Enabling an Analytics-Driven Organization

The First Step in Information Management
www.firstsanfranciscopartners.com
Enabling an Analytics-Driven Organization
Kelle O’Neal
kelle@firstsanfranciscopartners.com
415-425-9661
@1stsanfrancisco
Samra Sulaiman
samra.sulaiman1@gmail.com
202-320-9764

pg 2
Why We’re Here
Purpose:
Understand the People, Process and Technology needed to
support an Analytics-Driven Organization
Outcome:
 Understanding how Data Management and Data Governance Support Analytics
 Knowing the organizational constructs needed to trust Analytics Data
 An ability to manage change
 Practical knowledge that can be immediately implemented
© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Table of Contents
1. Introduction: Clarification of terms and Level setting
2. Enabling Analytics through EDM:
• Master and Reference Data Management
• Meta Data Management
• Data Quality
• Architecture
• Security and Privacy
3. Creating “line of sight” from Data to Analytics
4. Building the Organization
5. Addressing Change
6. Findings from Research: The relationship between Descriptive and Predictive
Analytics
7. Summary and Wrap up
pg 3© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

What is Analytics?
Data Insight Action
© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 4
Our Focus Today

pg 5
The Big Picture: EIM Framework
Provides a holistic view of information in order to manage information as a corporate asset
Enterprise Information Management
Information Strategy
Architecture and Technology Enablement
Content Delivery
BI, Performance
Management , and Analytics
Data Management
Information Asset
Management
GOVERNANCE
ORGANIZATIONAL ALIGNMENT
Content Management

Why is EIM Important?
Internal pressures:
 Desire to understand customer at any time
from any channel
 Data Quality issues are persistent
 Balance of old mainframe systems with
new technologies
 Movement to the cloud and losing control
of data
 Data Volumes are increasing
 Mobile apps enabling data to be created
and accessed anywhere
 Project oriented approach to addressing
issues/opportunities
External pressures:
 Greater amounts of new
regulations
 Increasing Customer
Demands – my information
anywhere at any time
 Technology and market
changes outpacing ability
to respond
EIM ensures the right people are involved in
determining standards, usage and integration of
data across projects, subject areas and lines of
business

Develop and execute architectures, policies and procedures to manage the full data lifecycle
Enterprise Data Management
Ensure data is available, accurate, complete and secure
Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

Symbiotic Relationship
An EIM initiative is an important
component of a Data Governance
Strategy
Must Have “Tools”…
• Documented and enforced governance policies
and processes
• Clear accountability, ownership and escalation
mechanisms
• Continuous measurement and monitoring of
data quality & adoption
• Executive support to create a culture of
accountability around the quality of the
data…it’s everyone’s concern
• Solid alignment between business & IT
Technology alone will not solve
the problem
You can’t “do” EIM without
Data Governance
pg 8

Data Governance Definition
 Data Governance is the organizing
framework for establishing the
strategy, objectives and policy for
effectively managing corporate data.
 It consists of the processes, policies,
organization and technologies required
to manage and ensure the availability,
usability, integrity, consistency,
auditability and security of your data.
Communication
and Metrics
Data
Strategy
Data Policies
and Processes
Data
Standards
and
Modeling
A Data
Governance
Program consists of
the inter-workings
of strategy,
standards, policies
and communication
pg 9

pg 10
Data Governance Framework
• Vision & Mission
• Objectives & Goals
• Alignment with Corporate
Objectives
• Alignment with Business
Strategy
• Guiding Principles
• Statistics and Analysis
• Tracking of progress
• Monitoring of issues
• Continuous Improvement
• Score-carding
• Policies & Rules
• Processes
• Controls
• Data Standards & Definitions
• Metadata, Taxonomy,
Cataloging, and Classification
• Operating Model
• Arbiters & Escalation points
• Data Governance Organization
Members
• Roles and Responsibilities
• Data Ownership & Accountability
• Collaboration & Information
Life Cycle Tools
• Data Mastering & Sharing
• Data Architecture & Security
• Data Quality & Stewardship
Workflow
• Metadata Repository
• Communication Plan
• Mass Communication
• Individual Updates
• Mechanisms
• Training Strategy
• Business Impact & Readiness
• IT Operations & Readiness
• Training & Awareness
• Stakeholder Management & Communication
• Defining Ownership & Accountability
Change
Management

How Data Management / Governance 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
“Between 25 percent and 30 percent of a BI initiative typically goes toward
initial data cleansing.”
Competing on Analytics, Davenport and Harris
pg 11

Enabling Analytics Through EDM
• Master and Reference Data Management
• Meta Data Management
• Data Quality
• Architecture
• Security and Privacy

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Privacy/Security
DATA GOVERNANCE
Reference Data
Management

pg 15
Master Data Management (MDM)
What is MDM?
MD is a technology-enabled discipline in
which business and IT work together to
ensure the uniformity, accuracy,
stewardship, semantic consistency and
accountability of the enterprise’s official
shared master data assets. Master data is
the consistent and uniform set of identifiers
and extended attributes that describes the
core entities of the enterprise including
customers, prospects, citizens, suppliers,
sites, hierarchies and chart of accounts.

pg 16
MDM Key Considerations
Category Decision
Entity Types • What type of data will be managed in the MDM Hub
• What are the agreed upon definitions of each type
• What is the required cardinality between the entity types
• What constitutes a unique instance of an entity
Key Data Elements • Purpose, definition and usage of each data element
Hierarchies and Relationships • Purpose, definition and usage of each hierarchy / relationship structure
Audit Trails and History • How long do we have to keep track of changes
Data Contributors • What type of data do they supply
• Why is this needed
• At what frequency should they supply it
• What should be taken for Initial load versus ongoing

pg 17
MDM Key Considerations (continued)
Category Decision
Data Quality Targets • How good does the data have to be
• Root cause analysis
Data Consumers • Who needs the data and for what purpose
• What do they need and at what frequency
Survivorship • What should happen when…
Lookups • Which attributes are lookup attributes
• What are the allowable list of values per attribute
• How different are the values across the applications and how do we deal with
inconsistencies
Types of Users and Security • What types of users have to be catered for
• Can they create, update, delete, search
• Can they merge, unmerge
Delete • How should deletes be managed
Privacy and Regulatory • Privacy and regulatory issues

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

pg 19
Reference Data Management
What is Reference Data?
Reference Data enables an
enterprise to make sense of its
data and to turn it into real
business information.
Reference Data are those
codes that categorize data and
enable an organization to
compare that data with
internal and external sources.
What is the Total sales for all Males who are Silver Customers
that live in states on the Eastern Seaboard and are 35-44 years
old?

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

pg 21
Meta Data Management
Enterprise
Goal:
A common glossary
governed at the
enterprise level
LineofBusiness
Application A
Business Glossary includes:
• Common terms, definitions,
business rules, etc.
Conceptual Data Model or
Enterprise Logical Data
Model includes:
• Key business
concepts/subject areas
• Key business
relationships
Application B
Data Model/Dictionary
Data Model/Dictionary
Model to Model

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

pg 23
Data Quality
What is Data Quality?
The planning, implementation and
control activities that apply quality
management techniques to measure,
assess, improve and ensure the
“fitness of data” for use.

pg 24
Data Quality Dimensions
Dimension Key Questions Impact
Completeness  Is all appropriate information readily available?
 Are data values missing or in an unusable state?
 Incomplete data can cause major gaps in data analysis which results in increased manual
manipulation and reconciliation
Conformity  Are there expectations that data values need to reside in specified
formats?
 If so, do all values conform to those formats?
 By not maintaining conformance to specific data formats, there is an increased chance for data
misrepresentation, conflicting presentation results, discrepancies when creating aggregated
reporting, as well as difficulty in establishing key relationships
Consistency  Is there conflicting information about the same underlying data object
in multiple data environments?
 Are values consistent across all data sources?
 Data inconsistencies represent the number one root cause in data reconciliation between
different systems and applications. A significant amount of time by business groups is being
consumed with manual manipulation and reconciliation efforts
Accuracy  Do data objects accurately represent the “real-world” business values
they are expected to model?
 Incorrect or stale data, such as customer address, product information, or policy information,
can impact downstream operational and analytical processes
Uniqueness  Are there multiple, unnecessary representations of the same data
objects within a given data set?
 The inability to maintain a single representation for each entity, such as agent name or contact
information (across all component business systems), leads to data redundancy and
inconsistency, as well as increased complexity in terms of reconciliation
Integrity  Which data elements are missing important relationship linkages that
would result in a disconnect between two data sources?
 The inability to link related records together can increase both the complexity and accuracy of
any corresponding business intelligence derived from those sources. It directly correlates to the
level of trust the business has in the data
Timeliness  Is data available for use as specified and in the time frame in which it
was expected?
 The timeliness of data is extremely important. Data delayed in data denied. Could lead to
reporting delays, providing slate information to customers and making decisions based stale
data

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

pg 26
Reference Architecture: Conventional EDW
Data Quality Business Rules EngineMeta Data ManagementSecurity/Privacy
Acquisition Management Aggregation/
Persistence
Access/Delivery
Staging
(structured
data)
• Cleanse
• Enrich
• Transform
• Create
golden
record
(MDM)
Sources
ODS
Data Mart(s)
Analytics
Data
Services
Other Data
Retrieval
Systems
Archival services
EDW• Internal: HR,
Finance
• External:
Market data,
Credit scores

pg 27
Reference Architecture: How Big Data Fits In
Data Quality Business Rules EngineMeta Data ManagementSecurity/Privacy
DATA GOVERNANCE
Acquisition Management Aggregation/
Persistence
Access/Delivery
Staging
(structured
data)
• Cleanse
• Enrich
• Transform
• Create
golden
record
(MDM)
Sources
ODS
Data Mart(s)
Analytics
Data
Services
Other Data
Retrieval
Systems
Archival services
EDW
Structured:
• Internal: HR,
Finance
• External:
Market data,
Credit scores
Unstructured:
• Sentiment
• Clickstream
• PDF
Semi-structured:
• XML, JSON
Staging
(Semi-
structured &
unstructured
data)
Hadoop

Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata Management
Reference Data
Management
Privacy/Security
DATA GOVERNANCE

Security
Confidentiality
IntegrityAvailability
According to NIST, “The security management practices
domain is the foundation for security professionals'
work and identifies key security concepts, controls, and
definitions. NIST defines computer security as the
"protection afforded to an automated information
system in order to attain the applicable objectives of
preserving the integrity, availability, and confidentiality
of information system resources (this includes
hardware, software, firmware, information/data, and
telecommunications).”
Your Analytics infrastructure and data should comply with the normal InfoSec and
Privacy practices of your organization!

pg 30
Key Security Domains
1. Security and Risk Management: Security, Risk, Compliance, Law, Regulations, and Business
Continuity
2. Asset Security: Protecting Security of Assets
3. Security Engineering: Engineering and Management of Security
4. Communication and Network Security: Designing and Protecting Network Security
5. Identity and Access Management: Controlling Access and Managing Identity
6. Security Assessment and Testing: Designing, Performing, and Analyzing Security Testing
7. Security Operations: Foundational Concepts, Investigations, Incident Management, and
Disaster
8. Software Development Security: Understanding, Applying, and Enforcing Software Security
Reference: (ISC)2

pg 31
Big Data Analytics Privacy
According to U.S. President’s Council of Advisors on Science and Technology, “Big data drives big benefits,
from innovative businesses to new ways to treat diseases. The challenges to privacy arise because
technologies collect so much data (e.g., from sensors in everything from phones to parking lots) and analyze
them so efficiently (e.g., through data mining and other kinds of analytics) that it is possible to learn far
more than most people had anticipated or can anticipate given continuing progress.”*
Some Key Challenges include:
 Difficulty in data anonymization and masking due to sheer volume, number of sources and variety of data
 Collecting information without explicit consent
 Lack of or insufficient data governance practices – according to Rand Secure Archives Data Governance
Survey in 2013, “44% of the respondent have no formal data governance policy”*
 Infrastructure – both on-premise and cloud-based
Reference: MIT Technology Review Custom + Oracle Courtesy of Samra Sulaiman, ConsultData, LLC

Analytics
What
happened?
Why did it
happen?
What will
happen?
How can we
make it
happen?
Diagnostic
Prescriptive
Descriptive
Predictive
Reference: Gartner
Value
Difficulty
Courtesy Samra Sulaiman, ConsultData, LLC

Key Components of an Effective Analytics Strategy
People Process
Technology Data

pg 35
People
Roles Key Responsibilities
Business SME/Manager (decision maker) • Defines the business problem, business objectives
Analyst (explorer) • Specific domain area expert
• Works with raw data
• Creates reports
• Leverages data visualization tools
IT • Sets up infrastructure
• Pre-processes data
• Tests and deploys models
Data Scientist • Develops models
• Performs statistical analysis
• Explores data trends, anomalies

Process
Descriptive
Prescriptive
Prescriptive
Courtesy of Samra Sulaiman, ConsultData, LLC
Typical Analytics Life-cycle:
• Define the business problem—e.g., forecasting future
sales based on past performance
• Design data requirements—e.g., aside from internal data
sources, can data be enriched using external data
sources such as credit scores, social media data feeds?
• Pre-process data—rationalize and cleanse data; apply
the appropriate level of data quality dimensions
• Perform data analytics—data analytics can be performed
using various algorithms or machine learning techniques
to gain insight
• Visualize the results—various tools can be leveraged to
visualize the insight, show anomalies, etc.
Define
Problem
Design
Pre-
process
Analyze
Visualize

pg 37
Technology
Define your use cases before selecting your tools!

Creating “Line of Sight”
• Select use cases: How EDM Impact Analytics
pg 38

From Data to Analytics
Data Insight Action

pg 40
Select Use Cases
How EDM Impacts Analytics:
1. How Data Quality impacts Analytics:
 Demand forecasting
 Sentiment analysis
2. How Meta Data impacts Analytics:
 Glossary
 Lineage
3. How Master Data Management impacts Analytics:
 Hierarchy management

pg 41
How Data Quality Impacts Analytics
Demand forecasting: a retail company wants to forecast future product sales
based on historical data to better manage inventory
Data Quality Considerations:
 Accuracy
 Completeness
 Consistency
 Timeliness
 Uniqueness
 Integrity
 Conformity
Focus: highest quality data

pg 42
How Data Quality Impacts Analytics (continued)
Sentiment analysis: a software product company wants to analyze consumer
feedback quickly after product launch
Focus: relevant data - eliminate ‘noise’ quickly
Data Quality Considerations
• Accuracy
• Completeness
 Consistency
 Timeliness
• Uniqueness
• Integrity
• Conformity
 Relevance (new)

pg 43
How Meta Data Impacts Analytics
• How data flows across the
infrastructure/company?
• How the data is derived?
• How is data transformed?
• Data type changes
• Calculations
• Business/DQ rules
What? Why? How?
• Ability to track upstream/data producers and
downstream/data consumers
• Which system transformed the data?
• How data was transformed (which rules and calculations
were applied)?
• Ability to perform root-cause-analysis – tracing data errors
from a report back to the source
• What data exists today?
• Who owns the data?
• Which system is the ‘System of
Record’ or ‘Trusted Source’?
• Are there standard business rules
for that data?
• Common understanding of available data
• Ability to locate needed data more quickly
• Ability to know who can answer questions about the data
• Ability to trust the data due to the governance process
• Audit trail of who touched/changed a term
• Data quality rules, metrics, etc.

pg 44
How MDM Impacts Analytics
Global
Company
US
Subsidiary
A
Branch A Branch B Branch C
Subsidiary
B
Europe
Subsidiary
C
Branch D Branch E
Business Challenges:
 Regulatory Compliance - e.g.,
inability to uniquely identify all
counterparties to a transaction
 Sales & Marketing - e.g., inability
to roll-up sales by subsidiaries or
by region
 Product – e.g., poor inventory
management due to lack of
product hierarchy and
inconsistent product data

pg 45
How MDM Impacts Analytics (continued)
 Executive sponsorship is critical!
 Business-driven with close collaboration
with IT
 Holistic strategy to avoid re-work later;
leverage existing (funded) projects, if
possible
 Strong Data Governance is key to success
 Iterative process – rapid and continuous
delivery of key capabilities that business
needs

pg 46
A Balancing Act!
Rank Number Topic Asa…. Iwantto… SothatIcan… AcceptanceCriteria
1 16 “Project”Field
identifier
Data
Governance
Lead
identifyfromwhichprojecttheWorkInProgress
businesstermshasoriginatedandfromwhich
projecttheWorkInProgressbusinesstermgroups
hasoriginated.WorkInProgressispartof
UngovernedBusinessTerms.
seewhichprojectteamsareaccountable
forspecificbusinesstermsandbeableto
managethemaccordinglyduringthework
inprogressbusinesstermsection
A“free-form”textfieldtitled“Project”thatcanbecompletedon
thesamepageasthebusinesstermandbusinesstermgroupis
beingdevelopedintheInfoMaptool.
TargetReleaseDate:05/01/15(IT)
Note:-Managingthisviabusinesstermgroupcanbe complicated
sincesomeofthebusinesstermsaresharedbymultipleprojects.
Thisapproachhasbeenrejected.
2 1 DataConcept
Managementin
InfoMap
CGData
Governance
Lead
-beabletoseeallDGdefinedcore,non-core(i.e.BL
governed)&ungovernedbusinessconceptwiththeir
associateddatasubjectanddataconcept
association(pertheCoreConceptsspreadsheet)
mangethecoreandnon-corebusiness
concepts
Flags-CGcore/noncore,BLownership(IM,DIST,SERV,GBSand
otherentitiese.g.PCS,ITG,GBSHR,GBSFIN,etc.),
StandardreportconfigurationthatcanbesharedbetweentheCG
DGL&B/LDGLs.
3 2 DataConcept
Managementin
InfoMap
CGData
Governance
Lead
-Forallcore&non-coreassigntheaccountable,
consultedandinformeddatagovernancebusiness
line&namedowner
managetheongoingdefinition,assignment
ofaccountabilityofDataSubjectsand
ConceptswithinInfoMap
Reportstatusandprogress
ACIassignmentattheBLlevel.
DGL&B/LDGLs.
4 3 DataConcept
Managementin
InfoMap
CGData
Governance
Lead
-seecore&noncore(businessgoverned)data
conceptsthathavenobusinessdefinitionby
businesslineandowner.
assignandmanagethedevelopmentof
coreconceptdefinitionstotheir
appropriateowners.
AddInflight(WIP)GroupwithinGovernedBusinessTerms
DGL&B/LDGLs.
Data Sources
DQ SolutionsYour Data Management
solutions should be
proportionate to your Data
Analytics needs and focused
on business value
Courtesy Samra Sulaiman, ConsultData, LLC

Data Governance Operating Models
pg 47

Data Governance is critical for Analytics
pg 48
You can’t “do” Analytics
without Data Governance
An Analytics initiative is an
important use case for a Data
Governance Office
Must Have “Tools”…
• Documented & enforced data quality policies and
processes to ensure data consistency and standards
• Understood business logic that maps data from
source to target
• Clear data accountability, ownership and escalation
mechanisms
• Continuous measurement & monitoring of data
quality, adoption & value
• Clearly defined data elements, attributes and
computation/derivation of shared data
• Really know your data quality before diving into an
Analytics “Project”
Data Governance is the program
that ensures that the Analytics
content is trusted
pg 48

Operating Model
 Outlines how Data Governance will operate
 Forms basis for the Data Governance organizational structure – but isn’t an org chart
 Ensures proper oversight, escalation and decision making
 Ensures the right people are involved in determining standards, usage and integration of
data across projects, subject areas and lines of business
 Creates the infrastructure for accountability and ownership
Wikipedia: An Operating Model describes the necessary level of business process
integration and data standardization in the business and among trading partners
and guides the underlying Business and Technical Architecture to effectively and
efficiently realize its Business Model. The process of Operating Model design is
also part of business strategy.
pg 49

Process to create an Operating Model
• How are decisions
made?
• Who makes them?
• How are Committee’s
used?
Culture
• Centralized
• Decentralized
• Hybrid
Operating
Model • Data Governance
Owner
• SME’s
• Leadership
People
pg 50

Pros:
 Formal Data Governance
executive position
 Data Governance Steering
Committee reports directly to
executive
 Data Czar/Lead – one person at
the top; easier decision making
 One place to stop and shop
 Easier to manage by data type
Cons:
 Large Organizational Impact
 New roles will most likely
require Human Resources
approval
 Formal separation of business
and technical architectural roles Bus / LOBs
Operating Model - Centralized
DG
Executive
Sponsor
DG
Steering
Committee
Center of Excellence (COE)
Data Governance
Lead
Technical Support
Data
Architecture
Group
Technical
Data
Analysis
Group
Business Support
Business
Analysis
Group
Data
Management
Group
pg 51

LOB/BU
Data Governance Steering Committee
LOB/BU Data Governance Working Group
pg 52
Operating Model - Decentralized
Data
Stewards
Application
Architects
Business
Analysts
Data Analysts
Pros:
 Relatively flat organization
 Informal Data Governance bodies
 Relatively quick to establish and
implement
Cons:
 Consensus discussions tend to take
longer than centralized edicts
 Many participants compromise
governance bodies
 May be difficult to sustain over time
 Provides least value
 Difficult coordination
 Business as usual
 Issues around co-owners of data and
accountability

pg 53
Operating Model - Networked
Pros:
 Flat structure
 Data management is aligned to lines of business and/or
IT ensuring there is a clear understanding of data
requirements for that organizational unit
 Relatively quick to establish and implement
 Known documented connections and RACI charting
creates accountability without impacting an organization
chart
Cons:
 Collaborative decisions tend to take longer to
implement than centralized edicts
 Many participants compromise governance bodies
(making it potentially unruly)
 RACI’s and the Network itself needs to be maintained
 Little enforceable consistency around managing data
across the enterprise
 Difficult coordination Autonomy at the LOB level can be
challenging to coordinate
Data Governance
Office

pg 54
Operating Model - Hybrid
Pros:
 Centralized structure for establishing appropriate direction
and tone at the top
 Formal Data Governance Lead role serving as a single point of
contact and accountability
 Data Governance Lead position is a full time, dedicated role –
DG gets the attention it deserves
 Working groups with broad membership for facilitating
collaboration and consensus building
 Potentially an easier model to implement initially and sustain
over time
 Pushes down decision making
 Ability to focus on specific data entities
 Issues resolution without pulling in the
whole team
Cons:
 Data Governance Lead position is a full time, dedicated role
 Working groups dynamics may require prioritization of
conflicting business requirements
 Too many layers
Data Governance Steering
Committee
Data Governance Office
Data Governance Working Group
Business Stakeholders IT Enablement
Data Governance Organization

pg 55
Operating Model - Federated
Pros:
 Centralized Enterprise strategy with decentralized
execution and implementation
 Enterprise Data Governance Lead role serving as a single
point of contact and accountability
 “Federated” Data Governance practices per Line of
Business (LOB) to empower divisions with differing
requirements
 Potentially an easier model to implement initially and
sustain over time
 Pushes down decision making
 Ability to focus on specific data entities, divisional
challenges or regional priorities
 Issues resolution without pulling in the
whole team
Cons:
 Too many layers
 Autonomy at the LOB level can be challenging to
coordinate
 Difficult to find balance between LOB priorities and
Enterprise priorities
Enterprise Data Governance Steering
Committee
Enterprise Data Governance Office
Data Governance Groups
Data Governance Organization
Business
Stakeholders
IT Enablement
Divisional DG
Office
Business
Stakeholders
IT Enablement
Divisional DG
Office
Business
Stakeholders
IT Enablement
Business
Stakeholders
IT Enablement
Divisional DG
Office

Operating Model Roles and Responsibilities
 Data Governance Steering Committee
− Provides overall strategic vision
− Approves funding, budget and resource allocation for strategic data projects
− Establishes annual discretionary spend allocation for data projects
− Adjudicates intractable issues that are escalated
− Ensures strategic alignment with corporate objectives and other business unit initiatives
 Data Governance Office
− Chairs the Data Governance Steering Committee and Data Governance Working Group
− Acts as the glue between the Data Governance Steering Group and the Working Committee
− Defines the standards, metrics and processes for data quality checks, investigations, and resolution
− Advises business and technical resources on data standards and ensures technical designs adhere to data architectural best practices
to ensure data quality
− Adjudicates where necessary, creates training plans, communication plans etc.
 Data Governance Working Group
− Governing body comprised of data owners across Business and IT functions that own data definitions and provide guidance &
enforcement to drive change in use and maintenance of data by the business
− Validates data quality rules and prioritize data quality issue resolution across the functional areas
− Trains, educates, and creates awareness for members in their respective functional areas
− Implements data business processes and are accountable to decisions that are made
pg 56

Aligning Governance and Analytics
pg 57

Example: Data & BI Governance Structure
Accountable for Governance and Change Leadership
for Data & BI across Company
• Executive Data & BI Owner
• Forum Chair
• Membership – Executive Process Owners
• Meeting Cadence - Monthly
Data & BI Governance
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
• BI Practice Lead
• Membership – Functional Reporting Leads
• Meeting Cadence – Bi-Weekly
Business Intelligence
Forum
Strategy & Guidance
Agreed Decisions
Strategic Initiative Alignment
Initiative Requests
Project / Initiative Progress
Intractable Issues
pg 58

Example: Data & BI Governance Forum Roles
 Forum to strategize, plan and review Master Data initiatives led by team members
 Forum to drive Company’s Performance Measurement Architecture development
 Forum to discuss & define strategic direction impacting policy, process & technology
 Data & BI decision forum for Project X as well as other Corporate initiatives
Market to
Sell
Idea to
Offer
Finance
World Wide
Operation
Hire to Retire
Issue to
Resolution to
Prevention
FP&A IT
Data & BI Governance Leadership Forum
Forum Chair – Data Governance Sr. Director
Process Owner VP SALES SVP Product Strategy CFO SVP ServicesVP HR SVP Finance VP Info Tech
Data Ownership Customer Product Chart of Acct Vendor Employee
Executive Data & BI Owner – EVP XXX
Executive Process Owner’s represent the Functions within their Process Domains
• Active participation is critical to our success
• When necessary, delegation to peer Functional Owners is acceptable
VP WWOPS
pg 59

Example: Analytics Operating Model – Immediate
IT Advisor
Enterprise Infrastructure
Committee
Executive
Office
(CEO)
Strategy &
Risk
(CRSO)
IT
(CIO)
Accounting
(CAO)
Global
Services (COO)
PMO
Head of Business Analytics
CEO
Credit Analytics Client Analytics Market Analytics
LOB … LOB … LOB …
Data Stewards
Executive Sponsor Analytics
CFO
pg 60

Example: Analytics Operating Model – Long Term
IT Advisor
Committee
Executive
Office
(CEO)
Strategy &
Risk
(CRSO)
IT
(CIO)
Accounting
(CAO)
Global
Services (COO)
PMO
CFO
Head of Business Analytics
Analytics Working
Group
LOB Reporting
LOB … LOB … LOB … LOB … LOB …
CEO
Credit Analytics Client Analytics Market Analytics
Analytic Directors
Data Stewards
pg 61

Example: Data Governance and Analytics
Sponsor - Data Governance
Business Steward Leads
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
CreditAdmin 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
Head of Business
Analytics
Analytics Working Group
Analytic Directors
Credit
Analytics
Client
Analytics
Market
Analytics
CEO This is a role / relationship chart and NOT an
organization chart
Data Stewards
pg 62

Example: Data Governance and Analytics
Sponsor - Data Governance
Business Steward Leads
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
CreditAdmin 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
Head of Business
Analytics
Analytics Working Group
Analytic Directors
Credit
Analytics
Client
Analytics
Market
Analytics
CEO This is a role / relationship chart and NOT an
organization chart
Data Stewards
pg 63

OCM Basics
pg 64

EIM Means Change
 Successful EIM means a change to the information management
culture, processes and policies
 Changing that culture means that you are asking people to think and
behave differently about how data is accessed and used
 You need an organized and systematic way to manage and sustain
those changes – or there is marginal likelihood of success
pg 65

Two Sides to Change Management
WHO? WHAT? WHEN?
WHERE? WHY?
 Something old stops, and
something new starts
 Relatively easy to plan for
and anticipate
SITUATIONAL
REORIENTATION PEOPLE GO THROUGH AS
THEY COME TO TERMS WITH THEIR NEW
SITUATION
 It’s important to help affected individuals let go
of the old situation and get comfortable with
the new way
 Everyone processes at a different rate and are
rarely aligned with the milestones of the
implementation plan
PSYCHOLOGICAL
For change to be successful, BOTH sides need to be addressed
pg 66

Why Do People Resist Change?
 Loss of identity and their familiar world
− Loss Analysis
 Disorienting experience of the transition between the old and the new
 Weak/no sponsorship by executive leaders and managers
− Lack of alignment
− No involvement
 Overloaded with current responsibilities
 No answer to WIIFM
 No involvement in the crafting the solution
 Each individual’s capacity to handle change
 Other work and personal issues
 How well an organization has handled changes in the past
67
pg 67

68
What Might Resistance Look Like?
 Trying to outlast the changes: bargaining for exemption from new policies or processes
 Reduction in productivity and missed deadlines
 Going back to the old way of doing things
 Lack of attendance in project status meetings and events, or training
 Higher absenteeism
 Open expression of negative emotion
 From executives, resistance could be:
− No visible sponsorship of data governance; no open endorsements
− Refusal or reluctance to provide needed resources and/or information
− Repeatedly canceling or refusing to attend critical meetings
pg 68

69
Getting People through Change Successfully Requires….
 Clear definition of what is changing
− Make sure the new behaviors, skills and attitudes are clearly defined and
communicated
− Provide examples, training and allow time for practice
 Attention to feedback:
− What are people saying and how are you addressing it?
− You must respond to feedback; be sure and attach the actions you take to
the feedback you received so that associates know you are listening
 Some reward or recognition structure to encourage new behaviors
 Measurement and performance management
pg 69

Communication Framework to Drive Change
 State the aspiration,
the BHAG (Big, Hairy,
Audacious Goal)
 What is the desired
future?
 What is the value of
the future state to
the company?
 How does that
future state move
forward the overall
business?
High-level, strategic
statement of a Goal
Vision Picture Plan Participation
 Paint the picture of
how the future will
look and feel once
Data Management
is implemented.
 How are people
going to get their
work done and
interact with each
other?
 How will a day be
organized?
Future State
Principles
 Lay out the plan for
achieving the future
state; the steps and
timeline in which people
will receive information,
training, and the
support they need to
transition to the future
 Orient managers to tell
employees how and
when their worlds are
going to change
 Start with where people
are and work forward
 What does this mean to
me?
Overall Roadmap
Group specific
roadmaps
 Establish each person’s
part in both the future
state and the plan to
get there
 Show associates their
roles and relationships
to each other in the
future
 Show associates what
part they play in
achieving the future
and the transition
process to get there
 All this helps them let
go of the past and
focus on the future
 What is my role?
Who does What
Across Enterprise
Group Specific
Adapted from William Bridges, Managing Transitions
 Explain why we’re
doing what we’re
doing - the purpose
behind the
outcome
 What is/was the
problem?
 Who said so and on
what evidence?
 What could occur if
no one acts to solve
it?
 What could happen
if that occurs?
Why you are
executing your
Vision
Purpose
pg 70

Change Management Phases
 Organizational alignment implemented
 Structure
 Jobs/people
 Policies/procedures
 Incentives
 Performance management
 Change integration/adoption assessment
 Communication plan execution
 Training development and delivery
 Feedback and analysis of results
 Leadership alignment checkpoint
 Measurement approach & metrics
 Organizational impact analysis
 Resistance management
 Implementation checklist development
 Information gathering and analysis
 Stakeholder Analysis
 Sponsorship development
 Change plan development
 Leadership alignment checkpoint
 Communications planning
 Training needs assessment and planning

Meet with
Program/Project
Manager, and lay out CM
Approach for the
Program/Project
Monitor & RefineExtend
Change Management Alignment to EIM Phases
****Communication Launch
Information Gathering and Analysis
Stakeholder Analysis/Loss Analysis
Change Readiness Assessment
Leadership Alignment
Sponsorship Development: Assessment
and Road Map
Detailed Change Planning
Communication Plan
OperationalizeImplementStrategize & PlanAssess & Align
Project
Initiation
Planning for Change
****Collect, Analyze and Report on Feedback
Implementation Checklist
More Leadership Alignment
Metrics and measurement
Org Impact Analysis: structure, jobs, training, policies
Managing Change
****Lesson Learned Assessment
Organizational Alignment Action Plan
Change Integration Checklist
Transitioning to the Business
Implementing/ Sustaining Change

The Potential Value of OCM to Your Business
 Can result in real monetary value to the business
− Acceleration of planned changes
− Faster realization of planned benefits
− Minimizes business disruption: loss of staff, lower productivity, etc.
 Greater likelihood that the IM/DG changes implemented will be sustained

The Correlation is There
There is data that shows a strong correlation between
effectively managing change and meeting objectives
− “Show me the numbers”
Analysis from:
− Prosci’s Best Practices studies
− McKinsey studies
− Your own organizational experience?

2002 McKinsey Study
 Examined 40 projects
 Evaluated
− ROI expected
− ROI delivered
− Level of change
management
effectiveness
Results
 Direct correlation between change
management effectiveness and gap
between ROI expected and ROI delivered
 Those that were above average on those
factors realized 143% of expected value
 Those that were below on all three factors
realized 35% of expected valueFrom the article “Helping Employees Embrace
Change”, McKinsey Quarterly 2002 Number 4,
Jennifer A. LaClair and Ravi P. Rao

Using reorganization as an opportunity to change
mind-sets and behaviors of the workforce
Focusing as much on how the new organizational
model would work as on what it looks like
Accelerating pace of implementation to make the new
model deliver value as soon as possible
Addressing all risks and bottlenecks as early as
possible, before and during implementation
Developing a clear communication plan for all
internal and external stakeholders
Ensuring that IT, financial, human resources, and
other systems were updated to support new
organizational model
Defining detailed metrics for reorganization’s
effect on short and long-term performance
and assessing progress against them
KEY STRATEGIES KEY PROCEDURES
2010 McKinsey Reorganization Study
Taking Organizational Redesign from Plan to
Practice, McKinsey Global Survey Results, 2010
Courtesy of McKinsey & Company

Contributes to: Achieving Project Objectives…

Consider:
 There is a direct and profound effect that
a strong change management program
bears on an organization’s ability to
meet or exceed its project objectives
 95% of those who rated their change
management program as excellent met
or exceeded their project objectives as
opposed to only 17% of those who rated
their change management program as
poor or non-existent

pg 79
Contributes to: Staying on Schedule…

pg 80
Consider:
 There is also significant correlation
between the quality of the change
management program and the project’s
ability to stay on or ahead of schedule.
 75% of those respondents with excellent
change management programs had
projects that were on or ahead of
schedule

Poorly Managed Change Results in:
 Lower productivity
 Resistance
 Turnover of valued employees
 Apathy for the future state
 Arguing about the need for change
 More people taking sick days or not showing up
 Changes not fully implemented; benefits not achieved
 People finding work-arounds or reverting to the old way of
doing things
 The change being totally scrapped
 Divides are created between ‘us’ and ‘them’

pg 82
Bottom Line…
There is significant correlation
between the effectiveness of a
change management program
and achieving Data
Governance results

Five Key Factors for DG Success
 Executive Sponsorship
 Aligned leadership
 Clear communication (early and often)
 Stakeholder Engagement
 Measurement
83© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Final Thoughts
Be absolutely clear and specific about what is
changing and what that will mean in terms of required
behavior changes: people can’t change behavior if
they don’t know what they’re supposed do
differently.
Appreciate that there is a psychology to change:
understanding how people react is essential to
structuring your initiative to deal with it.

Survey Results
pg 85

Descriptive Analytics Program
• Descriptive
Analytics/BI
is still going
strong

Predictive Analytics Program
• Predictive
Analytics is
still emerging

Relationship of the Programs
• Majority are
under the same
umbrella
• Very little
outsourcing of
overall Program

Predictive Analytics Investment
• Big Market
Opportunity

Descriptive Analytics Investment
• It’s not
“either/or”,
both are
receiving
investment

Organizational Construct
• All Analytics
will be
managed
together

Measuring Effectiveness of Descriptive Analytics
• Improved data is
a measurement
of effectiveness
• Usual suspects of
better decision
making, better
understanding of
results, improved
processes

Measuring Effectiveness of Predictive Analytics
• Very similar
to Descriptive
Analytics

Relative Measures of Effectiveness
• No comparison
between the
programs,
although they
appear to be
seeking a
similar outcome

Take-Aways
• Organizations still struggle for skilled
resourcesResources
• Descriptive BI isn’t going awayInvestment
• Optimization needs to occur across
Descriptive and Predictive BIOutcome

Wrap-Up
pg 96

Approach to practical, affordable analytics
Identify efficiency and
operational metrics for BI
Analytics environments
Confirm scope
and seed Analytics &
Metrics model
Define cost of ownership
and operating standards
Synthesize and
map to
benchmarks
Develop and
present efficiency
sustaining plan
Rationalize metrics and
predictive models
Align metrics to
business
Develop transition
plan to unified
metrics
Data Efficiency Corporate Metrics
Analytics / BI
Architecture
Sustaining
Plan
pg 97

98
Kelle O’Neal
kelle@firstsanfranciscopartners.com
415-425-9661
@1stsanfrancisco
Thank you!
Samra Sulaiman
samra.sulaiman1@gmail.com
202-320-9764

Who Took the Survey?

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Executive Management
Finance Management and / or Reporting
Content and / or Digital Asset Management
Application Development
Data and / or Information Architecture
Business Intelligence and / or Analytics
Information / Data Governance
Corporate Research and / or Library
Marketing and / or Market Research
IT Management
Software or System Vendor
Other
Job Function
Demographics

Business Intelligence v Data Science

Enabling an Analytics-Driven Organization

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