2. Enterprise Data Governance
• Oversight of organization’s data as an asset
• Goal of making data usable, consistent, open, available, reliable across the
enterprise
• Comprised of these people:
• Oversight Committee / Change Control Board
• Data Stewards (Business Owners)
• Data Custodians (IT Executors)
• Comprised of these initiatives:
• Master Data Management – single point of reference
• Data Quality - incoming data within acceptable range
• Naming Conventions – consistent with strategy
• Data Integrity – consistency across systems
• Metadata Management - Business glossaries, data dictionaries, business rules
• Entity Relationship Diagrams
• Data Lineage
• ETL Source to Target Tracking - downstream
• Reporting source of data - upstream
3. Why are we Implementing Data Governance?
• We want assurance that our data are accurate
• We want to be sure that the data are reliable
• We want our systems to talk to each other
• We want several groups’ reporting to produce the same result
• Externally shared data will be audited
• Compliance risks
• Security risks
4. Data Governance Steering Committee
• Must represent the Business and IT jointly
• Receives requests for changes or additions to data
• Approves changes in data
• Understands impact analysis of database tables
• Approves changes in database tables
• Drives updates to metadata / dictionaries / reports structure
• Develops, tests and releases on a regular schedule
• Informs impacted users of changes
5. Data Stewards (Business)
• Manage data content and business logic behind all data
transformations
• Own the data
• Responsible for
• Accuracy and completeness of data
• Defining the business item / data attribute
• Establishing approved values of data
• Business rules driving quality of data
• Reporting data quality issues to Steering Committee
• Feedback to users for correct data entry and data corrections
6. Data Custodians (IT)
• Manage data transformations
• Oversee the safe transport and storage of data
• Provide infrastructure for incoming data
• Responsible for
• Implementing data transformations
• Resolving data issues
• Collaboration on system changes
• Conduct data validation and reconciliation processes following completion
of data movement
7. Master Data Management
• Data among systems are out of sync
• Goal is to provide synchronization to the most critical pieces of data in the
company
• MDM provides a trusted view of critical entities typically stored and potentially
duplicated in siloed applications - customers, suppliers, partners, products,
materials, accounts, etc.
• MDM system provides central definition of the data
• MDM system provides the system to synchronize the various systems which
connect to it (middleware)
• Business owns this, not IT, and
• sets up processes for addressing discrepancies
• provides executive sponsorship / serve as tiebreaker
• May impact or drive the creation of middleware and a reporting layer
8. Metadata Management
• Metadata includes business terms and their definitions, examples
of usage, business rules policies, and constraints
• Definitions: A report shows profit margin, but what does it mean?
• Source data: Where does the data for calculating the profit margin come
from?
• Calculations: What calculations went into the determination of the profit
margin?
• Ownership: Who (which data steward) owns this term?
• Rules: What are the business rules that are applied to profit margin?
• Lineage: What other reports show profit margin?
9. Data Quality
• Incoming data
• Do not meet the standards of the user organization
• Do not adhere to business rules in use
• Internal Data Quality rules
• IT applies the DQ rule at time of ETL
• Makes corrections where possible and programmable
• Example: convert a number to text, or vice versa
• Example: convert two-digit years to four digits
• Data Quality Business Rules
• Defined by HQ and Regional analysts
• Applied by IT at time of ETL
• Error reports are processed and sent to Data Stewards
• Data Stewards retrain source system users
• Users or Data Stewards correct data in the source system
• Example: User input an abbreviation where entire word is required
10. Naming Conventions
• Developed by IT in adherence to
department standards
• Adherence to industry best practices
• Renaming of existing data *and*
• Rewriting of all data dictionaries *or*
• Creation of middle layer with consistent
naming conventions
11. Data Integrity
• Maintaining and assuring the accuracy and consistency of data
over its entire lifecycle
• Data relationships of attributes across tables
• Facility ID or Inn Code
• Finance Account Number
• Customer Account Number
• Cascading data for deletions
12. Documentation
• How do we document these items?
• Data Governance Strategy
• Policies and Procedures
• Roles and Responsibilities
• Data Governance Implementation Plan
• Data Quality Implementation Plan
• Which tools shall we use to document what we know?
• Metadata tools
• Data Quality tools
• Web-facing repositories
• How do we maintain the knowledge base?
14. Implementation Master Plan
1. Requirements Gathering and Analysis
• Identify business objective
• Obtain stakeholder sponsorship the Steering Committee
• Build DG Roadmap
2. Architecture and Design
• Design metadata repositories
• Document master DG plan
• Define rules, procedures, and metrics of DG
• Formalize security and audit policy
3. Implement and Sustain
• Identify stewards, custodians, roles the Working Group
• Establish control mechanism and baseline DQ numbers
• Manage lifecycle of information
• Measure results and plan for continuous improvement
