Data Governance Framework

Data Management Framework

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

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

Business Glossary

  • 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?

Business Content Layer
Data Catalog Layer
Data Quality Layer
Data Security Layer
Business Content Layer

Features


-Customer Domain Business Content

 

-Roles & Responsibilities

 

-Policies & Processes

 

-Data Quality Rules

 

-Change Management ( Workflows & Change Requests)

Data Catalog Layer

Features


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

Data Quality Layer

Features


-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.)

Data Security Layer

Features


-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

Define Capabilities

  • Definition of data dictionaries, policies, processes
  • Ownership
  • Workflows and change requests management
  • Roles and responsibilities
  • Tracking and monitoring of data quality results
  • Integration via API

Catalog Capabilities

  • Broad Pre-Built Connectivity
  • Semantic Search
  • Dynamic Filter
  • Column Profiling for gathering statistics such as
    • Value Frequency and Pattern
    • Null and Distinct value ratios
    • Max and Min values
  • Lineage and Impact Analyze
  • Auto Data Dictionary Association
  • Sensitive Data Discovery
    • Metadata and Data Rules
    • Complex Discovery Rules
    • Classification

Measure Capabilities

  • Column Profiling for gathering statistics such as
    • Value Frequency and Pattern
    • Null and Distinct value ratios
    • Max and Min values
  • Definition of data quality weights
  • Displaying data quality results with different metrics such as
    • Accuracy
    • Completeness etc.
  • Scorecards
  • Exception Management

Apply Capabilities

  • Pre-defined and expandable data discovery rules
  • Classify policies such as PHI, PII
  • Primary Key/Foreign Key Analyze
  • Prepared masking rules for sensitive data
    (Name, Surname, Phone number, Identity number, Credit card etc.)
  • Subset / Group
  • Predefined masking techniques
  • Automation

Manage Capabilities

  • Create & Schedule jobs
  • Monitoring Results and Mail Notifications
  • User privileges
  • Detailed error logs
  • Easy integration for new sources and databases