SG Data Mgt - Findings and Recommendations.pptx

Smart Grid Data Management
Assessment
Summary of Findings & Recommendations
February 10, 2011

Contents
Introduction and Context
Approach
Assessment Highlights
Recommendations
Appendix
2

Key Factors driving the need for a SmartGrid
data management strategy
• Provide information for the utility & the customer to make choices that take
advantage of energy alternatives and efficiencies, regarding both production
and consumption.
• Operate and maintain assets based on up to date, fact based performance
data, enabling the evolution from preventative and reactive to predictive and
self healing and for more efficient use of resources.
• Manage exponential data growth and data velocity from various intelligent
devices (like sensors and meters) in the field and in the customer premises.
• Manage risk and compliance via a single version of the truth for key
business data such as Customer, Work, Asset, Location, Operations.
3
We need a coordinated data management strategy to enhance
information capabilities and enable the Smart Grid vision to:

Contents
Approach
Recommendations
Appendix
4

SmartGrid Data Management Assessment
Approach
5
Inputs
Business Vision
Objectives &
Strategy
IT Vision
Objectives &
Strategy
Existing Data
Environment
Business & IT
Environment
Activities
1. Scope the Assessment
2. Gather Pain Points
3. Assess the current
information maturity level
4. Determine future desired
information maturity level
5. Identify the gaps
6. Develop Options &
Recommendations
7. Develop framework
8. Document and present
findings &
recommendations
Result
Current State
Business & IT
Environment
Desired State
Business and IT
Environment
Assessment
- Findings
- Recommendations
(data related
initiatives)
- framework

Scope
6
• Key Smart Grid Projects, Existing Applications, ODS &
other data stores for reporting & analysis
NGDR
(AMI, MDMS, DR)
CBM
PEC
DSDR
PEF
DSDR
OEVC
CIS
(CSS, CIM)
Work Mgt
(Work 2000,
STORMS)
OMS
(ABB’s CADOPS,
Intergraph’s In-
Service)
DSCADA/
DMS (Telvent)
GIS
(Intergraph’s G
Tech)
EDSI
CYME
(Engineering
Analysis)
OMS ODS
Customer
Reporting
Smart Grid Projects Existing Applications
Data Stores
(Reporting & Analysis)

Approach – Information Maturity Model
7
IBM Information Maturity Model
Business
Value
of
Information
Information Management Maturity
• Data: All relevant internal and
external information seamless and
shared. Additional sources easily
added
• Integration: Virtualized Information
Services
• Applications: Dynamic Application
Assembly
• Infrastructure: Dynamically, re-
configurable; Sense & Respond
• Flexible, adaptive business environments
across enterprise and extra rise
• Enablement of strategic business innovation
• Optimization of Business performance and
operations
• Strategic insight
• Data: Seamless & shared; Information
separated from process; Full integration of
structured and unstructured
• Integration: Information Available as a Service
• Applications: Process Integration via Services; in
line bus apps
• Infrastructure: Resilient SOA; Technology
Neutral
• Role-based, work environments commonplace
• Fully embedded capabilities within workflow,
processes & systems
• Information-enabled Process innovation
• Enhanced Business Process & Operations
Management
• Foresight, predictive analytics
• Data: Standards based, structured & some unstructured
• Integration: Integration of silos; Virtualization of
Information
• Applications: Services-based
• Infrastructure: Component/Emerging SOA, Platform
Specific
• Introduction of contextual, role-based, work
environments
• Enhanced levels of automation
• Enhancement of existing processes and
applications
• Integrated business performance management
• Single version of truth
• Insight thru analytics, real-time
• Data: Structured content; organized
• Integration: Some integration; silos still remain
• Applications: Component-based applications
• Infrastructure: Layered Architecture, Platform
Specific
• Basic search, query,
reporting and analytics
• Some automation
• Disparate work environments
• Limited enterprise visibility
• Multiple versions of the truth
• Data: Structured content, static
• Integration: Disjointed, Soloed, non-
integrated solutions
• Applications: Stand alone modules;
application-dependent
• Infrastructure: Monolithic, Platform Specific
• Basic reporting &
spreadsheet- based
• Manual, ad hoc
dependence
• Information overload
• No version of truth
• Hindsight based
Information as a
Competitive
Differentiator
Information to
Enable Innovation
Information as a
Strategic Asset
Information to
Manage the
Business
Data to Run the
Business
1
2
3
4
5
© Copyright IBM Corporation 2005

Contents
Approach
Recommendations
Appendix
8

9
Highlights – Areas which PGN is addressing
• PGN has standardized on SPARX Enterprise Architect for UML modeling and Erwin
for data modeling.
• PGN has established Distribution Data Integrity team for PEF and has had a
centralized Distribution Data & Services Design team for PEC to address operational
data integrity issues.
• PGN has started the use of the Utilities Common Information Model (CIM) for
modeling interfaces in the DSDR PEC project and this has been also adopted for
Catalyst in Florida and PE.com.
• DSDR PEC project has created DAS data mart to consolidate data and key learnings
and information architecture artifacts can be leveraged from this effort
• There has been progress in developing foundational information architecture artifacts
such as Information Architecture guiding principles and Information architecture and
design patterns.
• Enterprise integration standards, technology platforms and tool sets are newly
established. Existing integration technologies are being inventoried and being
rationalized.

Smart Grid Data Management
Maturity Assessment
10
Technology Data
Information
Process
Governance Information Use
 Enforcement and
controls for data
quality not always
built into data input
process
 No metadata
repository
accessible to both
business and IT
 Standards tools are
established for
object modeling,
data modeling, data
integration, and
analytics reporting
No specialized tools
to automate audit
and monitoring for
data quality, master
data management
Data assets not
architected or
designed to support
existing cross –domain
business imperatives
like SmartGrid
 Key master data is
either managed in silos
or fragmented across
many systems without
synchronization
Enterprise data model
not available for
portfolios or
applications work
 Data redundancy is
not actively managed
 Information strategy
aligned with business
drivers missing
Data quality
processes are
operational, tactical
 Most data kept
indefinitely - online or
on tape.
 Difficult to
consolidate data from
various sources for
reporting needs
 No metadata or
guidance for meaning
and usage of data in
various vendor
packages.
Elements of data
management in place
but no coordinated
SmartGrid data
management strategy
Formal data
stewardship not
recognized or used
Data governance
largely with in
applications
Authority over data
definitions, and
governance of
changes not yet
coordinated fully
between business
and IT.
Large degree of
manual processing
and reconciliation by
analysts before
information can be
used
IT knows the
business owner of the
data they support
directly, not aware of
data they do not
support
Data is not easily
accessible
 Data and information
management roles
not fully
institutionalized
Low Risk
Minimal impact to the business objectives
if not effectively addressed
Medium Risk
Failure to address will result in increased
inefficiencies and unmet business objectives
High Risk
Significant aspects of overall
business objectives will not be met

SmartGrid Findings Summary:
Maturity Level Assignments
11
Level 4
Level 2 Level 3 Level 5
Level 1
Governance
Data
Information
Process
Information
Technology
Information
Use/Users
Strategy
Information
Maturity
Lack of an information strategy for business lines representing
Smart Grid is a key driver for this data management assessment
What criteria will drive prioritization of data management
recommendation ? Should we start with a Data Governance
strategy, address building locks for a Master Data Management
and business intelligence strategy?
It is not clear that the business & IT&T have demonstrated the
accountability and deployed capability to drive cross-business unit
data architecture. We will need more follow up with business and
IT users.
Lack of formal processes for handling data quality, metadata,
data models, analytics limits productivity. Application security is
the most mature area, but data security needs to be addressed.
Data is designed and delivered in application databases most of
the time. Lack of an enterprise level BI & reporting process limits
effective data sharing, quality, and availability. Lack of a common
data model is being addressed by PESM, but this may be focused
on application interfaces, not including data interfaces
Project and portfolio governance is not yet mature . Funding is
project at a time. Data and information governance appear to be
by project or application. Lacking formal processes for data
stewardship, so sharing is anecdotal or based on tribal
knowledge.
Enterprise Architecture program’s investment in data modeling
and semantic modeling is promising. More technology investment
to automate or accelerate core processes such as data quality,
metadata management, and master data management.
20%
38%
38%
36%
24%
35%

SmartGrid Current and Targeted Competency
12
Governance
Data
Information
Process
Information
Technology
Information
Use/Users
Strategy
Information
Maturity
Lack of an information strategy for business lines representing
Smart Grid is a key driver for this data management assessment
What criteria will drive prioritization of data management
recommendation ? Should we start with a Data Governance
strategy, address building locks for a Master Data Management
and business intelligence strategy?.
It is not clear that the business & IT&T have demonstrated the
accountability and deployed capability to drive cross-business unit
data architecture. We will need more follow up with business
users.
Lack of formal processes for handling data quality, metadata,
data models, analytics limits productivity. Application security is
the most mature area, but data security needs to be addressed.
Data is designed and delivered in application databases most of
the time. Lack of an enterprise level BI & reporting process limits
effective data sharing, quality, and availability. Lack of a common
data model is being addressed by PESM, but this may be focused
on application interfaces, not including data interfaces
Project and portfolio governance is not yet mature . Funding is
project at a time. Data and information governance appear to be
by project or application. Lacking formal processes for data
stewardship, so sharing is anecdotal or based on tribal
knowledge.
Enterprise Architecture program’s investment in data modeling
and semantic modeling is promising. More technology investment
to automate or accelerate core processes such as data quality,
metadata management, and master data management.
Initial
Targeted
Competency
Level
Level 4
Level 1
20%
38%
38%
36%
24%
35%

13
Highlights – Key Areas of Improvement: (1 of 3)
• Elements for an Information strategy for Smart Grid (such as architecture patterns,
standard tools, the initial usage of the Utilities CIM in some projects) are being
established, however there is no coordinated strategy aligned with business drivers to
manage SmartGrid data assets proactively.
Strategy
The Data
• Key master data is either managed in a silo and has been replicated to other areas (like
CIS data) or the data is fragmented across many systems (like Asset data, Location data)
without adequate synchronization
• Data redundancy is not actively managed across applications. When information is
needed, the information is replicated without necessary provisions for keeping information
synchronized.
• An enterprise data model is not available to guide or accelerate applications or portfolio
work by developing re-usable data sources and services.
• There will be an increase in the volume and velocity of event data (e.g. Meter event data
such as Last gasp, Power quality / voltage ) which will require corresponding context data
and eventually complex event processing.

• Data ownership and stewardship are not formalized when decisions are made about data.
• Data Quality processes are operational and tactical and not at a strategic level
• Authority over data definitions, and governance of changes not yet coordinated fully
between business and IT
• There will be new data produced by the SmartGrid applications, and policies about usage
and security of this new data will need to be established
14
Data Governance
• Manual integration of systems is still occurring
• Analytic data stores are built by projects without a comprehensive BI analytics architecture
plan
• There is no metadata management or guidance for the meaning and usage of data and their
equivalent concepts in the various vendor packages in use
• There is a need for a formal Lifecycle data management for key data. Most data is kept in
systems forever, if not on disk on tape.
• It is difficult to consolidate data from various sources for reporting.
• There is a large degree of manual processing and reconciliation by analysts before
information can be used, both for operations and reporting
Information Processes

15
• Data and information management roles in IT are not fully institutionalized in the SDLC e.g.
data modeling, dimensional modeling for business intelligence and object modeling for CIM.
• New types of data will be exposed to the internal users and external users which will need
proper definition and usage rules.
• Security and cyber security are strong areas. However, there needs to be a more formalized
meta data security tags.
Information Use
• Enforcement and controls for data quality are not always built into data input processes
• No specialized tools to automate audit and monitoring for data quality
• No metadata repository accessible to both business and IT for meaning and usage of data
in various vendor packages.
• Standard tools are established for object and data modeling, data integration, and
reporting but need to be established for data quality, data repository, and master data
management.
Technology

Contents
Approach
Recommendations
Appendix
16

Project 1 – SmartGrid Business Intelligence
Method & Framework
Project Key Benefits
Key Technical Tasks
 Implement a BI Method and Framework to support
SmartGrid reporting and analytics needs.
 Develop a Business Intelligence framework that
addresses gaps in the information architecture that have
not been addressed by Master Data Management, Data
Governance, Data Quality, and Information Lifecycle
Management, which is mainly the Data Repositories
(see Slide 27). Slide 39 shows complex reporting for
DSDR prior to data mart approach.
 A Business Intelligence (BI) framework provides a phased
solution to deliver re-usable data stores and data services to
support SmartGrid reporting and analytics requirements.
 A BI Framework delivers results to enable decision-making for
monitoring and optimizing SmartGrid operations.
 A BI Framework facilitates real time analysis to make
intelligent decisions quickly.
 A BI Framework transforms data into useful , actionable
information for operations and planning.
 A BI Framework integrates structured and unstructured
content, from many different sources to provide insight and
predictive analytics.
 A Business Intelligence framework allows the SmartGrid
PMO and Enterprise Architecture to ensure that investments
in data stores and data services can be leveraged throughout
the SmartGrid program.
 All of the benefits above contribute to improved operational
and financial performance.
 Perform a BI assessment as part of Strategy & Planning
Phase. (See Slide 28, IBM BI Method)
 Document current information architecture
components .
 Document linkages with other Enterprise
Architecture domains.
 Formulate a BI strategy as part of an overall Information
Management strategy for SmartGrid.
 Adopt a BI method which includes a BI methodology ,
reference architecture, BI framework, and BI accelerators.
 Create a phased plan which supports SmartGrid initiatives
and realizes the BI Framework (See Appendix IBM BI
Method – slide 28)
 Review and extend the BI Framework, provide more
analytics capabilities, as needed.
17

Project 2 – SmartGrid Master Data Management
18
Project Description Key Benefits
Key Technical Tasks
 Develop a Master Data Management capability as
described by a Master Data Management plan. The plan
describes the essential components of a Master Data
Management (MDM) framework and an iterative plan to
build this capability.
 Manage and maintain Master Data as an “Authoritative
Source” and securely deliver accurate, up-to-date
Master Data such as Asset and Customer data across
SmartGrid authorized users and systems.
 MDM improves cost efficiency for operations by automating
and streamlining business processes. MDM synchronizes key
master data across applications which eliminates or prevents
manual processes, and decreases duplicate, incomplete or
inaccurate data. This can result in operational efficiencies such
as shorter restoration time and more efficient dispatch of field
crew.
 MDM enhances business agility through improved ease of
data use. Business process changes are implemented faster
because of access to accurate and complete data from a single
location, reducing the need to build new interfaces to acquire
or cleanse data.
 MDM improves the customer experience. MDM is a key
enabler for delivering consistent and timely information and
messages across channels from the web , to mobile devices
and call centers. This information could include customized
offerings based on a Customer’s net energy usage.
 MDM lowers the cost of regulatory compliance, customer
privacy preferences, and security policy implementation
through significant improvement in the accuracy and analysis
capabilities of reporting
 MDM improves data quality and accessibility which
maximizes SOA and SOA investments .
 Develop a business case and phased project plan (see
Appendix Slide 30 for IBM MDM Strategy & Planning
Method)
 Conduct an assessment for MDM
 Define the key subject areas of master data ; Identify the
systems and business processes that consume the data
 Identify current data sources
 Pilot a Phase 0 MDM project for Asset data
 Develop Use Cases
 Select MDM architecture style
 Select, acquire , and configure MDM tools
 Develop MDM metrics
 Dependency on Data Governance (Project 6)
 Dependency on Data Quality Management (Project 3)

Project 3 – SmartGrid Data Quality Management
19
Key Technical Tasks
 Develop a Data Quality (DQ) management capability by
adopting a Data Quality framework to support Data
Governance and Master Data Management.
 Data Quality extends throughout the lifecycle of the
data when it is first created, and as it flows through
various data stores.
 DQ management increases report accuracy and improves
decision making.
 DQ management is fundamental to successful corporate
performance management deployments.
 DQ management lowers cost and reduces rework, locating or
reconciling information.
 DQ management improves information accuracy for
compliance and regulatory requirements.
 DQ management increases customer satisfaction.
 DG management increases business and systems productivity.
 Develop a business case and project plan, which includes a
preliminary assessment and baselining of data quality
 Identify high value data attributes, key relationships
 Establish DQ dimensions
 Establish DQ process
 Select, acquire, and configure DQ tools
 Baseline data quality
 Cleanse data
 Develop remediation strategy – in partnership with the
data stewards and data owners
 Develop DQ metrics

Project 4 –SmartGrid Information Lifecycle
Management
20
Key Technical Tasks
 Establish Information Lifecycle Management (ILM) in
conjunction with the initial MDM implementation
and/or any large data stored developed for SmartGrid
 ILM is comprised of the policies, processes, practices,
and tools used to align the business value of
information with the most appropriate and cost
effective IT infrastructure from the time information is
conceived through its final disposition. Information is
aligned with business processes through management
of policies and service levels associated with
applications, metadata, information, and data.
 ILM manages growing data volumes in a cost effective
manner.
 ILM accommodates rapid business growth and the large
amounts of new data created.
 ILM increases the business value of information
 ILM aligns business needs with the appropriate technical
solution based on the value and usage patterns of the
information throughout its lifecycle, from creation and initial
storage until it becomes obsolete and is deleted.
 ILM improves accessibility of high value data regardless of
creation date facilitating business continuity, improving risk
management and meeting challenges of compliance.
 ILM enables automation of data retention and storage
resource optimization.
 Develop a business case and project plan. This includes a
preliminary assessment and establishment of scope
(Appendix Slide 35 has IBM Information Lifecycle
Management elements).
 Baseline database sizes and storage architecture
 Discover business objects - Referentially-intact subset of data
across related tables and applications; includes metadata
 Develop data retention policies
 Classify data and define service levels
 Select, acquire, and configure tools
Key Technical Tasks (continued)
 Archive data and unstructured content
 Establish policies for management of test data
 Analyze data content

Project 5 – Organize a Center of Excellence for
SmartGrid Data Management
21
Key Technical Tasks
 Organize and staff a Data Management Center of
Excellence (CoE) as part of IT&T Information
Architecture.
 The CoE includes dedicated resources to support
SmartGrid data management initiatives. Based on the
projects, this is at least 2 FTEs.
 The Data Management CoE owns and operationalizes
the Data Governance framework that is adopted in
Project 6 and its related core disciplines, Data Quality
(Project 3) and Information Lifecycle Management
(Project 4)
 The Data Management CoE also owns and
operationalizes Master Data Management (Project 2)
 The Data Management CoE provides data expertise to
the business as they tackle issues such as business
identifier design for Field Tags (Florida).
 The Data Management CoE also ensures that a solid
foundation and the core components for more advanced
information needs such as complex event processing can
be addressed in a timely manner.
 Provides leadership in data management and execution
through a single, cross-organizational, cross-functional
authority for data management related planning and
implementation.
 Establishes an environment for developing expert level skills
towards best practices, technology, standards and related
data management disciplines.
 Provides consistent communication of strategic and tactical
goals for data management the benefits and successes of data
management to the organization.
 Provides a focal point for collaborating with the business and
for data in SmartGrid.
 Define and implement the Data Management CoE
staffing model.
 CoE staff executes Project s 1-4 and 6 to adopt, publish,
operationalize the BI and MDM framework, the data
governance model and its related core disciplines.

Project 6 – SmartGrid Data Governance
22
Key Technical Tasks
 Develop a Data Governance (DG) capability to manage
data as a strategic asset.
 The Data Governance capability includes related core
disciplines: Data Quality, Information Lifecycle
Management
 Data Governance increases the business value of information.
 Data Governance increases trust in reporting by providing a
single version of financial and operational performance. For
SmartGrid, it should improve and eventually optimize grid
operations, optimize asset utilization and enhance the
customer experience.
 Data Governance facilitates better risk management from
operational and reputational perspective through its Data
Quality Management process. It reduces risk of poor data
quality.
 Data Governance lowers cost by avoiding unnecessary capital
expenses in its Information Lifecycle Management (ILM)
process. It provides policy-based, cost effective management
of data from creation, to storage, and deletion.
 Data Governance aligns business and IT through collaboration
in setting information policies.
 Conduct a data governance maturity assessment
 Adopt and implement a data governance framework based
on the assessment results
 Develop a communication plan
 Develop policies and standards
 Establish data governance organization, including virtual
teams
 Establish data stewards and data owners
 Build a metadata repository to support DG
 Develop Metrics
 Develop Data Quality Management (Project 3)
 Measure results

SmartGrid Current and Target Positions –
Which gaps are addressed by which recommendations
23
Governance
Data
Information
Process
Information
Technology
Information
Use/Users
Strategy
Information
Maturity
The recommendations address the gaps indicated above
between the current state target competency level.
Initial
Targeted
Competency
Level
Level 4
Level 1
20%
38%
38%
36%
24%
35%
Addressed by Project 5 Data Mgt CoE & 6 Data
Governance
Addressed by Project 1 BI, Project 3 Data Quality,
Project 4 ILM, Project 5 Data Mgt CoE & 6 Data
Governance
Addressed by Project 1 BI, Project 2 MDM, Project 3
Data Quality, Project 4 ILM, Project 5 Data Mgt CoE &
6 Data
Addressed by Project 5 Data Mgt CoE & 6 Data
Governance
Data Quality, Project 4 ILM, Project 5 Data Mgt CoE &
6 Data Governance
Data Quality, Project 4 ILM, , Project 5 Data Mgt CoE
& 6 Data Governance

Summary of Recommendations
24
Recommendations
1 SmartGrid Business Intelligence Method & Framework
2 SmartGrid Master Data Management
3 SmartGrid Data Quality Management
4 SmartGrid Information Lifecycle Management
5 Organize a Center of Excellence for SmartGrid Data Management
6 SmartGrid Data Governance

Contents
Approach
Recommendations
Appendix
25

Stakeholder List
26
Application
/ Subject
Area/ Focus
Business /
Technical
Participants
CSS Technical Cliff Rice
CSS/ EA Technical Patti Kreider
CSS Business Ruby L Clement
Head End
AMI Server
Technical Dave Durbano
Head End
AMI Server
Technical Casey Lalomia
OMS – PEF Technical Roosevelt Glen
GIS Technical Floyd Phillips
Technical Dennis Cottle
GIS Business Francisco Sarmento
NGDR -MV90,
MDMS
Technical Gary Kubousek
CIM Business Felice Chadwick
Application
/ Subject
Area/ Focus
Business /
Technical
Participants
OMS -PEC Technical Steven Schroedl
Smart Grid
Strategy
Business Jason Handley
Distribution
Data Integrity
- Florida
Business Scott Waldman
ED -
Performance
Support
Business Sally Jensen
Distribution
Design & Data
Service –
Carolinas
Business Shane Killion

Project 1 (1 of 3) - IBM BI Reference Architecture
27
Access
Web
Browser
Portals
Devices
Web
Service
s
Hardware & Software Platforms
Network Connectivity, Protocols & Access Middleware
Systems Management & Administration
Security and Data Privacy
Metadata
Data Sources
Enterprise
Unstructured
Informational
External
Analytics
Collaboration
Data Mining
Modeling
Query &
Reporting
Scorecard
Visualization
Embedded
Analytics
Business
Applications
Data Repositories
Operational
Data Stores
Data
Warehouses
Data Marts
External
Data Integration
Extract/Subscri
be
Initial Staging
Data Quality
Clean Staging
Transformation
Load-Ready
Publish
Load/Publish
Data Governance
Data Quality

Project 1 (2 of 3) - IBM BI Methodology
28
Note: For clarity, all
activities are not
shown
Solution
Outline
Define
Infrastructure
Requirements
Define
Organization
Review Client
Environment
Outline
Solution
Requirements
Outline
Solution
Strategy
Determine
Data Integration
Requirements
Determine
Data Repository
Requirements
Determine
Analytics
Requirements
Assess
Business Impact
Confirm
Solution Outline
BI
Strategy
and
Planning
Macro
Design
Create Logical
Data Integration
Design
Create Logical
Data Repositories
Design
Create Logical
Access
Design
Design
Architecture
Model
Design Solution
Plans
Design Test
Specifications
Build
Development
Environment
Micro
Design
Create Physical
Data Integration
Design
Create Physical
Data Repositories
Design
Create Physical
Access
Design
Refine
Architecture
Model
Perform
Static Testing
Define Training
and User Support
Plan
Development
Build
Cycle
Build
Data Integration
Code
Perform
Data Repositories
Build
Build/Test
Access
Components
Prepare for
Testing
Perform
Development
Testing
Perform
System
Testing
Plan
Deployment
Deployment
Perform
Acceptance
Testing
Setup Production
Environment
Deploy Client
Support
Cutover to
Production
Implementation
Checkpoint
Create Logical
Analytics
Design
Create Physical
Analytics
Design
Build/Extend
Analytics
Components
Assess Client
Business & IT
Environment
Formulate
Business
Intelligence
Strategy
Develop Business
Intelligence
Architectural
Strategy
Plan Business
Intelligence Plan
Activities
Determine
Organization
Definition
Activities
Confirm BI
Strategy Planning
Transition
IBM’s BI methodology is based on industry
leading set of phases, activities and tasks.

Project 1 (3 of 3) - Current Data Environment
29

Project 2 (1 of 5) – IBM MDM Strategy and
Planning Method
30
Phase Deliverable Content
 Enablement Phase Project Plan and Schedule
 Data Quality Assessment
 MDM Strategy and Roadmap
 Executive Summary & Background
 Current “as-is” environment assessment
 Business & I/T benefits prioritization
 Document current initiatives and descriptions
 MDM organization / governance assessment
 Initial MDM conceptual / logical architecture
 MDM Enablement Plan
Benefits of IBM Method
 Ensures alignment with overall organizational strategy
 Leverages existing investments as appropriate
 Identifies opportunities for optimizing business performance
through the use of MDM capabilities
 Creates support for future business cases for MDM business
and I/T investments
 Improves long-term cost efficiency through MDM strategic
planning
 Focuses work effort guided by roadmap / enablement
strategy
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8
Analyse Envision Design
Project
Kick -off and
Organis ’n
Assess
Current
Business
and I/T
Environment
Organis ’n
and Data
Governance
Review
Preliminary
Data
Assessment
Review
Implement ’n
Planning
Phase 1 Phase 2
Phase 3
Phase 4
Phase 5
MDM Evaluation
MDM
Strategy
Method
MDM
Strategy
Method
Analyse Envision Design
Project
Kick -off and
Organis ’n
Assess
Current
Business
and I/T
Environment
Organis ’n
and Data
Governance
Review
Preliminary
Data
Assessment
Review
Implement ’n
Planning
Phase 1 Phase 2
Phase 3
Phase 4
Phase 5
MDM Evaluation
MDM
Strategy
Method
Assess Envision Plan
Project
Kick -off and
Organization
Assess
Current
Business
and I/T
Environment
Organization
and Data
Governance
Review
Preliminary
Data
Assessment
Review
Implementation
Planning
Phase 1 Phase 2
Phase 3
Phase 4
Phase 5
MDM Evaluation
MDM
Strategy
Method
MDM
Strategy
Method

Project 2 (1 of 4)
Carolinas – SmartGrid Asset Information Flow
31

Project 2 (2 of 4)
Florida – SmartGrid Asset Information Flow
32

Project 2 (3 of 4) - Asset Type/ Application Matrix –
SmartGrid Carolinas
33
Application WMS GIS OMS OMS
ODS
DMS DSCADA CYME ROR VRU PE.com
Asset Type
Substation M M M R M M R M M R
Transformer M M M R M M R M M R
Underground
Feeder
M M M R M M R M M R
Overhead
Feeder
M M M R M M R M M R
Switch M M M R M M R M M R
Mounted
Switchgear
pad
M M M R M M R M M R
Capacitor M M M R M M R M M R
Fault
Indicator
M M M R M M R M M R
Legend: R=Read, M=Modify

Project 2 (4 of 4) - Asset Type/ Application
Matrix – SmartGrid Florida
34
Application WMS GIS CIS OMS OMS
Status
OMSR T/
DSCADA
CYME MOM VRU PE.com
Asset Type
Substation M M R M R R M R M M R
Transformer M M R M R R M R M M R
Underground
Feeder
M M R M R R M R M M R
Overhead
Feeder
M M R M R R M R M M R
Switch M M M R R M R M M R
Mounted
Switchgear
pad
M M M R R M R M M R
Capacitor M M M R R M R M M R
Fault
Indicator
M M M R R M R M M R
Legend: R=Read, M=Modify

Project 4 (1of 1)– Elements of Information
Lifecycle Management
35
Content assessment—Address unmanaged “content
in the wild,” which
helps to assess and decide what information to
manage, trust, and leverage.
Content collection and archiving—Manage the
explosion of information
volumes and types.
Advanced classification—Reduce the burden on
end users and improve the
ability to classify information.
Records management—Enforce retention and
disposition policies, and
confidently dispose of information.
eDiscovery search and analytics—Respond to
eDiscovery, audit, and internal investigation requests
quickly and cost-effectively.

Project 6 (1 of 1) – Data Governance –
Elements of Effective Governance
36

37
Relationships of Project 2,3,4, & 6 as shown in
IBM Information Governance Unified Process

Definition of Terms
38
Definitions
 IBM’s BI methodology is a collection of techniques and
technologies that helps organization build towards end
vision in an iterative fashion. This has been deployed
and refined over many years in many industries. This
methodology results in the creation of a comprehensive
strategy and BI architecture which will serve as the
framework for a flexible repository and analytic
environment, one that can meet the ever changing
business and technical environment for a utility.
 The IBM Business Intelligence Reference Architecture is
a component-based, scalable, conceptual architecture to
build towards an end visions. IBM has detailed reference
architecture and frameworks across people, process and
technology for each component.
 Master data management or MDM is a set of disciplines,
technologies, and solutions to create and maintain
consistent, complete, contextual, and accurate business
data for all stakeholders across and beyond the
enterprise.
Definitions
 Data Quality Management is a disciplines that includes
methods to measure, improve, and certify the quality
and integrity of production, test, and archival data.
Data Quality includes data standardization, matching,
survivorship, and the monitoring of quality over time.
 Information Lifecycle Management (ILM ) is comprised
of the policies, processes, practices, and tools used to
align the business value of information with the most
appropriate and cost effective IT infrastructure from the
time information is conceived through its final
disposition. Information is aligned with business
processes through management of policies and service
levels associated with applications, metadata,
information, and data.
 Data Governance is the discipline of treating data as an
enterprise asset. It involves the orchestration of people,
process, technology, and policy within an organization,
to derive the optimal value from enterprise data. It
involves the exercise of decision rights to optimize,
secure, and leverage data as an enterprise asset.

SG Data Assessment - Recommendations
40
Recommendations Rationale – Areas Addressed
1 SmartGrid Business Intelligence (BI)
Method & Framework
Addresses the difficulty and complexity of consolidating data for
reports.
Provides a phased solution to developing re-usable analytic data
stores and data services supporting reporting & analytics
requirements.
2 SmartGrid Master Data Management Addresses key master data silos, fragmentation , and synchronization.
It provides high quality enterprise data sources for business
intelligence.
3 SmartGrid Data Quality Management Address Data Quality issues and provides solutions for our resource
constrained operations.
 Is fundamental to report accuracy, improved decision making and
measuring performance.
4 SmartGrid Information Lifecycle
Management
Addresses the increased volume and velocity of data in a cost
effective manner.
5 Organize a Center of Excellence for
SmartGrid Data Management
Provides data management leadership and execution.
Provides an environment for developing skills towards best practices
Provides consistent communication of strategic and tactical goals.
6 SmartGrid Data Governance Aligns business and IT through collaboration in setting information
policy.
Is a key enabler for Business Intelligence & Reporting, Data Quality
and Master Data Management

SG Data Mgt - Findings and Recommendations.pptx

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