxonomies, manage business terms (such as “unique visitor” for clickstream analytics), import business terms in bulk and hotlink business terms within business terms.

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2. Custom Attributes

How does the software name and describe the custom attributes? Beyond naming the customizable attribute, it is important to provide a definition, short description (with a little background), a long description (a few paragraphs of more depth), an example and security classification (indicating the level of security, e.g., public, internal or confidential).

3. Custom Relationships

When assessing customizable relationships, consider the acronym or abbreviation, synonyms, replaces/replaced by (which points to deprecated terms), assigned assets, allowable values (links business term to associated reference data) and what policies and data rules govern the business term.

4. Data Stewardship

Data stewards need to be able to manage artifacts such as business terms, data policies, data standards, data quality rules, data quality metrics, master data rules, master data tasks (e.g., duplicates) and any other artifacts that are fully configurable (e.g., regulation)

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5. Custom Roles

Custom roles may include data owner, data executive, data sponsor, stakeholder, subject matter expert, and those who are responsible, accountable, consulted and/or informed.

6. Approval Workflows

It is important to define the approval workflows. For example, this may include regional stewards, global stewards and IT in the case of a multinational entry code change.

7. Data Policies, Standards and Processes

Determine the data ownership within data policies, the data roles within standards and the data processes including data stewardship meetings.

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8. Master Data Rules

Assess whether the tool will allow you to create data enrichment rules, create data validation rules, create entity relationships, create record matching rules, establish confidence thresholds and create record consolidation rules.

9. Allowable Values for Business Terms (Reference Data)

Soares shares the common abbreviations for U.S. states as an example of acceptable reference data.

10. Data Lineage

Does the tool allow you to document the data lineage, including jobs running in parallel?

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11. Impact Analysis

Will the tool create an impact analysis, specifically for assets identified in the data lineage?

12. Hierarchy of Data Artifacts

The tool should allow you to link policies, rules, terms and reference data.

13. Profiling of Diverse Data Sources

This includes manual (SQL scripts), automated (vendor tools) and diverse data sources including Hadoop.

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14. Data Quality Scorecard

Don’t underestimate the value of a scorecard, listing your information governance metrics, goal, periodic status updates and baseline.

15. Data Issues Log

The data issues log should track issues, the steward assigned, data assigned, date resolved and the current status (e.g., closed, steward talking with policy department.

16. Data Issue Resolution Process

Ensure the issue management and resolution process is fully documented.

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17. Support for Internal Audit

Soares states that approximately 25 percent of data repositories are subject to internal audit on a quarterly basis, with a full 100 percent on an annual basis. Each repository should have a data owner and will be audited for compliance to specific data governance policies, such as 1) presence of a data dictionary 2) whether the rules have been documented and 3) who determines access controls.

18. Data Governance Metrics

Soares outlines specific data governance metrics:

• Reference Data - Number of candidate code values, number pending approval, number approved

• Data Issues – Number of outstanding data issues, number resolved in the last period

• Data Quality Scorecard – Data Quality Index by application, by critical data element

• Reporting Vectors – By Data Steward, Data Owner, Data Repository, Application, Data Domain

About Sunil Soares:

Sunil Soares is the founder and managing partner of
Information Asset, LLC,a consulting firm that specializes in helping organizations build out their data governance programs. Prior to this role, Sunil was the Director of Information Governance at IBM, and worked with clients across six continents and multiple industries. He is the author of three books, “The IBM Data Governance Unified Process,” “Selling Information Governance to the Business: Best Practices by Industry and Job Function,” and “Big Data Governance.”

Sunil has also worked at the Financial Services Strategy Consulting Practice of Booz Allen & Hamilton in New York. Sunil lives in New Jersey and holds an MBA in Finance and Marketing from the University of Chicago Booth School of Business.

He is active on Twitter: @sunilsoares1

Photos used with permission from Thinkstock.