CDO Slides: Real World Data Strategy Success Stories

The First Step in Information Management
www.firstsanfranciscopartners.com
Produced by:
Real World Data Strategy Success Stories
Monthly CDO Webinar Series
Brought to you in partnership with:
#CDOVision
November 3, 2016

Agenda
§ What is success?
§ EIM strategy success stories
§ Reporting/data-centric execution success story
§ Data governance success story
§ Governance and MDM success stories
Sponsored by:
© 2016 First San Francisco Partners www.firstsanfranciscopartners.com pg 2

What is success?
§ Define “done”
§ Potential benefits that apply across EIM
− Raising visibility of data to an organization
− Pulling vision together
− Become more data-centric
− Improving efficiency and productivity
− Reduced costs of IT, processes, data, etc.
− Improved cost and delivery of projects
pg 3© 2016 First San Francisco Partners www.firstsanfranciscopartners.com
Sponsored by:

Success Story:
Enterprise-wide Information Strategy 1

Challenge: Large energy company
realized cost of data mismanagement
40 million in SAP development
Massive data clean-up
Upheaval across organization
No trust in data
Still no data consistency or
reliability
Costly denial of rate increase
Inability to adapt to smart grid
evolution
Sponsored by:

Solution: Data-centric enterprise model –
data management/governance engagement
Senior Executives All Business
Management
Customers
Future Users
All Business
Area Staff
Vendor
Product
Management
All DBAs
External
Data
Suppliers
Executive Sponsorship
Senior IT Management
Senior Business Management
Support Desk
All Intended Users
Application
Development
IT Operations
Management
Vendor Account
Management
Vendor Product
Support
PMO
Key Testing Users
Training Development & Execution
Source System
Technical Application Admin
Business Data
Subject Experts
Dashboard
Designers and
Developers
Project Management
Information Architects
Data Stewards
Data Warehouse Architect
Business Unit Information
Managers
Core Team à Extended Team à Involved Individuals à Support
Dashed lines indicate direct engagement of Data Governance
Ad hoc Query-
Builders
Stewards and Owners
Sponsored by:

Outcome: Flexible and adaptive architecture
App1
App2
App3
App4
App5
App6
App7
Appn..
Apps
Physical
Databases Current Physical
Views
SAP
Other
Reverse Engineering Forward Engineering
Abstraction
Processes
“Mapping”
Views
Meta Data Layer
Semantics Measures Data Assets Work Flow Rules
Future Logical
Views
Future Physical
DBMS
Future
Apps
Access layer - DW,ODS, Web Services, Analytics, etc.
1
2
3
1
2
3
31
2
Data Management
Synchronize to other data
3
2
1
pg 7© 2016 First San Francisco Partners
Sponsored by:

Results and takeaways
§ Results
− Reorganization around data
− Improvement in operational application use
− Improvement in data quality for reporting
− Deployment of change teams
§ Takeaways
− Non-standard operating models are OK
− Improved data quality is key to application usage
− Middle management embraced changes
− Solution architecture can be a hybrid of old and new
Sponsored by:
Lessons
learned…

Success Story:
Enterprise-wide Information Strategy 2

Challenge: Large specialty retailer
faced with huge operational issues
Multiple product data stores
Inconsistent merchandising
Destructive data redundancy
Ancient applications and
infrastructure
No trust in data
Unreliable inventory
Less agile in their market
Stock price explicitly affected
Sponsored by:

Message Processing Layer
Data Access Layer (DAL)
Publish/
Subscribe
Request/
Reply
Message
Queuing
Bulk/
Batch
Publish/
Subscribe
Request/
Reply
Message
Queuing
Bulk/
Batch
ODBC
Applications Portfolio
Application Application
End
Point-to-Point
Interfaces
LOCAL DATA
STORE
Application Application Application
LOCAL DATA
STORE
Enterprise Data Stores
(Physical and Logical)
ODS/DW
Transaction Data
Operational and
Analytical Data
ANALYTICAL DATA
TRANSFORMATION
DM
Activity Product Customer Store
Enterprise Data Stores populated over time to establish Single
Version of the Truth
Application Services
Technology
Services
Data Services
Canonical Model
Metadata
Repository
EIS
CUBE
Solution: Full reload of data architecture
Sponsored by:
pg 11

Analytics
Reporting
Transaction
processing
Data
capture
Merchandising/SupplyChain
Distribution/Manufacturing
NewBusinessDevelopment
StoreOps/Sales
Marketing/BrandMgmt
Accounting/Financials
RadioShack.com
Systemsupport
ENTERPRISE
ARCHITECTURE
DIRECTION
Application “stovepipes” aligned
with functional organizations
Enterprise Architecture
Flexible application layers mapped to
major business processes
• Applications aligned with core business
processes
• Flexible reporting and analytics respond to
business changes
• Reuse of common business functions
simplifies creation and maintenance of
applications
• Build analytical capability across enterprise
• Data-centric focus enables learning
organization
Data capture
Transaction Processing
Enterprise Data Stores
Reporting
Analytics
Logical
Physical
• Applications are rigid and do not easily
provide cross-enterprise functions or
information-centric views
• Data capture and transaction
processing capabilities do not promote
speed
• Reporting and analytic capabilities are
not flexible enough
Core processes
App
App
App
App
Solution: Followed enterprise architecture
Sponsored by:

§ Results
− Creation of a top data job (TDJ)
− Implementation of information management area
− Refresh of data warehouse and reporting technologies
− Created change teams
§ Generated internal accountability for successful change
§ Takeaways
− Engage enterprise architects in a solution
− Engage with executive team and consider a TDJ
Lessons
learned…
Sponsored by:

Success Story:
Data-Centric Project Execution of
Reporting/Analytics

Challenge: Reporting project
Data is stored as a byproduct of the
applications that capture the data, not in a way
that is accessible to the business user
Data descriptions are not centrally defined
resulting in inconsistent usage and
understanding of the data
Data transformations are complex and only
understood by IT resource
Formal data quality monitoring
practices do not exist
Lack of business ownership
Lack of trust in data
Inability to do analytics
Business is forced to rely only
on gut and intuition vs. making
informed, fact-based decisions
Low level of data
management maturity
Sponsored by:

Outcome: Reusable, subject-specific data marts
Source
Application
Database
Abstraction
Layer (inc. DQ &
ETL)
Business View of Data
Today: Data is stored as a byproduct of the
application
vs.
Future: Data is stored in a way that is optimized
for business retrieval
§ Data strategy execution is aligned to
project engagement
§ Reporting project provides an opportunity
to improve the way that data
requirements are captured and source
data is analyzed
Sponsored by:

§ Results
− Fact-based decision-making
− Improved data analytics capabilities (advanced and
predictive analysis)
− Business ownership of information assets
§ Takeaways
− Implementing new data standards and guidelines via a
project grounds governance in reality
− Adjusting an “SDLC” to be data-centric catches potential
data discrepancies before they become issues
− Incremental change is easier – especially when aligned
with a tangible outcome
Lessons
learned…
Sponsored by:

Success Story:
Governance

Goal: Improved coordination of
enterprise-wide data governance
Business Problems
• Business decisions are being
made on unreliable data sources
due to lack of understanding of
data definitions, calculations and
lineage
• Data governance is centered
around discrete business lines
and not across the enterprise
• Business and IT have no common
understanding of data sources
and their usage, resulting in
increased effort to verify and
remediate
Desired Business Outcomes
• Delivering the right data across different levels of the organization through the consistent execution of defined processes
spanning the complete data life cycle
• A baseline of data governance practices, process and metrics implemented across all business lines
• A discrete set of Core Data Concepts governed through a federated approach
• A roadmap of improvement initiatives that will drive continuous business improvement
Sponsored by:

Solution: Approved data
governance accountability
Steering Committee
Data Governance Council (DGC)
CRM Reporting ERP
Enterprise
Department – Local Data Governance (LDG)
Example
Projects/
Applications
PMO
Enterprise
Architecture,
Architects
Projects and
Programs
Existing Structures
Metadata
1% at the SC
Escalation/Resolution
19% at the DGC
80% at the LDG
OPS SERV SALES HR FIN
OPS DG WORKING
GROUP
FIN DG WORKING
GROUP
SERV DG
WORKING GROUP
SALES DG
WORKING GROUP
HR DG
WORKING
GROUP
Sponsored by:

Outcome: Aligned data governance
roadmap and actions
pg 21
Sponsored by:
Data
Concepts
Data Handing
Standards
and Processes
Oversight
2016 2017
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Business Benefit
Realized
Projects
Process with Projects : concept
and infomap engagement refined
BOR evaluation process engaged
DG Metrics integrated with
project process (estimated)
Modify SDLC process
for integration with
solution planning
gates for DG and PMO
oversight
DG Program Operations
Change Management Activities
Refine processes/Release 1 Refine and Release 2 Refine and Release 3 Release n
Concepts and Metadata
standard (InfoMap)
BOR
process
standard
DG Metrics
standard Other high-interest topics
Launch DG practices with Aligned LDG Roadmaps
Refine Metrics Metrics program
implementation
Refine Architecture with DG involvement with projects
Privacy and Security review
Potential
DGC or LDG
focus
Implement Rotating DGC Chair
Metrics program operations
Align Local Data
Governance to standard
roles, processes, and
roadmaps

§ Results
− Rolled out the concept of a minimum viable state of
governance within each division
− Defined a set of critical data concepts where previously
couldn’t agree
− Enabled improved data sharing and consistency
§ Takeaways
− A minimum viable state is still progress
− Create a flexible, culturally-tuned operating model to ensure
it is adopted – a referee body can be acceptable vs. a
centralized authority
− Focus on high-priority areas to make incremental progress
Lessons
learned…
Sponsored by:

Success Story:
Governance and MDM 1

Challenge: taking a business-
driven approach to MDM
Align MDM
Requirements to
Business
Requirements
Assess Data
Align Data
Governance
Sponsored by:

Solution: Roadmap prioritization
based on use cases
Value
Effort
HighLow Med
High
Low
Med
1
17
16
15
14
13 12
11
10
9
8
7
6
54
32
MDM Use Cases:
1.Unique Global Identifier
2.3600 View
3.Customer Groupings
4.Customer Reference Data Management
5.Data Quality at point of entry
6.Data Harmonization
7.Centralized Stewardship
8.Centralized Onboarding Repository
9.Master Data Enrichment
10. Customer Documentation Linkage
11. Event Based Data Remediation
12. Mastering Privacy & Preference
13. Security & Access Management
14. Mastering Customer/Account Relationships
15. Data Quality Dashboards
16. Audit & History
17. Early Warning System
Client-centric strategic initiatives, such as the Consumer Online effort and business use cases that were
documented as an outcome of the requirements gathering phase, formed the foundation for the layout and
scheduling of the roadmap.
Sponsored by:

Roadmap: Integrated MDM evolution
Enterprise MDM will deliver incremental value to the business – first in support of In-Flight Programs such as Consumer Online, and
then expanding to incrementally deliver on the program objectives and requirements across the enterprise.
Deploy
Build
Enhance
Pilot
§ To engage and confirm the decision
makers able to define customer data
attributes and processes
§ Confirm existing definitions and
decisions regarding customer data
§ Install INFA MDM in non-production
environments (NP2/NP3)
§ Develop foundational customer master
data model
§ Onboard deposits, lending (consumer)
& FSC customer SORs on to MDM
§ Develop foundational data quality &
de-duplication rules
§ Develop core MDM web services to
support consumer online development
§ Continuous improvement to mastering processes,
assess effectiveness of rules, measure
improvements and extend across organization
§ Augment MDM to enable capture of customer
privacy & preference
§ Onboard remaining SORs – FRIM, FX
§ Develop ETL for IDL and delta for additional sources
§ Finalize data quality & de-duplication rules
§ Configure hierarchy manager (HM) to support
customer/account relationship management
§ Augment MDM to enable capture of existing
Householding relationships (tax ID & mega-
household)
§ Refine data stewardship tools & capabilities (IDD)
§ Enhance MDM web services to deliver full CrUD
capabilities and conform to augmented data model
§ Develop outbound data feed for downstream
consumption
§ Execute system integration testing
§ Execute UAT
§ Deploy to Production
§ Create foundation for centralized
master data creation
§ Align business processes to
consume master data
§ Manage data quality at the
source
§ Augment MDM to capture
broader & other flavors of
householding relationships
§ Augment HM to support
householding
§ Implement business rules & ETL
to derive and populate
Householding relationships
§ Leverage BPM to develop data
stewardship workflows
§ Augment stewardship
capabilities (IDD)
§ Commence harmonizing
mastered customer data with
SORs
§ Determine MDM rules, create
foundational processes and identify initial
measurements
§ Identify and define master relationships
§ Expand foundational data model to
include all customer demographic and
relation attributes
§ Onboard additional sources – Lending
(Commercial) & FRTC
§ Develop ETL for IDL and delta for all
onboarded sources
§ Deliver stewardship capabilities (IDD) for
match resolution, centralized data
maintenance & DQ dashboard
§ Enhance data quality & de-duplication
rules
§ Build foundation for relationship
management
Q1 Q2 Q3 Q4
Use
Cases
1
17
16
15
14
13
12 1110
9 8
7 6
54
3
2
24
9
13
1316
15
16
Change Management, Communication , Training and Awareness
Sponsored by:

EDCI Group
Operational/Execution Group
Data Steward
Data Governance Council
(Business Data Owner/Data Governance Lead)
DO - Customer DO - Products DO - Finance
Enterprise Data Architect
Chief Data Officer/
Executive Sponsor
Group/
Individual
IndividualRoles
Operating
People/Process
Data Custodian
Data Users
Illustrative
MDM#Design#Team#
MDM#Opera/ons#Team#
Business#SMEs#
Master#Data#
Stewards#
IT#MDM#Opera/ons#
Team#
MDM#Architect#
MDM#Delivery#
§ Results
− Business accountability for
master data aligns expectations
and outcomes to deliver
business value
− Prioritized use cases based
on business needs ensures
master data usage and value
§ Takeaway
− Mastering data is a business process and should be treated as such Lessons
learned
Sponsored by:

Success Story:
Governance and MDM 2

Challenge: Support business
growth through MDM and DG
Revenue Growth
• Small increase in cross-sell/up-sell
success results in appreciable
increase in overall premium volume
Profitability
• Account view improves decision-
making and pricing ability
Improved Customer Experience
• Improve Net Promoter Scores and
customer retention
Improved Customer Knowledge
• Creation of 360o view of Customer
to aid analytics
Regulatory Compliance
• Consistent application of data
classification standards across
organization
Employee Satisfaction
• Enabling employees to be more
effective and efficient - increases
satisfaction
pg 29
Sponsored by:
© 2016 First San Francisco Partners www.firstsanfranciscopartners.com

Solution: Incremental value
through MDM Roadmap
Tactical
Operations
Strategic
Operations
Operational
Analytics
Predictive
Analytics
Improved
Capabilities
Time
Sponsored by:

Today Year 1 Year 2 Year 3 Year 4 Future
Today
Build
Complete
Operationalize
Complete
Prepare
Complete
Approval
PMO Process
Prepare
Prep Hub
Data Quality (DQ)
Data Governance (DG)
Build
Personal Lines
Flood
Commercial
Outbound Integration with DW
Implement & Integrate CRM
Operationalize
Establish DQ metrics
DQ Mgmt for Party
Establish DG metrics
Data Stewardship for Party
Establish MDM Metrics
Monitor Metrics
Change Leadership, Training & Communication
Outcome: Business-aligned, long-term plan
Sponsored by:

§ Results
− Alignment with a strategic initiative ensured funding and
business involvement
− Iterative implementation ensured the pace reflected the
available funding
− Clear goals and expectations rallied stakeholders
§ Takeaways
− MDM in isolation creates a challenge; aligning to a strategic
effort is a good approach
− Constraints will exist, plan accordingly
− Follow the 5-Step process to align a data-driven culture
§ Vision à Purpose à Picture à Plan à Participation
Lessons
learned…
Sponsored by:

Questions

Thank you!
John Ladley
john@firstsanfranciscopartners.com
Kelle O’Neal
kelle@firstsanfranciscopartners.com
Next in the CDO Vision series:
December 1, 2 PM ET
2017 Predictions

CDO Slides: Real World Data Strategy Success Stories

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