Post on 25-Dec-2015
Business and Systems Aligned. Business Empowered.TM
MIKE2 (Method for an Integrated Knowledge Environment) for Data
Governance and Management
Informatica Webinar: Data Governance and Management — How Ready Are You?
Sean McClowrySolutions Architect
BearingPointMay 2006
©2005 BearingPoint, Inc. 2
What are the guiding principles to a successful Data Governance program?
• Achieve alignment of information models with business models from the get-go
Enable people with the right skills to build and manage new information systems
Improve processes around information compliance, policies, practices and measurement
Create a culture of information excellence and information management organization structured to yield results
• Deliver solutions that meet the needs of today’s highly federated organizations
Quantitatively identify data governance problems and resolve them
Perform root cause analysis that led to poor data governance
Remove complexity by ensuring all information is exchanged through standards
Increase automation for the exchange of information across systems
• Must advance to a model focused on information development, just as we have models for developing applications and infrastructures
MIKE2 Overview Implementing an Overall Data Governance Program
©2005 BearingPoint, Inc. 3
Step 1. Organize Project Structure and Business Rules
Establish Governance Assess Business Impact Meta Model for Data Governance
Develop Data Benchmarks
Data Definition & Rules
Workshop & Prioritization
Business Process Definition
Establish Governance
Data Stewardship
Key Tasks
Measure information management awareness
Identify executive sponsor
Select in-scope subject areas
Identify system Data Quality authority
Define roles and responsibilities
Select in-scope Key Data Elements (KDEs)
Develop Data Governance Strategy
Select process areas
Identify data users
Identify and document entities and KDEs
Gather existing KDE business rules
Define data specification standards
Define data collection standards
Define reporting standards
Validate in-scope KDEs & existing business rules
Gather additional business rules
Determine business impact of erroneous or missing data
Prioritize KDEs by value
Define business entity and attribute definitions in metadata repository
Define business rules for KDEs in metadata repository
By completing Step 1, we will have a complete set of accepted data standards, definitions and business rules that will improve the productivity of the data quality project. We store data standards and rules for governance within a metadata repository.
Outcomes
• Organize the Data Governance Management project and establish the standards, definitions, and business rules for the Key Data Elements (KDEs)
• Select subject areas and agree upon the roles and responsibilities of data stewards
• Document business processes and rules thoroughly. Classify and prioritize KDEs. Ensure that metadata model is defined and ready to handle result sets from profiling
Data Governance Case Study: Public Sector Client An Initial Data Governance Program Focused on Data Quality
©2005 BearingPoint, Inc. 4
Workshop & Prioritisation
Business Process Definition
Establish Governance
Data StewardshipStep 2. Understand Historical Data Issues and Resolve Them
Data Profiling Information Process Improvement Data Re-Engineering
Data Re-Engineering
Data Archiving
Process Re-Engineering
Process Investigation
Establish Metrics
Profile & Benchmark
Analyse data profiling results
Use profiling results as basis of root cause analysis
Identify data and business process issues
Formally capture erroneous processes
Prioritise key processes for remediation
Recommend process improvements
Specify metrics categories for quality tracking
Document target metrics for each KDE
Establish baseline metrics for benchmarking
• Integrate data profiling methods into environment
Conduct data profiling activities down columns, across tables and between tables
Populate metadata repository
Identify data for re-engineering
Establish best practices to fix data
Standardize, correct, match, link and enrich data
Identify data for archiving
Review and validate archiving processes
Put archiving processes in place
• Implement a Data Governance solution that will assess the data against the identified rules that were captured in Step 1.• Report data quality assessment results against defined benchmarks in Data Governance
• Address data quality issues from the past and begin improving processes to prevent them from occurring in the future
• Define the data items that are candidates for archiving that no longer serve a useful purpose
Key Tasks
By completing Step 2, Data Governance levels are measurable and will begin to increase by improving processes and fixing historical problems caused by poor Data Governance. The Data Governance project will establish best practice re-usable services in data profiling, cleansing, and archiving. The result will be increased confidence of data within the user community.
Outcomes
Data Governance Case Study: Public Sector Client An Initial Data Governance Program Focused on Data Quality
©2005 BearingPoint, Inc. 5
Step 3. Continuous Improvement of Data Governance
Data Governance Reporting Transformation to an Information Development Center of Excellence
Change Management
Compliance Monitoring
Progressive Automation
Quality Incentives
Improved Reporting
DefineReconciliation Framework
•As part of the data remediation process, integrate and optimize the processes and techniques defined in Step 2 into the overall data management framework• Establish monitoring activities so we review data for completeness and accuracy in an ongoing fashion• Aim to enforce the use of standards and support ongoing data improvement and management processes• An organization is established that is focused on continuous improvement
Key Tasks
Introduce Root Cause Analysis as standard practice
Implement proactive approach to continuous quality improvement
Fix interfaces which cause breaches metrics
Document and prioritize interfaces requiring remediation
Ensure that new interfaces adhere to standards
Implement communication plan
Conduct management training
Align performance objectives with goals
Monitor for data standard compliance
Monitor for business rule compliance
Monitor for data management process usage compliance
Outcomes
The outcomes of Step 3 will be the continual monitoring and reporting of Data Governance metrics. Continual Data Governance improvement process will be established and the importance of Data Governance will be embedded within the culture of the company.
Provide timely result feedback to users and Stakeholders
Publish metrics and benchmarks
Establish policies, processes, and people to address data issues
Provide the means to investigate and remedy data issues
Resolution can be done across systems
Data Governance Case Study: Public Sector Client An Initial Data Governance Program Focused on Data Quality
©2005 BearingPoint, Inc. 6
MIKE2 Overview The Overall Structure Of The MIKE2 Methodology
Collaboration Framework
MIKE2 Methodology
MIKE2 Overall Implementation Guide
Data Re-Engineering
Data Modeling
ETL Integration
Metadata Management
Information Strategy
Data Warehousing IT Transformation
Conceptual Architecture
Physical Architecture
Benefits
Logical Architecture
Solutions Architecture
Architecture Team
Operations Team
Governance/Standards
Tactical Team
Skill Sets Required
Testing & Deployment
MIKE2 SolutionsArchitecture Guide
MIKE2 SolutionsCOE Delivery Team
Data Migration
Guiding Principles Data Investigation
Supporting Assets
Rationale/Benefits
MIKE 2 Vendor Solutions
Technology Backplane
MIKE2 SolutionsTechnology Backplane
MIKE 2 Business Solutions
MIKE2 Usage Model
©2005 BearingPoint, Inc. 7
MIKE2 Overview Key Aspects Of The Approach
What are some of the key aspects of MIKE2?
Architecturally-driven and is not tied to a document-based approach
— Uses “Foundation Capabilities” but also includes advanced technologies (SOA, search, unstructured data management) – with use of Metadata
— Primarily is focused on the technology backplane of integration and data management
— Complements other Enterprise Frameworks such as Zachman and TOGAF
Vision for an open and collaborative environment
— Is not vendor-specific (although there are vendor-specific supporting assets)
— Links directly into our knowledge management systems where we have done similar work on past projects
— Includes a web-based collaboration environment that provides an organizing framework for the area of Information Development
Employs a continuous implementation approach as opposed to waterfall
— Can be applied at the enterprise level, but allows for tactical project
It is thinking of “Information Development” as a new “foundational” concept that is one of the most critical aspects of MIKE2.
©2005 BearingPoint, Inc. 8
Information Development through the 5 Phases of MIKE2
Improved Governance and Operating Model
Phase 2Technology Assessment
Phase 1Business Assessment
Phase 3, 4, 5
Development
Deploy
Design
Operate
Increment 1Increment 2
Increment 3
Roadmap & Foundation Activities
Begin Next Increment
Strategic Program Blueprint is done once
Continuous Implementation Phases
MIKE2 Overview The 5 Phases of MIKE2
©2005 BearingPoint, Inc. 9
Information Development through the 5 Phases of MIKE2
Improved Governance and Operating Model
Phase 2Technology Assessment
Phase 1Business Assessment
Phase 3, 4, 5
Development
Deploy
Design
Operate
Increment 1Increment 2
Increment 3
Roadmap & Foundation Activities
Begin Next Increment
Strategic Program Blueprint is done once
Continuous Implementation Phases
MIKE2 Overview The 5 Phases of MIKE2
©2005 BearingPoint, Inc. 10
Strategic Architecture for the Federated Enterprise (SAFE) Framework – Integral To Governing Data
Integrated, Normalised, Detailed, Latest
‘Integration Apps’ Developed Over Time Enterprise Applications Product Systems
Sales Systems
Support Systems
Tightly Integrated Applications
Staging Areas
Op Risk
Adv Risk Analytics
Data Warehouse
Common Data
Mining
Calcs
Prection
Data Validation &
Monitoring
Operational Metadata
Business Metadata
Common Data and Metadata Services
Technical Metadata
Metadata Services
Mediator Services
Service Providers
Service Requestors
Interface Services
Technical Functions
Data Standardisation
Process Automation
CDC Capabilities
Shared Functions
Shared SCD job
Technical DM Services
Transactions
Master Data
CDI PDI
Reference Data Financial Treasury
Application Data Stores
Analytical Data Stores Enterprise Analytics,
External Data
Composite Applications
Producers and Consumers
(Operational Apps)
Orchestration of Integration
Processes
Data Quality Management
Integrated Data Store
Reusable Services
Integration Infrastructure
©2005 BearingPoint, Inc. 11
The Business ChallengeThe Commonwealth Bank of Australia (CBA) is one of Australia's leading financial institutions with businesses in New Zealand, Asia, and the UK. CBA initiated a metadata management program to address its increasing IT costs related to information management and improve agility. The increasing cost of new functionality as part of its business transformation is affected by the complexity of CBA’s systems. This impeded the Bank’s ability to compete in a rapidly changing market environment where reduced time-to-market for new products and service is essential to gaining competitive advantage. To simplify the complexity of their systems, CBA initiated a program to improve Data Governance through a metadata-driven architecture.
The Solution
• Comprehensive data governance and control procedures including metadata ownership to implement the system and transition to BAU
• Conducted an assessment of the bank’s metadata management maturity
• Developed a metadata management strategy and stepwise implementation plan
• Delivered an enterprise metadata extraction, load, and presentation interfaces for business metadata
• Integrated technical metadata stored from source data models, DQA, the BI environment, and ETL environment
The Benefits
• Supports the ability to perform end-to-end impact analysis of data management processes
• Improved dissemination of business rule and definition information to increase communication consistency
• Improved data quality by highlighting gaps and inconsistencies in business rules and definitions
• Is estimated by CBA to reduce the business KPI change management process by 156 days annually for each business analyst involved in the rollout of business report changes
Data Governance Case Study Enterprise Data Governance and Metadata Strategy & Implementation
©2005 BearingPoint, Inc. 12
MIKE2 Demonstration An Brief Overview of the Methodology
Overall Implementation Guide
Phase 1 – Business Assessment and Strategy Definition Blueprint
Phase 2 – Technology Assessment and Selection Blueprint
Phase 3 - Roadmap and Foundation Activities Use advanced collaboration technologies for discussing, sharing and building content
Activity 3.7 Data Profiling MIKE2 Solution for Data Investigation and Re-Engineering
• Phase 4 - Design Increment Activity 4.4 ETL Logical Design Activity 4.5 ETL Physical Design MIKE2 Solution for Data Integration
Phase 5 - Develop, Test and Deploy
Go to the Portal
©2005 BearingPoint, Inc. 13
How can you move to this metadata-driven approach that you have described?
What are the steps that you go through?
Define a strategic architecture that provides a blueprint to moving towards a metadata-driven architecture. Metadata management crosses all components in the architecture
Get foundational capabilities in place through data modeling best practices and the use of a well-defined Data Dictionary
Take an active approach to metadata management means that it is part of the SLDC – be ready to move to a model-driven approach
• How do you measure if you are on track?
Assess impact of changes in definitive terms – tables, columns, entities, classes of systems, hours in development time, etc.
Maximize automation in documenting of data relationships and flows, and subsequent changes
Capture operational metadata to understand the impact of changes in design time
Be mindful of efficiency in the development and analysis lifecycle – deployment plan, data classification, and team-based development with proper read/write access control is key
Discussion Question 1: Metadata Management Best Practice
©2005 BearingPoint, Inc. 14
What are the most significant issues that need to be addressed regarding Master Data Management (MDM), as pertains to Data Governance and Management?
How do you manage a high degree of overlap in master data?
First step is to understand where systems overlap on this enterprise data model, and the relationships between primary masters, secondary masters and slaves of master data
Then seek to come to a common agreement on domain values that are stored across a number of systems. Generally, a combination of standardizing to common domain values and making integration metadata-driven is the key to success
How do you treat the complex data quality issues with master data especially with customer and locality data from legacy systems?
Start with a quantitative analysis by data profiling tool is critical to defining the scope of the problem
Then design the information governance (stewardship, ownership, policies) requirements around master data. Combine preventive, detective, and policy-based enforcement to implement governance objectives
Integrate policies and standards, architectural considerations, and process design practices in order to effectively address the increasing federation of our systems and volumetric increase in data
Discussion Question 2: Master Data Management Across the Enterprise
©2005 BearingPoint, Inc. 15
How do you scope your project and prioritize data integration investments?
What should we keep in mind in an initial phase?
Establish the overall strategic technology blueprint that outlines the capabilities that you need for next 3 years – 5 years. Define your common technology needs for data integration to make enterprise purchases.
Leverage your compliance mandates such as SOX, IFRS and Basel II as a mobilizing force to launch a transformation around Data Governance and Management
It is often sensible to select a department to show business value beyond IT cost reductions. Starting with data quality initiatives typically deliver the fastest ROI that is easiest to quantify
What are your views on a centralized versus distributed approach, and incremental expansion from departmental to organization-wide?
Move to a centralized model for information management and integration, to complement business models in the areas that have highest degrees of shared elements
Optimize resources through a hybrid model where you combine centralized and distributed resources
Don’t over-design – get into it by prototyping and profiling. Progressively automate after understanding data management issues
Discussion Question 3: Prioritizing your Data Integration Investments
©2005 BearingPoint, Inc. 16
Driving Incremental Value Proportional to Business Demand
Align the Data Governance and Management program with a key business initiative that will contribute to your strategic goals
Don’t try to fix everything at once. Score quick wins along the way
Standard-driven, Exercise Care to Existing Environment
Data standards are the cornerstone of an effective Data Governance and Management program
Applications come and go, but the data largely stays the same. The Data Governance and Management decisions you make today will have a profound impact on your business
Rigorous Approach to Organizational Alignment
Data governance and management program is not an IT-only initiative. It requires active involvement and leadership from the business as well as within the IT
Executive Sponsor must provide leadership and senior data stewards must accept accountability
Building an integrated and accurate enterprise view won’t happen on its own – align initiatives across business units
MIKE2 Methodology Data Governance and Management – Lessons Learned
Information Development
Strategy Process Organization Technology People