Advanced Analytics Governance - Effective Model Management and Stewardship

The First Step in Information Management
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Advanced Analytics Governance:
Effective Model Management and Stewardship
April 5, 2018

What We’ll Cover on Today’s Webinar
pg 2© 2018 First San Francisco Partners www.firstsanfranciscopartners.com
§ Ensure analytics governance integration with data
governance processes, policies, operating model management and
data stewardship
§ Adapt governance best practices for
different analytics use cases
§ Confirm alignment of analytics and business
intelligence (BI) strategy with critical business
objectives
§ Balance digital technology and applied analytics rewards with
compliance risks of new ethical rules, standards and regulations
INTEGRATE
ADAPT
ALIGN
BALANCE

Why Align Analytics Governance and Data Governance?
Has this happened to you?
§ The “same” report derives different results
§ Data is shared cubicle to cubicle
§ Data is used inappropriately and inconsistently
§ Lack of trust in data leads to revalidation
§ Multiple data marts house the same “golden record”
§ Tracing data origin is a forensic exercise
The Pareto Principal of Data Science: It’s estimated that data
scientists spend 80% of their time finding, cleaning and
reorganizing data … and just 20% of their time on data analysis.

Analytics Governance Defined
An organizing framework for establishing the strategy, objectives, policy and decision-
making process for effectively finding, accessing and analyzing data – and sharing the
results to improve the competitiveness of a business.
This includes aspects of:
§ Algorithm governance
§ Model management
§ Reports governance

www.firstsanfranciscopartners.com
Integrate Analytics Governance
INTEGRATE
ADAPT
ALIGN
BALANCE

Analytics Governance and Data Governance Differences
Usage Timing
Participants Technology
Content

Roles Can Be Consistent Across Data and Analytics
Governance Partner Roles
Data SME
Data Analyst
Data Quality Analyst
Technical Lead, IT Support Partner
Librarian
pg 7
Analytics Roles
Business SME/Manager (decision-maker)
Analyst (explorer)
Data Scientist
IT
© 2018 First San Francisco Partners www.firstsanfranciscopartners.com
Governance Core Roles
Data Owner (Business)
Data Steward (Business)
Data Quality Lead (Business)
Technical Data Steward (IT)
Data Architect
Business Analyst (Business)
Data Governance Office Lead
Executive Sponsor (Business)

IT#
Provisioning#
§ Provides business relevance, agility and
responsiveness
§ Sacrifices consistency of output, data,
technology and reusability of resources
pg 8
Centralized vs. De-Centralized
© 2018 First San Francisco Partners www.firstsanfranciscopartners.com
CIO$/$COO$
Head$of$$
Analy0cs$
Technical$Support$
Solution
Architecture
Group
Data
Support
Group
Business$Support$
Business'
Analysis''
Group'
Data'
Management''
Group'
Report'
Analysis''
Group'
Provi-
sioning
Group
Centralized
§ Provides consistency of output, data,
technology and economies of scale
with resources
§ Sacrifices domain/subject matter
expertise and responsiveness/agility
De-Centralized

Example 1: Data & Analytics Governance Structure
Accountable for Governance and Change Leadership
for Data & Analytics across Company
• Executive Data & BI Owner
• Forum Chair
• Membership – Executive Process Owners
• Meeting Cadence - Monthly
Data & Analytics
Leadership Forum
Accountable for Master Data Quality across
(Customer, Product, etc.)
• MDM Practice Lead
• Membership – Chief Data Stewards
• Meeting Cadence – Bi-Weekly
Data Stewardship
Forum
Accountable for BI Standardization & Adoption
• Analytics Practice Lead
• Membership – Functional Reporting Leads
• Meeting Cadence – Bi-Weekly
Analytics
Forum
Strategy & Guidance
Agreed Decisions
Strategic Initiative Alignment
Initiative Requests
Project / Initiative Progress
Intractable Issues

Example 2: Analytics Operating Model
IT Advisor
Enterprise Infrastructure
Committee
Executive
Office
(CEO)
Strategy &
Risk
(CRSO)
IT
(CIO)
Accounting
(CAO)
Global
Services (COO)
PMO
Executive Sponsor Analytics
CFO
Head of Business Analytics
Analytics Working
Group
LOB Reporting
LOB … LOB … LOB … LOB … LOB …
CEO
Credit Analytics Client Analytics Market Analytics
LOB … LOB … LOB …
Analytic Directors
Data Analysts and Scientists

Example 2: Data Governance and Analytics
Executive Sponsor - Data Governance
COO
Business Steward Leads
Data Governance Office
DGO Chair
IT Lead
DG Coordinator
Data Management
IT Support Group
Data Governance
Working Group
Data Stewards
Marketing Fin.
Accting
Fin. Treasury Risk ECM Ops. HR
Fin.
FP&A
Credit Admin Fin. Ext.
Reporting
Legal/
Compliance
SVB Analytics Privacy/CS
O
IT Advisor
Committee
Executive Office
(CEO)
Strategy & Risk
(CRSO)
IT
(CIO)
Finance
(CAO/CFO)
Global Services
(COO)
PMO
CFO
Analytics Working Group
Analytic Directors
Credit
Analytics
Client
Analytics
Market
Analytics
CEO This is a role/relationship chart and NOT
an organization chart
Analysts, Scientists
Analytics
IT Support
LOB Reporting

Adapt Governance Best Practices
INTEGRATE
ADAPT
ALIGN
BALANCE

How Data Governance/Management Facilitates Analytics
§ Provides a focus on data as a foundational asset of the
company so that it can be used in multiple ways effectively
§ Defines data standards to ensure data consistency
§ Maps data from source to target and identifies
transformations
§ Creates rules, standards, policies and processes for data
cleansing and validation
§ Articulates most trusted and timely data sources to
facilitate data sharing
§ Identifies potential data irregularities and creates a process
to resolve them

Best Practice Components of Data Governance
Data
Quality
Data
Stewardship
Data Issue
Management
Data Change
Management
Information
Knowledge
Management
Reports
Governance
End User
Computing
Governance
Content
Governance
DATA
GOVERNANCE
DIRECTIVES
• Policies & Rules
• Processes & Practices
• Controls
• Data Standards & Definitions
• Metadata, Taxonomy, Cataloging,
and Classification
ORGANIZATION
• Operating Model
• Arbiters & Escalation Points
• Data Governance Organization Members
• Roles and Responsibilities
• Data Ownership & Accountability
STRATEGY
• Vision & Mission
• Objectives & Goals
• Alignment with Corporate Objectives
• Alignment with Business Operations
• Guiding Principles
COMMUNICATION
• Data-related Artifact Administration
• Communication Strategy
• Training Strategy
• Vehicles/Mechanisms and Content
• Awareness/Collaboration
• Statistics and Analysis
• Tracking of Progress
• Data Performance Metrics
• Continuous Improvement
• Score-carding
• Business Impact
PROACTIVE MEASUREMENT
• Impact & Readiness Assessment
• Leadership Alignment
• Stakeholder Management
• Change Plan and Implementation Checklist
• Transition to Sustaining Plan
CHANGE MANAGEMENT
TECHNOLOGY
• Collaboration & Knowledge Management
• Data Mastering & Sharing
• Data Security and Lifecycle Management
• Data Quality & Stewardship Workflow
• Modeling and Metadata Repository
Data-centric
Development
Business Rules
Governance
Access and Controls
Legal, Privacy and
Compliance
Data Management
Process
Data Security
Data Architecture /
Modeling
All these components
could be leveraged for
a sustainable Analytics
Governance Practice

Data and Analytics Governance Design Principles
Principle Description
Be clear on purpose Build governance to guide and oversee the strategic and enterprise mission.
Enterprise thinking Provide consistency and coordination for cross-functional initiatives. Maintain an enterprise perspective on
data.
Be flexible If you make it too difficult, people will circumvent it. Make it customizable (within guidelines) and people will
get a sense of ownership.
Simplicity and usability are the
keys to acceptance
Adopt a simple governance model people can use. A complicated and inefficient governance structure will
result in the business circumventing the process.
Be deliberate on participation
and process
Select sponsors and participants. Do not apply governance bureaucracy solely to build consensus or to satisfy
momentary political interest.
Enterprise-wide alignment and
goal congruence
Maintain alignment with both enterprise and local business needs. Guide prioritization and alignment of
initiatives to enterprise goals.
Establish policies with proper
mandate and ensure
compliance
Clearly define and publicize policies, processes and standards. Ensure compliance through tracking and audit.
Communicate, communicate,
communicate!
Frequent, directed communication will provide a mechanism for gauging when to “course correct,” manage
stakeholder and effectiveness of the program.

Align Analytics and BI Strategy
INTEGRATE
ADAPT
ALIGN
BALANCE

The Analytics/Alignment Process
Capture business
• Use to discern business
value
Translate into
measurable objectives
• Objectives need to be
measured
Identify metrics and
other business
information
requirements
• You need data to build a KPI
or provide a master list
Associate required DG
and DM components
• That data needs to be
managed and governed
§ Super high-value exercise
− Establish “line-of-sight” – your line of sight with critical business
objectives – business strategies, analytics and DG capabilities

Simple Alignment Example
pg 19© 2018 First San Francisco Partners www.firstsanfranciscopqartners.com
Strategy Better Outcomes
Initiative Improve outcomes and wellness through medical predictors
Objective
Deploy clinical best practices across pre-defined specialties, reducing re-admissions
10%
Metrics and business information
requirements
Re-admissions, CMS measures for wellness, Patient registry for disease categories,
Predictive model data for disease outcome predictors
Information use case (levers)
Develop analytic models to predict outcomes based on various personal, disease and
environmental factors, Identify treatments for patients that have unclear “best
practice” or evidence, Develop outcomes to treatment analysis to derive best
practices, Report CMS measures to ensure HCR compliance, Define patient-specific
order sets and patient summaries
Data Management components Predictive analytics, CMS Reporting, Patient registry, Patients, Admissions, Outcomes
Data Governance capabilities
Data Quality
Semantic and Model Management
Lineage and Provenance

Leverage the opportunity to advance maturity
1 2 3 4 5
Your
organization …
If you’re
starting here …
No data policies, so
MDM and Analytics
will remain
disconnected, no
ability to enable or
leverage these
efforts
At minimum, a
federated DG
operating model
and common data
policies will enable
the two areas to
operate
If you’re
starting here …
Each area has its
own polices and
governance. The DG
areas may even be
competing.
Policy at an
enterprise level will
enable the two
groups to
collaborate

Balance Risks and Rewards
INTEGRATE
ADAPT
ALIGN
BALANCE

New Risks Associated with Data Pervasiveness
§ Ethical Rules: Appropriate use
§ Standards: Content owner-driven usage rights
§ Regulations: GDPR, ePrivacy
(The Economist Image)

Governance Ensures a Data-Centric Approach to Analytics
§ Documented and enforced data quality
policies and processes to ensure data
consistency, standards and protection
§ Understood business logic that maps
data from source to target
§ Clear data accountability, ownership and
escalation mechanisms
§ Continuous measurement and
monitoring of data quality, adoption and
value
§ Clearly defined data elements, attributes
and computation/derivation of shared
data
§ Really know your data quality before
diving into an analytics “project”
Delivered Value INFORMATION
REQUIREMENTS
DATA DISCOVERY
DATA PROFILING
QUALITY
ASSURANCE
PRODUCTIONPOST-PRODUCTION
TARGET DESIGN
DEVELOPMENT
SOURCE TARGET
MAPPING

Adapting Governance for Different Analytics
Source: Gartner, “Best Practices for Driving Successful Analytics Governance” August 2015, refreshed December 2016, (By Thomas W. Oestreich and Joao Tapadinhasa)

Key Takeaways From Today’s Webinar
§ What business intends to do in analytics directly
influences what it needs to do with its data; form
follows function
§ Leveraging existing constructs can facilitate agility and
collaboration
§ The balance of risks and rewards isn’t driven by
regulatory compliance alone
§ Align business needs to analytics capabilities to frame
the correct data governance environment
INTEGRATE
ADAPT
ALIGN
BALANCE

Please Share Your Questions and Comments
MONTHLY SERIES

Thank you for joining us today!
Our Thursday, May 3 #DIAnaltyics webinar is:
Data-Centric Analytics and Understanding the
Full Data Supply Chain.
John Ladley @jladley
john@firstsanfranciscopartners.com
Kelle O’Neal @kellezoneal
kelle@firstsanfranciscopartners.com

Advanced Analytics Governance - Effective Model Management and Stewardship

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