A Quick Overview on the Evaluation Checklist to Choose Right Data Governance Tools

Presently, several businesses are evolving rapidly and daily data governance mdm systems process several transactions and generate massive amounts of new data, a few as simple as adding new customer, payments, credits and debits, merchant and material. When you are entering the data manually or digitally there is constantly the possibility to enter wrong or duplicate data and this can lead to a big data failure for decision making and executing new business tactics.

Companies understand this and see that their data have to be purified and improved to compete and to reap all the benefits of their old and present master data and transactional data. To get an improved handle on data as a strategic asset, organizations are empowering their people and technology and processes to deal with the long-term quality of their product data.

Data governance tools control the quality of the master data and offer consistent and relevant data on which the business users can depend upon to make important decisions. The below mentioned checklist of evaluation for data governance tools will assist you in choosing the right one for your company.

Take a Look at the Evaluation Checklist To Choose Right Data Governance Tools:

1. Business Glossary Usability: It is very important to evaluate whether the chosen data governance tool enables you to generate taxonomies, deal with the business terms, import the business terms in large quantities and hotlink business terms within the business terms.

2. Personalized Features: Find out in what ways does the chosen data governance tools name and illustrate the custom attributes. Apart from naming the customizable attribute, it is essential to offer a definition, a small description, a lengthy description, an example and safety categorization.

3. Personalized Relationships: While evaluating personalized relationships, consider the assigned assets, allowable values, abbreviation, synonyms, replaces/replaced by and which policies and data principles run the business terms.

4. Data Stewardship: Data stewards must be able to deal with the artifacts including business terms, data quality metrics, master data rules, data policies, data standards, data quality rules, master data tasks and any other artifacts which are completely configurable.

5. Personalized Functions: Custom roles in the advanced data governance tools might include data owner, stakeholder, subject matter expert, data executive, data sponsor and those that are responsible, consulted and/or informed.

6. Approval Workflows: It is very essential to describe the approval workflows and it might include local stewards, global stewards and IT for a multi-national entry code change.

7. Data Policies, Principles And Procedures: Find out the data ownership in the data policies, the data functions within rules and the data procedures including data stewardship meetings.

8. Master Data Principles: Evaluate whether the data governance mdm tool will enable you to make entity relationships,  generate data improvement regulations, make record consolidation regulations, build data validation regulations, build record matching regulations and set up confidence thresholds.

9.   Data Lineage: Find out whether the chosen tool enables you to document the data lineage such as the works that are running in parallel.

10. Data Artifacts Hierarchy: The chosen data governance tool must let you to connect policies, principles, business terms and reference data effectively.

11. Different Data Sources Profiling: This would include manual, automated and different data sources.

12. Data Quality Scorecard: Never undervalue the importance of a scorecard, as it allows you to list your data governance metrics, objectives, periodic status updates and baseline systematically.

13. Data Errors Log: The data errors log must monitor errors, the allotted steward, data assigned, date resolved and the present status.

14. Data Error Resolution: You must make sure that the data error management and resolution process is completely documented.

15. Support the Internal Audit Process: Around 25% of the data repositories require an internal audit on a quarterly basis, with 100% data repositories on a yearly basis. Every repository must have a data owner and must be audited for compliance to particular data governance policies.

16. Primary Data Governance Metrics: Make sure that your data governance tools outlines a few data governance metrics such as reference data (total number of candidate code values, pending approval and approved), data errors (total number of outstanding data errors and previously resolved errors), data quality scorecard (Data Quality Index by application and by vital data element) and reporting vectors (data repository, application, data steward, data owner and data domain).

So, make sure to evaluate your data governance tools on the basis of the checklist mentioned above to make the right choice and manage your master data effectively.