Post on 10-Jun-2020
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IRMACSAS INFORMATION MANAGEMENT, TRANSFORMING AN
ANALYTICS CULTURE
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d . Copyright © 2012, SAS Institute Inc. All rights reserved.
ABOUT THE
PRESENTER
Marc has been with SAS for 10 years and leads the
information management practice for canada. Marc’s area of specialty is in building enterprise architecture and information management strategies for large businesses.
Marc has been in the information management and
business analytics space for 20 years and he has
experience developing and applying his knowledge in several industries in several provinces across Canada –
including banking, insurance, communication, retail,
federal and provincial government, healthcare, pharmaceutical, utilities, oil and gas, and mining.
Marc.Smith@sas.com@canmjs
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Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
THE CONTEXT BUILDING AN ANALYTICAL CULTURE
• Facts, evidence, analysis as the primary way of deciding
• Pervasive “test and learn” emphasis where there aren’t facts
• Free pass for push backs—”Where’s your data?”
• Still room for intuition based on experience
• A focus on action after analysis
• Monitor and adjust - never resting on your analytical laurels
Thomas Davenport, Analytics at Work
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Analytical Culture
And Business
Processes
ANALYTICS AT
WORK THE BIG PICTURE
Data
Enterprise
Leadership
Targets
Analysts
Better
Decisions!
Systematic Review
Analytical Capability Organizational Context Desired Result
Thomas Davenport, Analytics at Work
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DATA
Thomas Davenport, Analytics at Work
• It’s not what you report with, it’s what you report on
• The prerequisite for everything analytical
• Clean, consistent, accurate, common, integrated,
accessible
• Needs to be centralized and governed
• Analytical environment ties to it, but extendable -
measuring something new and important
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Source: The Current State of Business Analytics: Where Do We Go From Here?
Prepared by Bloomberg Businessweek Research Services, 2011
EXTERNAL
VIEWPOINTCHALLENGES IN ANALYTICS ADOPTION
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Elevating data to a core business asset
Enables competitive differentiation
Analytics as a key Information Management processes
Fast time to intelligence with optimal resources
Apply analytics pervasively to a broader range of decisions
Manage data as a strategic information
asset for business value
Optimize decision making to gain
competitive advantage
KEY OBJECTIVES TOP OF MIND FOR INFORMATION MANAGEMENT
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DATA GOVERNANCE
MATURITY MODELIS YOUR DATA TREATED AS AN ASSET?
LOW HIGH
HIGH LOW
People, Policies, Technology Adoption
Undisciplined
Technology projects driven by IT only
Duplicate, inconsistent data
Inability to adapt to business changes
Reactive
Line of business influences IT projects
Redundant information foils cross-functional
efforts
High cost to maintain multiple applications
Proactive
IT and business groups collaborate
Enterprise views of master data
Data and business processes remain separate, slowing
innovation
Governed
Business requirements drive
all IT efforts
Repeatable, automated business processes
Personalized customer relationships and
an optimized supply chain
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WHAT DATA
GOVERNANCE DOES
Establishes
business
stakeholders as
information owners.
Aligns data quality
with business
measures and
acceptance.
Invites new rigor
around monitoring
and measurement.
Removes IT from
business decision
making.
Clearly defined
decision rights for
appropriate
definition and
appropriate use.
Ensures data is
managed
separately from
applications.
Positions
enterprise data
issues as cross-
functional.
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COMMON
CORPORATE
DRIVERS
CRM
ERP
BI/DW
Valuations
Monitoring Dashboards
Privacy Policy
OSFI
FINTRAC
Terms Glossary
Fraud Detection
Security Policy
PCI
SOX
Regulatory ComplianceRegulatory Compliance
Data QualityData Quality
Risk Management
Risk Management
Enterprise InitiativesEnterprise Initiatives
Data Governance
Data Governance
Data Stewards
Policy hub
MDM
What’s the highest-profile business need that can benefit from
data governance?
What additional strategies or initiatives
can leverage data governance?
Could individual projects or
organizations benefit from data
governance?
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DATA GOVERNANCE PROGRAM ROLES
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DATA GOVERNANCE
FRAMEWORKCorporate
Drivers
Business
Framework
Process
& Policy
Data
Management
Data
Governance
Execution
Process
P
R
O
G
R
A
M
O
V
E
R
V
S
I
G
H
T
Data
Governance
Charter
Guiding
Principles
Decision-
making
Bodies
Decision
Rights
Strategic Priorities: Voice of the
Customer; Compliance Mandates,
Mergers & Acquisitions
Business Drivers: At-Risk Projects: Data
Quality Improvement; Operational
Efficiencies
Data Stewardship Roles & Tasks
Mechanisms: Stewardship Dashboards,
Workflow Automation, Data Profiling Tools
People: Council, Stakeholders, Meeting Agendas
Process: Metrics Definition, Workflow, Council By-Laws
Data
Requirement
Data
Architecture
Data
Administration
Metadata
Management
Data
Quality
Security &
Access
Rights
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INSTITUTING DATA
GOVERNANCEDO YOU HAVE A TOP-DOWN PROBLEM?
• There is a large or pending corporate initiative that will hinge on decisions about the
use of information across organizations.
• Managers and staff across lines of business consistently complain about the same data
issues.
• An executive or decision maker with authority has begun using the term “data as an
asset.”
• There is consensus across organizations about the need to assign data ownership.
• Tiebreaking among organizations is needed for:
• Data definitions
• Business rules
• Data usage policies
• Security and privacy rules
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TOP-DOWN APPROACH
Initial
VisionAssemble Core Team Create
Guiding Principles
Apply Process
Identify Decision-
Making Bodies
Assign Decision Rights
Determine DG Stake-holders
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INSTITUTING DATA
GOVERNANCEDO YOU HAVE A BOTTOM-UP PROBLEM?
• A specific project is hindered by the lack of data policies or decision making.
• A team on the business side needs help for a specific initiative and doesn’t know where
to start.
• There is a key project that could benefit from tactical data improvements, like metadata
services or
• The poor quality of data is widely acknowledged for a particular:
• Source system
• Business application
• Data subject area (e.g., product)
• Business process (e.g., target marketing)
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BOTTOM-UP APPROACH
Choose Target Scope
Capability Gaps
Benefit Analysis
Engage Sponsors &
Stakeholders
Exploration
Data Profiling
Data Correction
Additional Data
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SAS / DataFlux provides unified data management
capabilities that include data governance, data integration,
data quality and MDM
SAS provides complete analytics management that includes model management, deployment, monitoring and governance of the analytics information asset
SAS provides decision services that include business rules and workflow
that facilitates integration of the information services into the business
systems
Capabilities
Strategy STRATEGY & IMPLEMENTATION SUPPORT
DATA MANAGEMENT
DECISIONMANAGEMENT
ANALYTICS MANAGEMENT
Governance INFORMATION GOVERNANCE
SASINFORMATION MANAGEMENT
SUPPORT FOR ENTIRE INFORMATION MANAGEMENT CONTINUUM
INFORMATION MANAGEMENT
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IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTSDomain ExpertMakes DecisionsEvaluates Processes and ROI
BUSINESSMANAGER
Model ValidationModel DeploymentModel Monitoring Data Preparation
IT SYSTEMS /MANAGEMENT
Data ExplorationData Visualization
DATA SCIENTIST
Exploratory AnalysisDescriptive SegmentationPredictive Modeling
DATA MINER /STATISTICIAN
How can you create a strategic advantage?
THE ANALYTICS LIFECYCLE
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SAS INFORMATION MANAGEMENT
UNDERPINS THE BUSINESS ANALYTICS FRAMEWORK
INFORMATION MANAGEMENT
ANALYTICS MANAGEMENT
INFORMATION MANAGEMENT
BUSINESSINTELLIGENCE
AN
ALY
TIC
S DATA MANAGEMENT
DECISION MANAGEMENT
Data
Trusted Information
Data
Data
Data
Data Comprehensive quality, relevance,
governance
Complete lifecycle
management
BUSINESS SOLUTIONS
Driving the decision cycle: bringing
analytics to the point of contact
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DATA SERVICES
Analytics Management
Data Management
INFORMATION SERVICES
INFORMATION GOVERNANCE & COLLABORATION
DATA
INTEGRATION
ENTERPRISE DATA ACCESS
Infrastructure Support
INFRASTRUCTURE SUPPORT:Text & Unstructured Data Support, Security, Meta-data & Lineage, Monitoring & Deployment
DATA
QUALITY
MASTER DATA
MANAGEMENT
DECISION
MANAGEMENT
Events,
Workflow &
Business Rules
MODEL
MANAGEMENT
&
MONITORING
MODEL
DEPLOYMENT
&
INTEGRATION
SASINFORMATION MANAGEMENT
CAPABILITY VIEW
INFORMATION MANAGEMENT
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VOLUME
VARIETY
VELOCITY
VALUE
TODAY THE FUTURE
DA
TA
SIZ
E
THE CHALLENGE?
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1. Acquire 2. Determine Relevance
3. Store
Trash Cache Storage
HOW DO WE MANAGE DATA IN THE PHYSICAL WORLD?
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Data Acquisition
Data Transformations
Data Normalization
Queries
SystemsUsers
Relevance is traditionally determined at query time . . .
“Acquire, Store, Analyze”
A Big Data Analytics strategy requires a new approach . . . “Stream it, Score it, Store it”
DATACopyright © 2012, SAS Institute Inc. All rights reserved.
HOW DO WE MANAGE INFORMATION IN THE IT WORLD?
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INFORMATION MANAGEMENT
DECISIONS / ACTIONS / DATA
RAW RELEVANT DATA
LOW COST STORAGE
ENTERPRISE
STREAM IT, SCORE IT, STORE IT
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REQUIRES ENTERPRISE ARCHITECTURE APPROACH
BIG DATA ANALYTICS
MARKETING
IN-MEMORY
EDW
ADW
SALES
FINANCE
SUPPLY
CHAIN
RISK
HR
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• Ultimately it’s about making better decisions faster than your competitors…
• Integrated information and analytic services directly in the operational applications
• Ability to process events & support workflow and case management
• Closed loop cycle that feeds the result of the information and analytic service back into the process
Analytics at the
Point of Decision
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…IF THE DATA IS NOT ACCURATE, RELIABLE AND
COMPLETE…
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SAS INFORMATION MANAGEMENT