DATA GOVERNANCE FRAMEWORK

Data Management Framework

DATA GOVERNANCE REQUIREMENTS

What Is Data Governance?

  • Data becomes a strategic priority so does data governance
  • Data Governance is not an outcome, but a means to good business outcomes
  • It focuses efforts on critical data that drives the most value
  • It is a strategy to centralize standards and policies
  • Multiple levels of executive engagement
  • Distribute data ownership in business units
  • Provides accountability
  • It creates TRUST
  • Increase understanding
  • Availability of data with known quality levels
  • Helps everyone in the company to work collaboratively

Challenges

01

Data Dictionary

  • What are source and target systems?
  • Do you know where your sensitive data is located?
  • Is this data safe?
  • Is your data classified in different security classes with the appropriate governance around it?

02

Data Validity & Consistency

  • Preserve Production Formats
  • Referential Integrity
  • Consistency & Validity

03

Lack of Data Privacy

  • Loss of Reputation
  • Loss of Customer
  • Loss of Income
  • Customer Insecurity

04

Storage Cost

  • Managing Huge Volume of Data
  • Identify Relationships
  • Subset Criteria
  • Handle Increasing Storage Cost

05

Development Cost

  • Huge Amount of Time & Effort
  • Managing Different Technologies
  • Re-Usability
  • Performance

06

Governance & Legal Regulations

  • Lack of Data Governance in Enterprise Scale
  • Compliance with Regulations ( GDPR, KVKK etc.)
  • Penalties

Business Benefits

  • Reputation
  • Culture
  • Inovation
  • Common Business Language
  • High Data Quality
  • Business Glossary & Data Catalog
  • Customer Centricity
    • Customer loyalty
    • Customer experience
    • Single customer view / next best action
  • Digital Transformation
  • Cost Reduction
  • Automated processing / re-engineering
  • ↑ Value – ↓ Cost

Technical Benefits

01

Data Dictionary Challenges

02

Data Ownership & Collobration Challenges

03

Data Catalog Challenges

04

Data Quality
Challenges

05

Data Security Challenges

06

Governance & Legal Regulations Challenges

Challenges

  • Ability to read, understand, create, and communicate data
  • How data fits into your life
  • How to use it in an effective
  • Who data own
  • What it means

Data Catalog

  • Catalog technical metada
  • Where data is
  • Lineage
  • Impact

Data Quality

  • Data Statistics
  • Metrics to define what it means to have “good data”
  • Identifying, prioritizing, & remediating data defects
  • Monitor
  • Take action to correct poor data

Privacy, Risk & Compliance

  • Decision Making
  • Policies
  • Data Access
  • Appropriate Use
  • Regulatory Compliance
  • Risk Scores

What Is Data Governance Layer?

  • Customer Domain Business Content
  • Roles & Responsibilities
  • Policies & Processes
  • Data Quality Rules
  • Change Management ( Workflows & Change Requests)
  • Capture
    • Collect Metadata from different technologies
  • Discover
    • 20+ Pre-Built Discovery Rules
      (Name, Surname, Credit Card, Identity Number, Tax Numer etc.)
  • Lineage and Impact
    • Capture data flow between data source
    • Custom Modelling
  • Service
    • Presenting the metadata information to the use of the company
  • 100+ Pre-Built Data Quality Rules for Specific Attributes
    (Name, Identity Number, Tax Number, Mobile Phone etc)
  • Data Quality Metrics
    (Completeness, Accuracy, Timeless etc.)
  • Data Quality Score and Index
  • Reporting Infastructure
  • Ready to Use Templates
    (Data Model Document, Data Analyze Report etc.)
  • Pre-built Sensitive Data Discovery Rules
  • Pre-Built Data Masking Rules
  • Risk Score by Data Stores
  • Test Data & Data Archive Automation
  • Ready to Use Templates
    (Data Model Document, Data Analyze Report etc.)

Data Governance Layer Approach

Slide
Define Capabilities
  1. Definition of data dictionaries, policies, processes
  2. Ownership
  3. Workflows and change requests management
  4. Roles and responsibilities
  5. Tracking and monitoring of data quality results
  6. Integration via API
Slide
Catalog Capabilities
  1. Broad Pre-Built Connectivity
  2. Semantic Search
  3. Dynamic Filter
  4. Column Profiling for gathering statistics such as
  5. Value Frequency and Pattern
  6. Null and Distinct value ratios
  7. Max and Min values
  1. Lineage and Impact Analyze
  2. Auto Data Dictionary Association
  3. Sensitive Data Discovery
  4. Metadata and Data Rules
  5. Complex Discovery Rules
  6. Classification
Slide
Measure Capabilities
  1. Column Profiling for gathering statistics such as
  2. Value Frequency and Pattern
  3. Null and Distinct value ratios
  4. Max and Min values
  5. Definition of data quality weights
  6. Displaying data quality results with different metrics such as
  7. Accuracy
  8. Completeness etc.
  9. Scorecards
  10. Exception Management
Slide
Apply Capabilities
  1. Pre-defined and expandable data discovery rules
  2. Classify policies such as PHI, PII
  3. Primary Key/Foreign Key Analyze
  4. Prepared masking rules for sensitive data
  5. (Name, Surname, Phone number, Identity number, Credit card etc.)
  6. Subset / Group
  7. Predefined masking techniques
  8. Automation
Slide
Manage Capabilities
  1. Create & Schedule jobs
  2. Monitoring Results and Mail Notifications
  3. User privileges
  4. Detailed error logs
  5. Easy integration for new sources and databases

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