Embed Size (px)
Citation preview
2
Context
In most data-challenged environments everyone experiences the constant challenges of getting and using data, but lacks a shared understanding and acceptance of a common vision for how to collectively fix the problem(s) once and for all?
• Seeing the problem (and believing everyone else sees the SAME problem) is relatively easy, but with no clearly defined and SHARED “way forward”, everyone solves for these “common” challenges differently (leveraging the individual resources at their disposal) while not necessarily addressing the problem from a broader cross-functional perspective to the benefit of the organization holistically.
• Often deploying sub-optimized or temporary fixes tailored to meet the needs of an individual project, team and/or LOB. Thereby actually only exacerbating the underlying issues contributing to the complexity, confusion and costs of maturing the organization’s data management and governance capabilities.
• Driving ACTIONABLE cross-functional sponsorship and involvement is CRUCIAL to defining and executing a SHARED VISION for delivering real VALUE from data management & governance.
A Common Challenge to Maturing Data Management & Governance is...
Things already work!
There is often no sense of urgency to invest in data management & governance because the business has gotten really good at facilitating 'work arounds' to meet their individual (LOB-centric) data/reporting needs - thus
complicating business case justification and diverting needed resources and budget to other priorities.
3
Making the Business Case….
Leve
ragi
ng D
ata
& In
sigh
ts A
cros
s th
e C
usto
mer
Life
cycl
e
To compete and win in a digital environment, Organizations MUST strengthen core cross-functional Data Management and Analytics capabilities!
Organizations which lack a governed & trusted single view of consistent, accurate and timely (Master) data across functional entities and activities risk missing HUGE opportunities to gain insight for strategic advantage!
…not to mention facing regulatory, compliance and legal ramifications related to data misrepresentation & Inaccurate reporting
Acquire more profitable customers
Reduce cost to serve and drive margin
Cross-sell / up-sell and drive revenue
Focus on recovery
Reduce number of unprofitable customers
Focus on retention, loyalty and engagement to drive profitability
Opportunities for greater information exploitation & ACTIONS!
Make customers profitable faster
Source: (Customer Lifecycle Stages) CREDITCARDPLANS.BLOGSPOT.COM
Call to Action
Better DecisionMaking
ImprovingEfficiencies
IncreasingRevenue
ReducingCosts
Will Enable…
EXAMPLE
ONLY
4
“Fragmented Reporting”
Typical Current-State Reporting EnvironmentUNGOVERNED sourcing and use of data outside of a broader Enterprise Data Governance and Reporting Operating Model (with defined Controls,
Roles/Responsibilities, Ownership & Accountability) has contributed to an overly complex and costly reporting environment, resulting in an INCREASED RISK of inconsistent and misrepresented data contributing to ill-informed decision-making.
Bottom-line Negative Impacts to the Business as a result of the Current Environment are:
Loss of Operations Agility
Speed & Cost of Data Solution Delivery
Inaccurate Reporting /Delayed Decision Making
Inconsistent Business/Accounting Rules
Operational Inefficiencies / Increased Costs
Complexity & Redundancy
Noncompliant with Regulatory Standards / Laws
INCREASED Risk of Data Misuse
Key data is “made available” / stored in multiple locations.
Independent Sourcing, Creation and Management of LOB Generated Reports greatly increases potential of inconsistent representation of data and
misapplied business/accounting rules.
Logical Representation Only
Vendor Files /
Reports
Spreadsheets
Data Mart
Desktop Solutions
App Data Stores
Data StoresData Content
Customer
Account
Product
Transaction
Financials
….
LOB Generated Reports (Business / Accounting Rules & Data Presentation Layer)
Ope
ratio
nal S
yste
ms
LOB A LOB B
EXTERNAL PARTNERS
LOB C LOB D LOB N
Report OPS1
Report OPS3
Report OPS2
Report CR4
Report COM4
Report OPS4
Report P1
Report P2
Report P3
Report CR1
Report CR3
Report CR2
Report C1
Report C3
Report C2
Report COM1
Report COM3
Report COM2
Increases Risk of:
• Inaccurate reporting / data misrepresentation
• Inconsistent definition of data context & business rules
• Incomplete requirements / business view (LOB) Specific
• Redundant testing required
• Redundant data management activities performed across the Bank
• Conflicting & competing priorities for reporting budget & resources
EXAMPLE
ONLY
5
The Need to Take Data Mgmt to the Next Level
Points to Consider for most LOB (Reporting-Centric) Environmentsa. Federated approach to data management often causes confusion around accountability, priorities,
funding, and delivery execution from a cross-functional perspective, but is often sufficient to satisfy LOB/single function-specific reporting needs; which is why it's never a priority to change approaches.
b. Data Ownership and governance is often unclear, which contributes to ineffective business involvement leading to inconsistent/incomplete user acceptance and review of information solutions.
c. Data maintained outside of a shared/common data environment requires extensive manual manipulation to append to managed information for cross-functional (e.g. regulatory, performance mgmt, risk) reporting purposes GREATLY increasing the potential for data errors.
d. If no single (cross-functional) Business Glossary/Data Dictionary exist, the potential for misuse / misrepresentation of data is greatly increased.
e. Opportunities to leverage/reuse data assets and work is limited as there is no vetted/reconciled single comprehensive cross-functional view of data related to: customer, product, account, channel, activity and relationships.
Change in direction is needed to mature data management capabilities.Continuing with “business as usual” will only yield the same results.
THE HIDDEN COST OF DATA MANAGEMENTFor every reporting/analysis oriented role across the organization that spends a mere 3 hrs/wk gathering, manipulating, formatting,
consolidating, re-keying and reconciling data the estimated annual “hidden” cost for their data augmentation activities is: $10,294 per FTE; not including the opportunity cost of spending time to manipulate data instead of analyzing it.
NOTE: Simple ‘back of the envelope’ calculation ((3hrs*47wks)*$73/hr blended rate) used to illustrate the value/impact of data management .
6Page 6
NF Producer Data Roadmap
Common Challenges of a “Fragmented Reporting” Environment
Data Quality / Consistency
• Inaccurate Data• Missing Data • Ill-defined Data• Manual Data Entry• Inaccurate Business Rules
being Applied
Data Availability /
Accessibility
• Data needed isn’t
available in the Data
Mart• Not sure what data is
available• Don’t know where to
get the data I need
Fragmented
Data
• Duplicate Data Across
Systems / Environments
• Spreadsheets
• Access Databases
• Desktop Solutions
• Vendor Files / Reports
Key manually manipulated
data often never makes it
back into system of record
7
Moving Forward
Understanding What's Needed & Making it Real
8
Change Imperatives:1. Self-Service on-demand access to
Company-wide Information2. Integrated and Consistent Analysis
& Reporting (context of use)3. Cost Containment & Efficiency
Improvements in Data Management & Analytics
The current federated model of individual Data Gathering & Manipulation for LOB-focused reporting sub-optimizes our ability to FULLY LEVERAGE our information assets and investments.
Transition to a more holistic and centrally managed data operating model is required
Analysis & Decision-Making
Consolidated Support Model
Data Validation
Data Gathering & Mgmt
þ Emphasis is on analysis & decision-making
þ Expanded support services & information sharing
þ Improved data consistency & business rules
þ Streamlined and reusable data acquisition processes
Reporting
Support
Data Gathering & Manipulation
Data Validation
Primary focus is on reporting NOT analysis due to time spent on data access & manipulation
Support options are not always clear given the large number of desktop solutions
Large amounts of time & effort are spent on validating the accuracy of the data
Large lead times are required to identify and access required data
The consequences of managing data as Individual Efforts
Majority of effort is spent on Analysis &
Decision-making
Desired-State
Majority of effort is spent on Data
Gathering & Manipulation
Current-State
Change of Operating Model is Required
9
þ Improved Agility § Ability to introduce new functionality /
capability in a timely manner
þ Improved Decision Making§ Improved enablement of fact-based
decisions
þ Better Risk Management § Gain better control over data
environment
þ Increased Efficiency § Reduce / eliminate inefficiencies &
decrease complexity
þ Data values follow the business rulesþ Data corresponds to established domainsþ Data is well defined and understoodþ Vendor-data independenceþ Improved data consistencyþ Improved data sharingþ Increased application development
productivityþ Enforcement of standardsþ Improved data qualityþ Improved data accessibility and
responsivenessþ Reduced program maintenanceþ Improved decision support
enabling the business to respond quicker to new & changing
opportunities
Realizing Greater ROI from Our Data Assets Will Require ACTION
that results in improved data management…To lay a foundation…
CRITICAL ELEMENTS to MOVING FORWARD
Source: Gartner Data Mgmt Study 2012
The Way Forward
10
CoreDataRequirements
Targeted Benefits
Business Enablement
§ Accelerates solution development by enabling reuse, reducing development time and cost
§ Reduces redundancy and variances in analysis & design activities, maximizing consistency of solutions and potential reuse of data assets / investments
§ Provides an enforcement model for standards across all projects within the Bank. This ensures that the level of re-use is being maximized
§ Aids in scope and release management by providing consistent view of impacted and linked data
Common Models
Shared Data(Logical / Physical)
Process Models
ValueChains
BusinessCapabilityModelsSt
rate
gic
Req
uire
men
ts
• Centrally managed requirements• Common data definitions• Consistently defined business
rules• Reduced redundancy of data
acquisition, transformation, storage and reporting
External Data
Common Business Rules
Consistent Transformation
LogicETL
Processing
Data Governance
Council
Proj
ect 1
Vend
or C
hang
es
Oth
er
Proj
ect 2
Proj
ect …
Operational / Tactical Requirements
Reporting Analytics Dashboards / Scorecards
On Demand Access / Ad
HocBIG Data Analysis
Collective information requirements aligned with Strategy, Priorities, and Governance!
Business Architecture(strategy, capabilities & operations)
• Business Owns
• IT Facilitates
Solution Architecture(platform /Vendorindependent)
• IT Owns• Business
approves
Technical Architecture(infrastructure & operations)
• IT Owns• Governed by
Business and Solution Architecture
Desired State – Consolidated Delivery Through Collaboration & Governance
RLSHP
Party / Customer Product
Account Activity
MASTER DATA
Regulatory
Legal
CorporateRequirements
11
Executive Leadership
Executive Data Committee
LOB A LOB B LOB C LOB D
Data Steward A Data Steward B Data Steward C Data Steward D Data Steward ...
LOB E
Data Steward E
LOB F
Data Steward F
LOB …
Data Governance Council
Shared Data Services Capability (LOGICAL VIEW)
Data Management
Common Information Model (Data Requirements Management) Authoritative Sourcing
Data Quality / Certification
Reporting & Analytics
1. Data Policy and Strategic Planning – In partnership with the CDO and CIO defines the data management strategic direction and promotes compliance with policies, procedures and standards.
2. Data Governance and Metadata Management - Implements data governance processes to maintain standardized data definitions and associated metadata, and uses the metadata to guide, control and integrate data activities and products.
3. Business Data Architecture – Promotes sharing of data assets, the use of an integrated architecture to support bank-wide data movement, access to common data, consistent data transformation and migration.
4. Data Warehousing/BI – Facilitates sharing of Business Intelligence data across the organization, and promotes the use of standards for data acquisition, transformation and delivery.
5. Data Quality – Institutionalizes a set of repeatable processes to continuously monitor data and improve data accuracy, completeness, timeliness and relevance.
Shared Data Services Objectives:
Data Management & Governance
• Common Requirements• Report Inventory• Data Catalog• Business Rules• Approved Controlled Sourcing• Data Ownership• Data Classification• Master Data
Business Rules / Controls Mgmt
Data Definitions / Business Glossary
Liaison w/LOB Stakeholders/Users
Metadata Mgmt
Core Products / Services
Improved Data
Governance
Improved Data Quality &
Accountability
Increased Operating
Efficiencies
Improved Risk Management
Practices
Suggested Data Services Capability
• Subject Matter Experts (SMEs)
• Represents Functional Area by Identifying Roadmap Initiatives and Business Case Justification
• Recommends Polices & Standards
• Recommends Data Definitions and Business Rules
Responsibilities
Chief Data Steward
Report / Analytics Development
12
Suggested High-Level Process
LOB Initiatives
Reporting Needs
Analytic Needs
LOB Data Steward
Needs Identification
Shared Data Services
IT
Data Governance
Council
Executive Data
Committee
Data Requirements Repository /
Report Inventory
Project Portfolio
RegulatoryRequirements
/ Legal Mandates
Common Information
Model
Drafts / Manages Business Glossary
Proposed LOB
Standards
DRAFT Policies /
Standards
Recommends Policies /
Standards
Review & Comment
Review & Approve
Common Definitions
Policy / Standards Definition
Regulatory Requirements
/ Legal Mandates
Data Catalog Published for
Use
Reviews & Approves
Data CatalogData Policies & Standards
Review & Endorse Policies /
Standards
13
Future State Data Management Focus / AlignmentPurpose
Improve information enablement through governed utilization of designated Authoritative Source(s) for integrated and holistic reporting/analytics; enabling data driven decisions by providing a single version of the truth with accurate, consistent & actionable information.
Road
map
Enab
ling
Capa
bilit
ies
(Foc
usin
g on
Wha
t Mat
ters
)
Data Governance
Reporting & Analytics(Toolset & Skills)
Business Information Architecture
(Data Automation & Delivery)
Data Management Challenges
Speed & Cost of Data Solution Delivery Data Redundancy Reporting Inconsistencies Conflicting Business /
Accounting Rules
Self-Service & Mobility
Growth & Profitability
Operational Excellence / Customer Experience
Common Information Model(Standard Definitions & Rules)
Rapid Data Access(Data Discovery)
Predictive Analysis &
Trending(Interactive
Dashboards)
Foundational Focus Enabling Capabilities Business Enablement- Strategic Objectives -
Operational Agility(Identify & Respond)
Growth & Penetration
( Next Best Offer)
Regulatory & Compliance(Risk Management )
Integrated & Consistent Reporting
Product
Account
Relationships
Customer / Hshld
Data as a Strategic Enabler
Master Data Management
(Integrated & Holistic View)
1
2
3
Investment Priority 3“EXTEND ”
Technical Fixes /Stabilization & Optimization
Leveraging Current-State
Extending Capabilities Strategic Initiatives(NEXT GENERATION)
Upgrades / Regulatory / Security
Investment Priority 2“LEVERAGE”
Investment Priority 1“STABILIZE”
Grow what we have to harvest ADDITIONAL VALUEManage what we have…. and MAKE IT BETTER
14
Adv
ance
d A
naly
tics
- B
ig D
ata
Dat
a In
tegr
atio
n &
Ava
ilabi
lity
Optimize, Leverage & Extend - Operational Excellence
Speed & Agility - Execution & Delivery
Performance Security & Privacy High Availability / DR
Analysis
Components Primary Concerns
Identification of what data assets exist and how they’re used (context) by whom supporting which business processes
Known data owners & stewards by subject area
Where should data be reported from (with what tools)?
3
5
7
8
9
12
Capabilities, process, people, technology, standards, and governance needed to leverage our data assets
What are the key business questions that drive the Business, and what data is needed to answer them?
Pers
iste
nce
Des
ign
Dis
trib
utio
n
Reporting
10
Enforcement of authoritative data sources 4
13
1
Inventory
Ownership
Sourcing
Access & Security 11
Approved data design and modeling patterns
Data store & management plan (physcial data model)
In what order should replicated data be updated?
How should data be accessed/secured (across locations)?
How & when should data be distributed (replicated)?
2 Cross-organizational structure required to ensure data decisions are being made consistently an din keeping with strategy
StructureReference ModelGOVERNANCE
METADATA
DATA MANAGEMENT
DECISION SUPPORT
Supporting Artifacts
Data Catalog
Data Steward Directory
Enterprise Data Model & Standards
Data Management Plan
Data Distribution Strategy
Reporting Center Heat Map
Update Patterns
Data Store Classification
Enterprise Reporting Strategy
Reporting Capabilities Framework
Data Access Policy and Stds
Governance Framework & Process
ETL 6 Complete listing of all data transformations from source to target ETL Mappings & Logic Catalog
Establishing Data Architecture to Make it REAL
Confidential – Not for Distribution
15
Automate Data / Reporting
Development & Delivery
Possible 'Quick Hit' Value Add Activities May Include…Pe
ople
Proc
ess
Tech
nolo
gy
Data Governance Council
Conduct Reporting Tool(s) End User
Training
Define Formal Roles & Responsibilities
(Data Management Operating Model)
Add Data to Shared Data Store
(Expand Data Availability)
Verify Business Rules
(Reporting Controls)
Define Common Data Definitions
(Business Glossary)
Deploy Rapid Data Exploitation Capability
Near-term Activity
Source Missing Data
• Data Owners• Chief Data Steward• LOB Data Stewards• Decision Rights &
Approvals
Near-term ActivityInternal or External
Related/Supported ActivityPrecursor Activity
Define & Enforce Consistent
Data Sourcing(Policies / Standards)
Define Holistic Information Model
(Centralized Requirements)
Conduct Data Store Gap Analysis
Identify Immediate Cross-functional Data
Needs
16
Defines Information Objectives in support of
Business Priorities
How Data & Analytics Enables the Bank
Sets Direction & Context
Identifies Capabilities (& Data) required to satisfy
Business Needs
Execution & Delivery Aligned to Information
Agenda
Challenges & Opportunities
Data & Analytics Roadmap
Current-State
Environment
StrategyBusiness
Imperatives
Information Agenda
Key Focus Areas (KFAs)
Project Portfolio
Planning Approach
17
High-Level Approach
Transformation Plan Future StateCurrent State Stewardship
Identify Major Milestones and Dependencies Crucial to Implementing Future State Architecture
Identify Data Reqs ; Sourcing; Master Data Stores; Interfacing; How Data is Accessed & From Where
Inventory Current State Assets and Data; Document Context & Semantics
Establish Governance Structure; Identify Ownership and Accountability of Data Assets; Monitor & Report
Track IIIGOVERNANCE
Track IIARCHITECTURE
Charter & Plan
Identify Key Participants; High Level Requirements; Goals & Objectives; Desired Outcome and Plan
TRACK I
Hig
h-Le
vel I
tera
tive
App
roac
h
People1. Establish Decision
Rights and Checks-and-Balances
2. Establish Accountability3. Stakeholder Support
Process1. Stewardship2. Manage Change3. Resolve Issues
Communication1. Stakeholder
Communications2. Measuring and
Reporting Value
Policy1. Align Policies,
Requirements, and Controls
Technology1. Outline Acceptable
Technology and Tools Usage
Master Data Management1. Define Master Data 2. Specify Data Quality
Requirements
Prioritization & Roadmap
Data Governance
Program Management
Asset Guidance
Draft and Publish Data Management Policies & Standards
Policies & Standards
Communication & Education
Prepare & Conduct Training; Mentor IT & Business Staff
Training Data / Information Architecture
Continuous Improvement Iterative Process
Track IPLANNING
18
Moving Forward
Current to Future State Transition
19
Current State: Ungoverned Flow & Use of Information
• Poor Visibility of Data Usage across the Organization and Inconsistent Prioritization
• Ungoverned Sourcing of Data (Email attachments, spreadsheet, etc.)
• Lack of Data Validation Controls• Incomplete or Inaccurate Data
Flow Between Systems & People• Lack of Coordinated Control Over
Data Propagation / Reporting and Business Exceptions
1
2
3
4
5
*Representation Only – Does Not Reflect All Data Flows.
SeniorManagement
Product
Marketing
OperationsFinance
2
2
4
1
Data Sources
5
Ø Conflicting & competing prioritiesØ Inconsistent definition of data context & business rulesØ Requirements focus solely on current-state need / reportingØ Redundant testing requiredØ Requirements are reporting specific and LOB focusedØ No conforming information modelØ Redundant data management activities performed across the
Organization
Current State Challenges
Impacts
20
SeniorManagementOperations
Marketing
Finance
Product
Applying Process & Governance…brings order to the chaos
1. Automate Data Delivery2. Reduce errors and
improve consistency3. Standardize reporting
across business units4. Leverage existing
systems and data5. Better monitoring for
business events and initiate actions
6. Improved visibility and control over data usage
Benefits:• Huge Reduction in
Manual Work, Errors• Faster, More Consistent
Issue Resolution• Metrics, measurements,
visibility and business-friendly reports
• Rapid, Agile and Iterative process improvements
Defined Data Process
& Governance• Common
Requirements• Data Catalog• Business Rules• Controlled Sourcing• Validation Routines• Data Ownership• Data Classification• Master Data• Access Control• Business Continuity
21
SeniorManagement
Operations
Marketing
Finance
Product
Defined Reporting
Process & Governance
Enabling Improved
Decision Making!
Visibility
Control
Collaboration
Automation
Integration
Governance
Optimization
Data Consumers
Decision Makers
Integrated & ConsistentInformation
Data Management Capabilities & Focus = Business Value
