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Data Governance

Platform to address data trust & compliance challenges for your organization

What is data governance?

The data governance process involves applying a set of processes to manage enterprise IT data as a corporate asset and ensure that the different business entities use and access the data in a simple, consistent and error free manner. What this does is give organizations the framework that forms the basis of recognizing, managing and applying the necessary controls around the enterprise data processing activities.

Why do businesses need data governance?

Data governance provides an organization with an accurate inventory of processing activities, apply the documented controls around the PII usage, and manage data retention requirements.

Data Quality

  • Pre-configured Rules
  • Profiling and analysis
  • Dashboard view, Issue management

Business Glossary

  • Metadata repository - business, technical and operational Data
  • Lineage and impact analysis
  • Harvesting from data store
  • Customizable UI and API

Governance Process

  • Policy creation and maintenance
  • Cross-organizational Stewardship
  • Workflow creation and monitoring
  • Integrated DG applications
  • Data Curation

Master and Reference Data

  • Match and merge
  • De-duplicate data
  • Master data promotion

Reporting and Metrics

  • Serves all data users , stewards, analysts, owners, C-level stakeholders
  • Slice and dice , Analytics capability to provide insights on improvements
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Unique features of our data governance solution

Policy Engine

The policy engine defines the policies and controls that will ensure compliance with regulatory rules applied to source data and enforce quality of data.

Business Glossary

The business glossary that defines the meta data with its standard data definitions, and documents this. This is the Heart of data governance.

Orchestration Engine

The orchestration engine manages complex cross-domain workflows, creates source data connections and maps the source data by adding predefined rules to the data asset which is broken down into a group of critical data elements as per the business requirement and configure the existing Governance process to check data quality of source data.

Governance Stewardship

Governance stewardship is when the data from multiple siloed sources is consolidated in one accurate record, using the match and merge concept, and remove duplicates to achieve the error free ‘golden record.’

Governance Metrics

Governance metrics involves assigning a trust index score to selected data assets. Reports for data assets and critical data elements are generated and available for analytics.

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Data Governance Approach

Our 6 Step Data Governance Approach

  1. Identify areas for business improvement
    The focus and accountability is on a particular area of the organization that needs improvement on priority.

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  2. Increase availability and visibility
    Increase availability of information assets, by having an open integration architecture, using which real- time accessibility is possible.

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  3. Define rules, roles and responsibilities
    Organizations define the rules for accessing the data. Roles and responsibilities of users need to be clearly stated so that the appropriate access is given.

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  4. Ensure the information asset is accurate and consistent
    Consistently ensure information asset is subjected to data profiling, parsing, standardizing, enrichment and monitoring to give users accurate and consistent data.

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  5. Drive a move towards master-data based management
    Emphasize the importance of having a governed master data based system because an organization uses high value information across its business unit. Managing this is vital to ensure data is interpreted and used correctly.

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  6. Analysis driven iterations
    Based on the analytical data, and monitoring of information assets, have process improvements, iterations, identify the success/ failure of initiatives and present this data in an easy to interpret visual format.

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