Business and Systems Aligned. Business Empowered. TM MIKE2 (Method for an Integrated Knowledge Environment) for Data Governance and Management Informatica

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


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


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



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



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


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.


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


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


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


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


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


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


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


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


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

Documents

Business and Systems Aligned. Business Empowered. TM MIKE2 (Method for an Integrated Knowledge Environment) for Data Governance and Management Informatica