Driving Business Performance with effective Enterprise Information Management

A talkbook on Data Governance and
Data Quality Management
Driving Business Performance
with effective Enterprise
Information Management

Internal Notes – Delete before showing to the clients
• The purpose of this presentation is to introduce clients or prospects to our capabilities in delivering Enterprise Information
Management solutions.
• It is intended for use by non-subject matter experts or SMEs to showcase our capabilities and solutions and to lead prospects
to the next conversation, which should be with a SME.
• Additional slides on our Enterprise Information Management Framework and service offerings can be included as an appendix
to this presentation. If your client is ready for a more detailed discussion, you can customize the presentation through the use
of these additional slides.
• Lastly, where ever applicable, the messaging notes can be found in the speaker notes pages. Please delete these notes before
sharing electronic copies of the presentation with external parties.
2

Contents
• Market View on Enterprise Information Management
• Common Enterprise Information Management Challenges and Drivers
• Our Information Governance Framework and Service Offerings
– Introduction to our Information Governance Framework
– Information Lifecycle Management (ILM)
– Data Governance Framework and Organisation Structure
– Data Ownership and Stewardship
– Master Data Management
– Data Classification
– Data Flow Analysis
– Data Quality Management
• Our Engagement Methodology
• Appendix- Our Point of view on Business Intelligence v/s Enterprise Information Management
• Appendix - Other Supporting Slides / Contents
3

Market View on
Business Intelligence and
Enterprise Information
Management

Business Intelligence and Enterprise Information Management -
Defined
5
Our point of view is a little less complicated….
Business Intelligence (BI) empowers the right people to receive the right information, at the right time, allowing them to make
the right business decisions.
Enterprise Information Management (EIM) provides the foundation for the business to operate as truly “intelligent
enterprise”.
“Business intelligence (BI) is an umbrella term that includes the applications, infrastructure, tools, and best practices that
enable access to and analysis of information to improve and optimize decisions and performance.” – Gartner
“Enterprise Information Management (EIM) is an integrative discipline for structuring, describing and governing information
assets across organizational and technological boundaries to improve efficiency, promote transparency and enable business
insight.” – Gartner
Enterprise information Management (including Data Quality Management and Data Governance) is the single most
important prerequisite to a Business Intelligence implementation.
“Without the proper data, or with too little quality data, any BI implementation will fail. Before implementation it is a good idea
to do data profiling; this analysis will be able to describe the “content, consistency and structure” - Kimball et al., 2008
1
2
3

Executives are focused on BI…
6
1. Gartner EXP 2012 Survey of CIOs
2. MIT Sloan Management Review : Findings from the
2010 New Intelligent Enterprise Global Executive
Study and Research Project “Analytics: The New
Path to Value”
Business Intelligence continues
to be the top priorities for the
CIOs across the globe.1
“Over the next 24 months
executives say they will focus
on supplementing standard
historical reporting of data with
emerging approaches that
convert information into
scenarios and simulations that
make insights easier to
understand and act on.” 2
By 2013:
33% of BI functionality will be consumed via
handheld devices
15% of BI deployments will combine
BI, collaboration and social software into
decision-making environments
*Gartner Research

…. but BI has not delivered on the promise.
7
1. “Coming Up Short on Non Financial Performance Measurement,” Ittner and Larcker, HBR
2. “Does your business intelligence tell you the whole story? “ - KPMG International.
Your BI solution may
have you looking at the
wrong information.
1Fewer than 10 percent of
organizations have
successfully used BI to
enhance their
organizational and
technological
infrastructures.
2 3 4
More than 50 percent
of business intelligence
projects fail to deliver
the expected benefit.
Two thirds of executives
feel that the quality of
and timely access to
data is poor and
inconsistent.
Seven out of ten
executives do not get
the right information to
make business
decisions.
70% of companies employ metrics that lack statistical validity
and reliability.1
While 95% of companies forecast cash flows, only 14% of cash
forecasts are accurate.2
According to KPMG International, the execution of business strategy is often hampered by a lack of reliable information2:

“Accuracy of Data” continues to drive most Business Intelligence
and Information Management Projects
2%
12%
15%
16%
17%
52%
56%
57%
60%
Other
Reduced risk of noncompliance
Reduced risk to business performance
Greater control
Greater flexibility
More-timely, accurate indication of future performance
Improved efficiency
More-robust analytical capabilities
High-quality, more-reliable management reporting
Multiple responses permitted.
Source: CFO Research Services/Lawson Software 2010
0% 10 20 30 40 50 60%
Areas of prioritized spend
8
Ensuring the accuracy of the information reported form a business intelligence system is another central
theme that organisations encounter. Integrating information from across your enterprise while keeping the
quality of data intact “from record to report” has revealed a range of issues not previously apparent.
The executives are now focused on making their BI solutions more reliable by through an information
management agenda.

Organisations are using an “information agenda” to plan for the
future
9
Highest data priorities for Organization:
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for
Business Value study. Copyright © Massachusetts Institute of Technology 2010.
Information Governance: In order to create truly
valuable business intelligence, organizations should
clearly define who owns, uses, and produces
information and how it is presented. Such tight
ownership and control should help give
consistent, accurate reports and allow fair, “like for
like” comparisons of performance.
Information Integration: The sheer volume and
types of data an organization generates has grown
enormously. Managing this data effectively and
being able to rely on its accuracy is now even more
critical to businesses.
Simplification is a one of the most significant
component of the information agenda. “Complexity
should not be viewed as a burden to be avoided; it
should be seen as a catalyst and an accelerator to
create innovation and new ways of delivering
value” - Juan Ramon Alaix, President, Pfizer Animal Health - IBM
2010 Global CEO Study.

Business Intelligence is more than just reporting
10
Valued Business
Information
Dashboards, monitoring, in
sight
KPIs, scorecards
Real time reporting
Supporting Framework
Data Governance
Data Quality
Information Integration
Reporting and Data
Management Platforms
Infrastructure –
Database, Security, ETL…
There are many benefits to the business at the surface, however a sustainable and defined infrastructure and
governance framework is required to support the consistent delivery of effective Business Intelligence and
Performance Management information.

Common Enterprise
Challenges and Drivers

Common Business Information Challenges
Everyone is effected
12
CEO
CFO
Board &
Management
CxO
CIO Associates
Customers &
Suppliers
“I can’t get the information I need
quickly enough to react to the
events and changes in the market
conditions.”
Root Cause: Data Governance Framework &
Organisation Structure, Data Ownership and
Stewardship and Data Flow Analysis.
“It is too difficult to obtain all the
information I need to make better
decisions.”
Root Cause: Data Classification, Data Flow
Analysis, Data Mapping and Data Modeling.
“I don’t have enough confidence in
some of our information to make
critical decisions.”
Root Cause: Data Quality Management and
Enterprise Information Strategy .
“We run this business by “gut feel” rather than
facts.”
Root Cause: Data Quality Management, Information Integration and
Distribution.
“I receive a multitude of reports with conflicting information so we waste time debating
which measures are correct instead of making decisions.”
Root Cause: Data Quality, Data Standardization and Master Data Management.
“Majority of our analyst’s time is spent gathering
data instead of analyzing and the information to
create insights.”
Root Cause: Data Quality and Data Ownership/Stewardship.
“ Your organization does not understand the full
breadth of the relationship your customers and
suppliers have with you.”
Root Cause: Master Data Management , Data Flow
Analysis, Information Lifecycle Management and Enterprise
Information Strategy.
All most all executives within your organisation are effected by the quality of your organizational data. For Industries that processes
huge amounts of data on a daily basis, Data Quality can make or break an organization. Some of the key information management
challenges faced by organizations are:
Information
Consumers

The root causes are also common
Common and critical
data resides in
separate systems
•Data is incomplete
and critical
information is not
captured.
•Obsolete Data.
•Reference data is not
consistent across
systems.
•Data content differs
from actual business
rules.
•Data does not
reconcile across all
integrated systems.
•The same
information is
captured from
multiple system
Inconsistent
definitions and
standards
•No common
definition of
data, including
customer data.
•The same data is not
captured in a
consistent format
across the
organisation.
•Difficult or
impossible to
consolidate
information for cross
department / cross
geography use.
Limited Analytical
Capability
• As the data is not
completely
captured, the ability
to analyse data is
limited.
•Restricted ability to
discover potential
opportunities for
cross-sell and up sell
using the current
available data.
•Lack of knowledge of
the affiliates with
repeated
transactions.
•Income/spend
patterns and trends
are not easily
available.
Unclear Data
Ownership
•No formalised
governance policy in
place.
•The data ownership
and consumption of
data is not clearly
defined.
Manual Processes
and Limited Sharing
of Data
•There are high
degree of manual
processes in place
and limited sharing
of data between
countries and
systems.
•Lack of knowledge
for end-to-end
process.
ChallengesRootCause

The Need for Enterprise Information Management
Why is information management so important to me?
Many organisations fail to effectively manage their data, resulting in greater risks to the business and missed
opportunities for commercial and competitive advantage.
Effective and innovative data governance and data quality management can help you to reduce the risks and
realise the true potential of your organisation's information:
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o Improved customer profitability and product coverage through single customer
view and product insights.
o Reduce risk (financial and reputational) through improved data quality, control
and security.
o Better informed planning based on accurate operational and forecast data
o Enhanced anti-fraud measures through linking and forensic analysis of
structured and unstructured data.
o Auditable regulatory compliance and repeatable decisions on large data-
intensive.
o Early risk warning systems to continuously monitor and improve operational
efficiencies and profitability.
o Technology-enabled-solutions that are tailored to your needs and enables you to
focus on the most relevant information.

Driver for Information Governance
15
Drivers Pressures Information Governance Value proposition
Increasing Business Value • Merger and Acquisition Activities
• Business Unit Consolidation/Diversification
• Off-Shoring/Outsourcing
• Business Process Improvement
• Administrative Process Improvement
• Consistent Security and Compliance Practices
• Quicker Rebranding of Services
• Reduced Lost Productivity
• Reduce Cost of Compliance
• Improved Business Process and Workflow for Data
Improving Compliance • Federal and State Privacy regulations
• HIPPA
• Safe Harbour
• COPPA
• FCRA/FACTA
• NERC/FERC
• Improved Compliance Monitoring
• Compliance automation
• Improved Auditing and Logging
• Flexibility to Adapt to New Regulations
• Improved Compliance Reporting
Reducing Risk • Regulatory compliance
• Breach of client, employee data or
Intellectual Property (IP)
• Third Party Management
• Information Asset Management
• Increased Security Risks
• Compliance industry regulations
• Protection of Critical Data Elements
• Understanding of Data Location and Flow
• Improved Control of Information Assets
• Brand protection
• Better Enforcement of Policy and 3rd Party
Management
Containing Cost • Breach Recovery/Management Costs
• Management of Legacy Documentation
• Consolidation of IT Data Repositories
• Consistent Security and Compliance Practices
• Information Life Cycle Management
• Reduced Costs, Resources
• Classification of Data Elements

Our Information
Governance Framework
and
Service offerings

Our Information Governance Framework
17
Our Information Governance Framework is
designed to strike the right balance between
technical and functional infrastructure for
effective information management. There are
seven “components” to the
framework, which, although
interdependent, can also be applied
individually.
Our Information Governance Framework also
unpins our service offerings that help us to
assist our client with designing
personnel, process, technology, and controls
that address compliance requirements, while
also protecting the most important
information assets
Our approach encompasses the complete
governance lifecycle, helping to enable
clients to choose the appropriate services to
achieve their specific business needs.
Information
Lifecycle
Management
Data
Governance
Framework &
Organisation
Structure
Data
Ownership
and
Stewardship
Master Data
Management
Data
Classification
Data Flow
Analysis
Data Quality
Management
Technology

Key Components of Our Information Governance Framework
18
Information Lifecycle Management (ILM)
The core of our approach focuses on ILM which comprises
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.
Data Governance Framework and Organisation Structure
The process of defining the roles / relationships and information
management organization structure. It helps to drive ownership
rules, set standards and direction for data management and create
data quality standards.
Data Ownership and Stewardship
The process of understanding the data and establishing standards
and measurable goals to help improve the quality of the data by
evangelizing the leading practices across the organization.
Master Data Management
The process of consistently defining and managing the information
that is key to the operation of your business covering
customers, products, employees, materials, suppliers etc.
Data Classification
The process of dividing data sources
(documents, applications, databases, etc.) into groupings to which
defined level of controls, protection and policies can be applied to
support business objectives.
Data Flow Analysis
The process of classifying and managing the flow of information
assets within an organization.
The process of accessing, designing, improving, monitoring and
measuring the duality of data across the organization.

Information Lifecycle Management
Establishes the Information Management Vision and Translates it to Measurable Execution.
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Phase 1 – Generation
•Ownership
•Classification
•Governance
Phase 5 – Storage
• Access Control
• Structured versus Unstructured
• Integrity/Availability/Confidentiality
• Encryption
Phase 2 – Use
• Internal versus External
• Third Party
• Appropriateness
• Discovery/Subpoena
Phase 3 – Transfer
•Public versus Private Networks
•Encryption Requirements
•Access Control
Phase 6 – Archival
•Legal and Compliance
•Offsite Considerations
•Media Concerns
•Retention
Phase 7 – Destruction
•Secure
•Complete
Compliance
•Audit & Regulatory
•Legal
•Measurement
•Business Objectives
Phase 4 – Transformation
•Derivation
•Aggregation
•Lineage
•Integrity
At the core of our framework lies the Information Lifecycle Management (ILM) which helps us to gain an understanding of the risks
associated with your information across its lifecycle. Key considerations are as follows:

Information Lifecycle Management
Service Offerings and Sample Deliverables
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• It may take some time for the business to realise that the Data Governance program will
create an opportunity for them to improve the overall information quality across the
organisation.
• Practical considerations will come to light when you put into practice the Governance
principles and bodies defined in this phase. Be prepared for scope and requirements to
change overtime.
• Ownership and accountability is critical; if no one owns it, it will not get done. Create a
risk mitigation plan in the event of a critical lack of available documentation on how
owns what information and how is it managed.
• Emphasize training and internal awareness. Many user satisfaction issues may be
overcome by end user training and increased awareness about the Program.
• Limit the number of “instantiated” policies and preserve the key capability to
“customize” the management of each individual piece of personal data, based on users’
privacy preferences.
SAMPLE DELIVERABLES AND TEMPLATES
• Information Lifecycle Gap Analysis
• Information Lifecycle Improvement Roadmap
• Change Management Program
• Data Standardization – Process/Bus. Alignment
•
KEY COMPONENTS OF OUR SERVICE OFFERING
LESSONS LEARNT
Information
Life Cycle
Management
Information Risk and Gap Assessment
Information Improvement Roadmap
Stakeholder and Change Management

Defines the governance model that supports value delivery and serves the needs of the business.
21
Executive
Sponsors
•Data Governance Manager
•Business Process Owners
•IT Systems Owners
Governance Team
•Subject Matter Experts
•Data Administrators
•IT Administrators
Data Stewards and Data
Owners
•Technical Analyst
•Business Analyst
•Data Quality Analyst
Data Quality Assurance
User Community
Data Governance is how an enterprise manages its data assets. Key step within Data Governance is to identify the Information
stakeholders (e.g. management, investors, supervisors, auditors)
A Data Governance organisation structure primarily made up of the Governance Committee, who are responsible for project
oversight, Data Stewards and a Data Quality Group. This example below can be tailored to fit the current organisation structure of
our clients, leveraging current existing roles wherever possible.
Set direction, strategy and goals for the council globally (e.g., across
regions, Sales and Marketing ,etc.). Champion the councils mission and
purpose to the corporation.
Set Business and IT direction for data architecture and
underlying infrastructure
Data Stewardship, Corporate data standards, data
repository, data architecture and database
administration. Data and ownership rules validation
Data quality processes and tools
Data requirements, data ownership
(Location A, Location
B, Sales, Marketing, etc.)

• Invest in resources for participation in the information gathering process. Key project
team members such as the Project Manager, Architect(s), Business Analyst(s) and Data
Analyst(s) should participate in as many of the meetings as possible, even if the planned
subject matter may not seem to be directly applicable to every resource.
• Site visits to organizations that have mature Information Governance models in place to
bring theory to life and crystallize good applicable features into the future design may be
a useful tool.
• Share findings and initial impressions across the program to test initial hypothesis.
• It is critical to obtain the client’s agreement and sign off for the governance framework
to gain consensus and the appropriate level of stakeholder support.
• Mapping current organization to the future organization in the early stages of the project
can help ensure efficient transition to the future state.
• Businesses will incur learning and education costs as they familiarise themselves with the
new reporting methods.
22
Information Governance Process and
Procedures
Data Governance Org Structure
Governance Framework – The Model to Guide
decision making
• Data Governance Strategy Definition
• Data and Information Governance Process and Procedures
• Data Governance Organisation Structure Definition
• Data Governance Framework Definition
LESSONS LEARNT
Data
Governance
Framework &
Organisation
Structure

Articulates Roles and Responsibilities, and helps to keep the Measurement and Analytics Function
Synchronized Across Business Units and IT.
23
Effective Definition of Data Ownership can help to:
• Understand the data and the purpose of the data in a specific system.
• Provide contextual understanding of the data in a particular system.
• Comprehend the data and various data elements in the context of the
business purpose of the system.
• For initial data ownership focus, start with Accounts and Contacts across
systems.
Data stewards play a crucial role in defining the organizational data
elements and subsequently the conceptual data model for an organization.
Data Stewards act as the liaison between IT and the business and accept
accountability for data definition, data management process definition, and
information quality levels for specific data subject areas. Later, the
technology team translates this conceptual data model into technology
solutions.
Data Stewardship Responsibilities
• Monitor and report data quality issues
• Review error and exception logs
• Correct data gaps (i.e., missing data)
• Randomly audit data
• Maintain and fine tune data transformation and
matching rules

• When differences occur between sources of information it is often very difficult to gain
consensus in an organization as to which version of the truth is correct. It is necessary to
exercise caution to obtain information at or as close to the source as possible and to
avoid hidden filters, views, stored processes or transformations when profiling data. It is
important to obtain sign-off on the correct sources for the data.
• Assessing the Corporate Strategic Plan will provide insight as to goals and objectives to
be obtained. They will be at a high level but should be able to be broken down to lower
levels in the individual business units.
• Consider co-coordinating structured interviews and meetings across all workstreams and
socialize the design with key stakeholders to get business buy-in across the workstreams.
• A refresher training of the key participants has to be undertaken closer to the go-live
time to help ensure knowledge is retained.
• Early Communication about the new Governance organization will help ensure that
people are better prepared and are involved in the implementation early in the process.
24
• Data Ownership Matrix Definition
• Data Stewardship Structure Definition
• Change Management Program
LESSONS LEARNT
Data Ownership Matrix
Stakeholder and Change Management
Data Stewardship Structure
Data
Ownership
and
Stewardship

Uncover value creation opportunities in master data management and data
standardization, and reduce the cost of ownership.
25
Master Data
Management Processes
Supporting Process
LEGEND:
Routine and Planned
Maintenance
• Ongoing Review of policies and
procedures, business rules, system
improvements and training material
• Process and data quality audits
Event Triggered Tasks
• Business or process changes
• Impact on value lists
• Impact on mandatory fields
• New reporting requirements
• Data quality issues identified
• System changes
Master Data Maintenance
Approve and Create
• Approval to make change
• Create new record in Master table
• Quality check on trigger data
• Data owners contribute key values
• Control and validation of attribute
values
Implementation Tasks
• Review key forms
• Create Data Dictionary - Define
mandatory fields, define attribute
values, establish test for duplicates
• Policy & communication
• Purging and archiving guidelines
Modify and Update
• Approval to make change
• Enter changes
• Changes controlled in same manner
as the create process
Delete and Archive
• Identification of inactive records
• Setting of record status based
upon set rules
• Archiving of records
Master Data Operations
Source Systems
Analysis & Extraction
Master Data Analysis
(profiling)
Master Data Design
Root Cause
Analysis
Establish MDM
Process Flows
Remediation
Action Plan
Identify and Develop Data
Standards
Data Governance and Stewardship
Define Global Data Flow
Master Data Strategy
MDM Vision and
Sponsorship
High-level Profiling
Implementation
Roadmap
Our methodology for Master Data Management (MDM) provides a holistic approach to manage master data across the entire
organisation’ it is well supported by other information management processes.

26
• There is no single approach to addressing metadata. An iterative approach may be used
to increase functionality gradually through a series of planned releases.
• Any harmonisation of data definitions would require changes. Often business units are
only willing to participate in harmonisation exercises to the extent that everyone else
agrees to their existing definitions.
• Context is extremely important. In addition to Integrated Information Management
skills, the project team should have knowledge of the domain and/or industry
knowledge.
• Plan for substantial changes to the physical design during the Implement Phase as data
sourcing and data access construction activities mandate modifications to the
Operational Data Stores, Data Warehouse or Data Mart.
• Data Management and Governance are more than data tools - to be effective they
should encompass people, process, all types of data, technology and tools.
• Leverage the Information Lifecycle Management to determine what information is
critical to link and support the operational data.
• Master and Meta Data Management Strategy
• MDM Design (Data Dictionaries and Data Mapping)
• MDM Standards and Ownership Design
• MDM Tools Selection Assistance
• MDM Platform Implementation and Integration
LESSONS LEARNT
Data Dictionary
Master Data Definition and Mapping
Meta and Master Data Models
Master Data
Management

Data Classification
Clearly identifies the information and how to progress towards improved data quality/control and
underlying material factors of influence.
27
Organizational data (including Master Data and Meta Data) should have a common definition across the enterprise to operate as
a truly unified, consistent, and efficient organization. Our comprehensive Information classifications and Control model can help
you identify and prioritize the execution of various information management initiatives
“Information
Classification and
Control Model”
Attributes of Information
Definition
Accuracy/
Quality
Risk
Timing
Source
Usage
Disposition
Maintenance
Security
Redundancy
The following attributes should be considered while defining the
Information Classification and Control Model:
 Definition - A brief statement that describes the information
 Accuracy/Quality - An appraisal of the confidence one can
have in the information
 Risk - A statement to highlight the risk associated with the
information
 Timing - A discussion of frequency, regular of irregular, etc.
 Source - Where did the information originate?
 Usage - How is it used and by whom?
 Disposition - Where is the information sent?
 Maintenance - A description of the activities required to keep
the information current
 Security - A statement of the privacy / confidentiality issues
surrounding the information
 Redundancy - Are there too many versions of the same
information?

Data Classification
28
• Do not permit data gaps to go unresolved since they will require changes to the logical
and physical models and may even require revisiting project success factors.
• Ensure the level of detail is sufficient to help enable the next phase (Build) to take place.
The difficulty is to strike the right balance and not to get to the build level but at the
same time to define sufficient detail (“all thinking should be finished at this stage”)
• Well-established designs, tools and technologies are suitable and innovations should be
analyzed for risks and architecture components.
• Data Management and Governance processes expand beyond MDM to include all data
types (structured/unstructured, internal/external) and flows of data.
• Process and/or technology led transformations should take into consideration the impact
on supporting operating model. Organizational issues need to be addressed in parallel
with process and technology considerations.
• Information flow lead time is a critical consideration when designing the processes as it
has a direct impact on decision making
• Information Compliance Assessment
• Information Classification Matrix
• Information Risk Assessment
• Governance Controls Design
• Data Standardization and Quality (including Data
Cleansing and Integration)
LESSONS LEARNT
Data Precedence Matrix
Information Classification Matrix
Data Availability Matrix
Data
Classification

Data Flow Analysis
Visualize the operational elements for enterprise information integration and infrastructure
enabling successful delivery of information to the business.
Mapping the flow of information “from record to report” is critical for the ensuring the quality, accuracy and completeness of the
information presented through reports. Working across business functions and aligning with process initiatives, we deliver and
end-to-end mapping of your organization's information
29
Dimensional or third
normal form (3NF)
Approaches, Business, Con
ceptual and Physical Data
Models.
Data Mapping, Data Flow
Conceptual Architecture
and High level ETL Design
Conceptual Data
Repository Architecture
(Operational Data
Store(ODS), Enterprise
Data Warehouse (EDW)
and Data Marts
Master Data
Management
Processes, Standards and
Information Access
Privileges
Future State of Data Flow should
consider:
Efficiently managing the flow of information can help you
address the following CHALLENGES:
• Islands of Information – Most organizations suffer from
information proliferation and duplication. Each island is
assessed for its viability and duplicate systems are
rationalized, combined, or eliminated. If data I need is stored
in several different places, how can I perform comprehensive
analysis?
• Data Management – Who owns the data?
• Data Quality – How will we enforce the format in which data
is entered?
• Accountabilities – Who is responsible and accountable for
the data?
• Stakeholder Management – Who is impacted by the use of
the data and what expectations need to be set for the
delivery of the information?

Data Flow Analysis
30
• Avoid jumping to technological solutions until the business objectives and the
requirements are well understood.
• Analyzing the details and complexities of the numerous data elements is the longest and
most labour intensive component of Data Governance projects. Care should be taken to
manage the scope and estimated effort for this activity.
• It is important to use a realistic baseline of the maturity level of the client’s business and
information technology environments. Establish early the priorities for improvement as
well as understand the constraints applicable.
• Plan in advance for any sample data or data analysis requests related to data profiling
including adherence to all policies and procedures.
• Prototyping can be very valuable and prototyping opportunities should be considered
where there is a compelling benefit (including risk mitigation) that outweighs the costs.
• The quality of the source-to-target mappings has a direct impact on the data integration
design and implementation..
• Information Flow Process Maps
• Data Mapping and Data Modeling
• Enterprise Information Strategy and Conceptual Design
• Prototyping and Visualization:
• ETL Design and Development (Extract Transform Load)
• Data Warehouse Assessment and Design
• ETL and Data Warehousing Tools Selection Assistance
LESSONS LEARNT
Information Flow Process Maps
Data Mapping Matrix
Conceptual Data Flow Architecture
Internalsourcesystems
Eclipse
Elgar
BUKS
I90
Investment
products
Extract
Extract
Extract
Extract
Extract
Maxim
iser
Extract
Great
Plains
Extract
Landing Zone
Landing
Disk Area
Staging Area
Surrogate key
assignment
Data Quality
Checks
Validation &
Reconciliation
Change
capture
Business
rules
Sanity
checks
Archival &
Retention
Backup
Archive Backup
Staging
Data Warehouse
Databases
Archival &
Retention
Backup
Archive Backup
Reporting Structure 1
Reporting Structure n
.
.
.
Loading
(DW)
Loading
(RS)
Applications
(BI, Ad-Hoc
Querying,
etc.)
Reporting
Ad-hoc
queries
Reporting
1
Reporting
n
Reports
.
.
.
Extracting
Data Modeling
Data Flow
Analysis

Enable business to deliver high information to the executives when they need it, while continuously
improving its quality and the associated processes.
31
Measure Data Quality
• Implement continuous monitoring
dashboards in the environment
Access Data Quality
• Determine information/quality
requirements / KPI’s
• Identify data quality issues
• Identify controls to improve data
quality
Design Quality Improvement
Processes
• Determine information/reporting
requirements / KPI’s
• Develop continuous monitoring
dashboards in your proof-of-concept
environment
Implement Quality Improvement
Processes
• Clean-up existing data quality issues
• Implement controls in the environment to
prevent future data quality issues (e.g. field
validations, segregation of
duties, mandatory fields etc.)
ASSESS DESIGN
MEASURE IMPROVE
Data Quality
Management
Our Data Quality Management services provides a structured approach to achieve data quality across multiple systems and
processes.
• Scoping and definition of data
• Prioritization framework for identification of key risk and control areas
• Standardized approach to the development of automated monitoring of data quality and implementation of data quality
improvement measures (remediation processes and data cleansing/transforming activities)

32
• Follow the leading enterprises that focus their Information Governance Vision on
leveraging data and information as an asset to execute their core strategic objectives
• The quality of the source-to-target mappings has a direct impact on the data integration
design and implementation. Poor Quality of data simply transfers the load to other
solutions and may have a wide spread effect throughout the organisation.
• A central and widely accessible document repository of Governance documentation such
as, for example, e-rooms makes this effort more transparent to clients’ stakeholders.
• Establishing a clear baseline for current business processes is critical for reducing
complexity and identifying root causes, gaps, critical requirements, and alignment issues
• Specialized skill sets can be very helpful to assess architectural components and
interfaces. Resources outside the project team such as vendors, industry
groups, partners and other organizations should be leveraged where necessary.
• Preserve the design of the data quality framework as much as possible and document
decisions resulting from constraints or limitations of technology components.
• Data Quality Readiness Assessment
• Data Quality Gap Analysis
• Data Quality Improvement Roadmap
• Data Standardization – Process/Bus. Alignment
• Data Security Assessment and Design
LESSONS LEARNT
Data Standardization – Process/Bus.
Alignment
A key element of the measurement framework is the linkage of process and business measures.
Mobilise, data gather,
Strategy Map Workshop
preparation
Strategy Map
Workshop
Mobilise, data gather,
Strategy Map Workshop
preparation
Strategy Map
Workshop
CoB Business
and
Process measures
Agree final
Business KPIs
and Performance
Measures
CoB Business
and
Process measures
Agree final
Business KPIs
and Performance
Measures
Document and
confirm the output
from the Strategy Map
Workshop
Refine Process
Performance
Measures
Document and
confirm the output
from the Strategy Map
Workshop
Refine Process
Performance
Measures
Business Lens
Process Lens
The aim is to take strategy maps and work with the Streamline process teams to align process outcomes and measurement to
those strategy maps
Document and
confirm the output
from the Strategy
Map Workshop
Refine Business
KPIs
Document and
confirm the output
from the Strategy
Map Workshop
Refine Business
KPIs
Data Quality Gap Assessment
Data Quality Improvement Roadmap
Data Quality
Management

Data Governance
Framework &
Organisation
Structure
Data Ownership
and Stewardship
Master Data
Management
Data
Classification
Data Flow
Analysis
Data Quality
Management
Initiation Sustenance Maturity
Onsite – 100%, Offsite: 0% Duration: 3-4 Weeks Onsite 50%, Offsite 50% Duration: 6-8 Weeks Onsite 30%, Offsite 70% Duration: 12-20 Weeks
Our Engagement Methodology
34
Feedback to adjust Information
Management Strategy and Roadmap
Change Management and Communication
Project Management
Ongoing Data Quality Improvement and Support
Access Current
State
Understand the business
direction and Information
Strategy
Identify “As-Is” capabilities
for Information
Management
Perform Maturity
Assessment for Information
Management
Design and Plan
Future State
Identify “To-Be”
Requirements for
Conduct Gap Analysis and
identify Improvement
Initiatives
Build an Information
Management Strategy and
Roadmap
Implement key initiatives
(Quick Wins)
Validate Requirements and Design
Build improvement Initiatives
Test, Train and Deploy
Implement other initiatives
(Reliable Information Management)
Validate Requirements and Design
Build improvement Initiatives
Test, Train and Deploy
Feedback to adjust Information
Management Strategy and Roadmap
Our Information
Governance
Framework

Our Engagement Methodology
35
 Strengthen Processes/Controls
 Reduce # of Vulnerabilities
 Reduce Incident Costs
 Information Ownership
 Reduce Process Time
 Information Sharing & Availability
 Integrate Key Processes
 Reduce Litigation Costs
 Lower IT Infrastructure Costs
 Reduce Error Costs
 Reduce Errors & Mishandling
 Increase Process Efficiency
 Cultural Change/Accountability
Better
Cheaper
Faster
More Secure
 Controls Across Lifecycle
 Controls by Sensitivity
 Controls Designed for Risk
 Reduce Implementation Time
 Effective Communication and
Cooperation b/t business units
 Streamline Acquisitions
 Lower Audit Costs
 Decrease Cost of Control
 Lower Insurance Premiums
 Project Management
 Sustain Compliance
 Optimize Portfolio Management
With our broad ranging Information Management Methodology, we can run a program for you that can lead to
many benefits:

Appendix
Our Point of view on Business Intelligence
v/s Enterprise Information Management

An Integrated Approach to Business Intelligence
• When aligned with business goals and well executed, BI enables an organization to harness performance
drivers and risk and to make better, more timely decisions.
• An integrated Business Intelligence approach can help our clients to realize the full business value of their
information.
• A Business Intelligence offering can be divided into three key components
37
Enterprise
Information
Management
Performance
and Risk
Management
BUSINESS
INTELLIGENCE
Analytics and
Decision
Support
Our perspective expands the focus of BI from being a
tactical solution to broader organizational capabilities
that allow organizations to:
• Improved customer profitability and product
coverage
• Reduce risk (financial and reputational)
• Better informed planning
• Enhanced anti-fraud measures
• Auditable regulatory compliance and repeatable
decisions on large data-intensive
• Early risk warning systems
• Technology-enabled-solutions that are tailored
to your needs and enables you to focus on the
most relevant information

• Performance
Management
• Planning and Analysis
• Integrated Reports &
Consolidation
• Risk Management
Key Functionalities Addressed by BI
38
• Key Performance
Indicators
(KPIs)/Metrics/Measures
• Advanced Analytics
• Dashboards and Reporting
• Data Visualization
Enterprise
Information
Management
Performance and
Risk Management
BUSINESS
INTELLIGENCE
Analytics and
Decision Support
Enterprise Information Management is the
collection, organization, and distribution of all
types of information to deliver business value
to an organization
• Data Governance
• Data Quality
• Data Integration
• Data Integration Platforms
• Information Access and Distribution
The convergence of Performance and Risk
involves shifting BI’s objective beyond
reporting to delivery of information that
enhances the business performance
outcome while minimizing risk
Analytics and Decision Support
represent the ability to
acquire, consolidate and transform
relevant information into knowledge

Enterprise Information Management
39
Enterprise
Information
Management
Performance
and Risk
Management
BUSINESS
INTELLIGENCE
Analytics and
Decision
Support
• Data Governance – the process that articulates Roles and
Responsibilities, and helps to keep the Measurement and
Analytics Function Synchronized Across Business Units and
IT
• Data Quality – managing information as a corporate asset
will maintain and enhance its value, using quality-driven
organizations, processes, standards and supporting
technologies
• Data Integration – includes the collection, organization and
distribution of all types of data, to manage the full data life-
cycle needs of an enterprise
• Data Integration Platforms – represents the set of servers,
databases, software, networks and storage used to deliver
and maintain information
Many companies struggle to produce consistent reporting — with a mass of different data in multiple formats —
making meaningful comparisons difficult or impossible.
Effective Enterprise Information Management drives clear accountabilities regarding BI; clearly defined business
performance metrics and the ongoing integration between the business units.

Performance and Risk Management
40
Enterprise
Information
Management
Performance
and Risk
Management
BUSINESS
INTELLIGENCE
Analytics and
Decision
Support
 Performance Management – defined as the overarching
activities performed with the objective of measuring,
managing and optimizing enterprise-wide performance
 Planning and Analysis – financial planning and analysis
of an enterprise address end-to-end needs in financial
management, reporting, planning, forecasting and
budgeting processes, profitability management and
strategic finance
 Integrated Reporting and Consolidations – financial
consolidation activities occur as organizations reconcile,
consolidate, summarize and aggregate financial data
based on different accounting standards and regulations
 Risk Management – encompasses the processes related
to identifying, analyzing and managing a wide range of
business risks within an organization
Enterprise Performance and Risk Management helps the organizations in the design and implementation of
performance management framework including process, measures and reporting cascade, in most cases linking
strategic objectives to individual performance appraisal and reward.

Analytics and Decision Support
41
Enterprise
Information
Management
Performance
and Risk
Management
BUSINESS
INTELLIGENCE
Analytics and
Decision
Support
 KPIs, Metrics, Measures - measures are standard unit of
performance utilized to guide the tactical business
decisions, these summarize into metrics which are relied
upon for the operational business decisions and finally
consolidated into the KPI which is relied upon to manage
the strategic performance of an organization.
 Predictive Analytics – make insights more understandable
and actionable via scenario analysis, data
exploration, regression analysis, discrete choice
modeling, etc.
 Dashboards and Reporting – provide a real-time insight
into operational and financial performance in order to
facilitate timely, well-informed business decision-making
 Data Visualization – provides a mechanism to
communicate organizational information in a clear and an
effective manner through graphical means
While gathering and managing business information sets a foundation of an effective Business Intelligence system;
the true aim of a BI system to support business decision making.
Ability to deliver the right reports, to the right people, at the right time and in the right format can make or break
your BI system.

Appendix
Other Supporting Slides / Contents

Information has never been more important
43
[
Global Economic Crisis:
In the current economic climate it is vital to understand and manage performance by cutting costs to increase margins while
managing your organisations risk. Organisations are focusing on effective information management to address some of the
following challenges:
o Know Your Customer, Anti-Money Laundering and sanctions screening processes
o Transformational projects or product integration across channels requiring data to support management decisions
o Internal and external reporting for customers / financial requirements
o Regulatory reporting requirements
o Fraud detection and anti-bribery and corruption programmes
Big Data Explosion:
Big data is a popular term used to describe the exponential growth, availability and use of information, both
structured and unstructured.
Effective Big Data analysis is the key component in providing Unique Customer Benefits (UCBs) as it can offer a
treasure trove of intelligence that businesses can use to gain insights into things like subscriber behaviour and
customer churn, and to improve billing accuracy and service quality.
Data Privacy / Information Security:
Data privacy / Security continues to remain one of the top priorities for information managers across industries. Some
industries like Healthcare, Banking and Insurance are effected significantly by the way they handle customer information. Data
Governance and Data Quality management can help to manage the integrity and security of information across all interfaces
including:
o Information exchange between businesses and customers.
o Information exchange between businesses
o Internal information exchange within an organisation.

Categories on Organizational Data and its Management
44
Term Definition Demo
Data Management A thorough process of managing various types of data throughout the
organization
Change Management A thorough process of managing business change requests from the point
when a request is logged to the point when it is implemented. Examples of
requests handled by the change management process include a new report,
changes to data definitions, change to workflow, etc.
Master Data Sets of core business entities used in traditional or analytical applications
across the organization, and subjected to enterprise governance policies,
along with their associated metadata, attributes, definitions, roles, and
connections. Master data covers all the traditional master data sets:
customers, products, employees, vendors, parts, policies and activities.
Meta Data Data about data meaning. It can contain details about the structure of
database tables and objects, but also information on how data is extracted,
transformed and loaded from source to target. It can also contain information
on the origin of the data.
Reference Data Any kind of data used solely to categorize other data. Volumes of reference
data are much lower than those of master data, and it changes more slowly
than master data. An example of reference data is the use of code tables
consisting of codes and/or acronyms, descriptions, etc. The code tables hold
information about product line, gender, country or customer type etc.
Transaction Data A single piece of information related to a certain occurring activity;
Transactional Data can change very often and are not constant. Examples
of transaction data include purchase order number, general ledger posting, a
journal amount, etc
Structured Data Data that resides in fixed fields within a record or file. Relational databases,
master data files and spreadsheets are examples of structured data
Unstructured Data Unstructured Data that does not reside in fixed locations. Examples of
unstructured data include e-mails, copies of scanned invoices, free-form text
in a word processing document, etc
TRANSACTIONAL
DATA
STRUCTURED
DATA
META DATA
UNSTRUCTURED
DATA
MASTER
DATA
DATA MANAGEMENT

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