sas data management capabilities: supporting a …...sas data governance framework top-down...
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SAS Data Management Technologies
Supporting a Data Governance Process
Dave Smith, SAS UK & I
Agenda
• Data Governance
• What it is
• Why it’s needed
• How to get started
• SAS technologies which can assist Data
Governance programs
Data Governance Defined
• Formal orchestration of people, processes and
technology to leverage data as a corporate asset
Why Govern Data?
• Regulation
• Risk
• Efficiency
• Opportunity
The interconnectedness of people
process and technology
Data Item
Owner
Physical
Relationships
Quality
AttributesLineage
Metadata
Location
Business
Technical
Status
Links
Definitions
Importance
Requirements
The Data
Governance
Journey
ORCHESTRATING PEOPLE, PROCESSES, AND
TECHNOLOGY
IntegratedProactiveControlledManagedUnpredictable
No awareness
No accountability
Nascent awareness
and Data Stewards
Assigned DS
Accountability of
business owners
Proactive
behaviours on DQ
Recognized DS
DG office
DQ culture
No common
language
Application-centric
Business
definitions
DQ processes
Shared business
definitions and
rules
DG policies
DQ embedded in
processes
Optimised cross-
functional
processes
No tools
DQ tools
Metadata
repositories
Shared metadata
Business glossary
Analytical MDM
DG process tool
and dashboards
Operational /
collaborative MDM
Data as a service
Data Management vs. Data Governance
Data management is a by-product
of data governance
Effective data management needs
to be governed
Data
Governance
THE QUESTIONS IT ADDRESSES
Buying
System
Warehouse
Mgt System
Promotions /
Marketing
Campaign
Mgt
POS WEB
CRM /
Loyalty
Program
Cons.
Marketing
Customer
Exp.
Digital
Marketing
Finance &
Risk
Call
Centre
Market
insight
Disparate needs for data consumption
PricingNew Product Introduction
Promotion ManagementCustomer Registration
Emailing Marketing Campaign
Data silos / Application Centric Data Generation & Manipulation
Unmanaged cross-functional processes
Who owns the data?
Who can author data
and how?
How are conflicting
needs addressed?
How is inaccurate
information
corrected?
Who can decide
about the
changes?
What does good
data look like?
The role of
Data
Stewards
ORCHESTRATING CROSS-FUNCTIONAL COLLABORATION
ITBusiness
Users
Data
Stewards
Create & Consume
Manage & Monitor
Implement,
Adapt & Extend
Common Data Governance Challenges
• Seen as an academic exercise
• The culture doesn’t support centralized decision making
• Considered an IT issue
• The ROI isn’t clear
• Definitions and explanations of data governance are varied and
contradictory
• Nervousness about “the ‘G’ word”
SAS Data Governance Framework
Top-down
Bottom-up
Where to Start?
Data Quality
Analysis
Impact & root cause
analysis
DQ Standards
definition
Quick Wins
Data Dictionary
definition
Vision &
Roadmap
Organizational
framework
Data
Stewardship
model
DG & DQ
Processes
Business
case / ROI
Prioritization of
DM initiatives
Other Best Practices
• Understand what’s important to management now
• Work within your culture
• Understand your current state before making the pitch
• Choose sponsors based on initiative owners
• Corrections at source & available to real time processes
• Treat Data Governance as a project
• Rely on the big-bang approach
• Treat all data the same way
SAS Technologies for Data Governance
SAS Data Management Platform
Data Quality Process
Define the terms and
sourcesBusiness Owner
Define the key entities
Identify the sources and responsibilities
1
Discover & Profile
the DataDQ Analyst
Qualify & Quantify actual issues with the
Data
2
Design data quality
standardsBusiness Owner DQ Analyst
Design the business rules to enforce data
quality and data services
3
Apply injection and
execution
Operations and
DI Experts
Embed the DQ services and business rules into the operating
systems and DI processes
4
Measure & Monitor actual vs. expected,
identify trends, allocated tasks
Monitor & Publish
DQ measurement5
Data
Steward
Business
Owner
Update & Improve systems and processes
Remediate &
Improve6Operations and
DI ExpertsDQ
Analyst
The Relationship service
• The Relationship Service collects and stores metadata
• Content from SAS and sources outside of SAS
• Processes that include resources used in data management,
business intelligence, and data integration
• Consists of Resources and Relationships
• Resources are metadata representations of data assets or
processes
• Relationships describe how two Resources are related
Relationship Types
• Is dependent on
• Is parent of
• Contains
• Is synonymous with
• Is associated with
• Is equal to
Lineage Viewer
• Acts as a viewer on the
relationships database
• Allows different views of data
lineage including governance
and impact analysis
Business Data Network
• Central definitions of Terms
across the organisation
• Links business and technical
definitions to enable
collaboration and clarity
Federation Server
• Create federated views of data
from diverse sources
• Apply row and column access
control, data encryption and
masking to sources
• Enable detailed logging of data
access