edwws: maximizing the value of mdm with data governance
TRANSCRIPT
Proprietary & Confidential
The First Step in EIM
Maximizing the Value of MDM with Data Governance
Kelle O’Neal Managing Partner
First San Francisco Partners Inc. @1stsanfrancisco
pg 2 Proprietary and Confidential
Keys to Success
Successful MDM Implementation
Technology
Process People
Failed MDM Implementation!
Technology
Process People
pg 3 Proprietary and Confidential
Some Warning Signs
Limited business involvement:
We know what the business wants to do, we can involve them later in testing…
Not recognizing the importance of Data
Stewardship: We can deal with the concept of Data Stewards at a later date…
No clear understanding of what data is needed to support the business
objectives: Bring all the data just in case…
Not enough thought given to metrics and measurement:
How will we know when we have achieved our objective?
Inability to reach a clear decision:
Definitions, Input Sources, Survivorship, KDE’s, lookup values, exception management processes, data quality targets, etc.
Value not understood: I’m not sure that this new piece of technology will actually help me in any way…
How do you know when you’re heading for trouble?
pg 4 Proprietary and Confidential
Why We’re Here
Purpose: Drive awareness of how MDM and Data
Governance interact to provide business value
An understanding of: MDM and Data Governance working together Data Governance decisions for MDM planning
& implementation How to create success
Outcome
pg 5 Proprietary and Confidential
Agenda
• FSFP’s Perspective on MDM
• Data Governance Framework
• Building the Organization • MDM decisions made by Data Governance
• Ensuring Success
pg 6 Proprietary and Confidential
[ FSFP’S PERSPECTIVE ]
pg 7 Proprietary and Confidential
Enterprise Information Management Framework
Provides a holistic view of information in order to manage data as a corporate asset
Enterprise Information Management
Information Strategy
Architecture and Technology Enablement
Content Delivery
Business Intelligence and Performance Management
Data Management Information Asset Management
GOVERNANCE
ORGANIZATIONAL ALIGNMENT
Content Management
pg 8 Proprietary and Confidential
Develop and execute architectures, policies and procedures to manage the full data lifecycle
Enterprise Data Management
Enterprise Data Management Ensure data is available, accurate, complete and secure
Traditional & Big Data Governance
Data Quality Management
Data Architecture Data Retention/Archiving
Master Data Management
Big Data Management
Metadata Management
Reference Data
Management
Privacy/Security
pg 9 Proprietary and Confidential
What is Master Data Management?
Gartner Master Data Management (MDM) is a discipline in which the business and the IT organization work together to ensure the uniformity, accuracy, semantic persistence, stewardship and accountability of the enterprise’s official, shared master data. Organizations apply MDM to eliminate endless, time-consuming debates about “whose data is right,” which can lead to poor decision making and business performance.
Wikipedia Master data management (MDM) comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization (which may include reference data). MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the ongoing maintenance and application use of this information.
pg 10 Proprietary and Confidential
Master Data Management Services Framework
pg 11 Proprietary and Confidential
MDM Value Proposition
Business Value
• Improve reporting and decision making • Better regulatory reporting compliance • Maximize revenue with integrated
solutions across business units • Reduce costs through operational
efficiency gains • Easily share trusted data across various
business functions and channels • Drive costs of bad data out of the
system • Rapidly respond to new business
opportunities • Provide “plug and play” capabilities to
consolidate and easily extend IT architecture
Technology Enablement
• Easily integrate data across siloed IT solutions
• Ensure the quality of data being delivered enhances the value of data integration investments
• Provides a single integrated architecture and solution
• Support for service oriented architecture (SOA) ensures data quality capabilities can easily be consumed as services and provides a flexible, scalable environment for data to move across the enterprise
• Easily assimilate new data elements into enterprise processes
pg 12 Proprietary and Confidential
Challenges of MDM Success
According to a recent TDWI survey, many of the MDM challenges are organizational and collaborative issues—not technical ones.
Half of users surveyed (56%) realize that MDM can be hamstrung without data governance.
pg 13 Proprietary and Confidential pg 13 Proprietary & Confidential
You can’t “do” MDM without Data Governance
An MDM initiative is an important component of a Data
Governance Strategy
Must Have “Tools”…
• Documented and enforced governance policies and processes
• Clear accountability, ownership and escalation mechanisms
• Continuous measurement and monitoring of data quality & adoption
• Executive support to create a culture of accountability around the quality of the data…it’s everyone’s concern
• Solid alignment between business & IT
• Understanding of data before diving into an MDM “Project”
Technology alone will not solve the problem
What else is needed?
pg 14 Proprietary and Confidential
[ DATA GOVERNANCE FRAMEWORK ]
pg 15 Proprietary and Confidential
Data Governance Definition
Data Governance is the organizing framework for establishing strategy, objectives and policy for effectively managing corporate data.
It consists of the organization, processes, policies, standards and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability and security of data.
Communication
Data Strategy
Data Policies, Processes & standards
Metrics and KPIs
A Data Governance Program consists of the inter-workings of strategy, standards, policies, measurements and communication.
pg 16 Proprietary and Confidential
Data Governance Framework
pg 17 Proprietary and Confidential
Why is Data Governance Important?
• Increasing customer demands and new regulations
• Streamlines and unifies the approach to managing data
• Ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business
• Balances silo-ed short-term project delivery focus
• Traditional projects don’t give enough focus to data management
• Systems are becoming more challenging to manage
• Data quality issues are persistent
Data is a valuable Corporate Asset
pg 18 Proprietary and Confidential
Data Governance & MDM Work Together
Standardized Methods and Data Definitions
Roles and Responsibilities
Decision Rights
Arbiters and Escalation
Statistics / Analysis / Monitoring
Discovery and Profiling
Cleansing
Duplicate Detection
Data Maintenance and Management
Measurement and Monitoring
Data Sharing
Workflow
Provide Guidance
Track Progress
Governance MDM
Create & Enforce Policies
Provide Feedback
pg 19 Proprietary and Confidential
[ BUILDING THE ORGANIZATION ]
pg 20 Proprietary and Confidential
Operating Model
• Outlines how Data Governance will operate
• Forms basis for the Data Governance organizational structure – but isn’t an org chart
• Ensures proper oversight, escalation and decision making
• Ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business
• Creates the infrastructure for accountability and ownership
Wikipedia: An Operating Model describes the necessary level of business process integration and data standardization in the business and among trading partners and guides the underlying Business and Technical Architecture to effectively and efficiently realize its Business Model. The process of Operating Model design is also part of business strategy.
pg 21 Proprietary and Confidential
Operating Model Design Principles
21
Design Principles
Principle Description Be clear on purpose Build governance to guide and oversee the strategic and enterprise
mission
Enterprise thinking Provide consistency and coordination for cross functional initiatives. Maintain an enterprise perspective on data
Be flexible If you make it too difficult, and people will circumvent it. Make it customizable (within guidelines), and people will get a sense of ownership
Simplicity and usability are the keys to acceptance
Adopt a simple governance model people can use. A complicated and inefficient governance structure will result in the business circumventing the process
Be deliberate on participation and process
Select sponsors and participants. Do not apply governance bureaucracy solely to build consensus or to satisfy momentary political interest
Enterprise wide alignment and goal congruence
Maintain alignment with both enterprise and local business needs. Guide prioritization and alignment of initiatives to enterprise goals
Establish policies with proper mandate and ensure compliance
Clearly define and publicize policies, processes and standards. Ensure compliance through tracking and audit
Communicate, Communicate, Communicate!
Frequent, directed communication will provide a mechanism for gauging when to “course correct”, manage stakeholder and effectiveness of the program
pg 22 Proprietary and Confidential
Data Governance Leads: Business (Full Time) and IT (Support) Program Manager
Sample: DG Operating Model
Data Governance Steering Committee
Data Quality Team Information Delivery Team
MDM Delivery Team
Reference Data Delivery Team
Data Governance Working Group
Information Architecture Lead
Security –Risk Lead (ISSC)
Existing Technology Workstreams and Delivery Teams
Data Governance Analysts • Represents cross-functional data analysis and data governance principles for the workstreams (Information, Mgr Acct & Results, Reference data, Acctg & Settlement) • When MDM is completed, becomes the go to person for data related questions
Data Stewards Lead • Represents data stewardship within each of the workstreams (Data Mgmt, Mgr Acct & Results, Reference Data, Acctg & Settlement)
Compliance Global Services
International Relationship Management
CFO Marketing IT
Common Services
Information Management supports: • Data Quality lead • Metadata Lead • Data Custodian Lead
pg 23 Proprietary and Confidential
Keys to a Successful DG Organization
• Governance team must contain members from multiple lines of business
• Team members must represent both business and IT
• Team needs to meet on a regular basis
• Agreed upon fundamentals that serve as the Guiding Principles
• Clear lines of communication
• Ensure the operating model fits the culture of the company
pg 24 Proprietary and Confidential
[ MDM DECISIONS MADE BY DATA GOVERNANCE ]
pg 25 Proprietary and Confidential pg 25 Proprietary & Confidential
Building support for MDM
• Tie the project to a larger business initiative
• Link the value of MDM to specific corporate and organizational initiatives
• Utilize metrics to make MDM real
• Leverage the approval process
• Articulate the value to individuals in their terms, per their interests and priorities
• Identify an Executive Mentor who can help you sell up • Work with those individuals who have the power to
approve and/or can influence the approvers
• Publish updates and incremental successes
pg 26 Proprietary and Confidential
MDM Decisions Made by DG
Category Decision Entity Types • What type of data will be managed in the MDM Hub
• What are the agreed upon definitions of each type • What is the required cardinality between the entity types • What constitutes a unique instance of an entity
Key Data Elements • Purpose, definition and usage of each data element
Hierarchies and Relationships
• Purpose, definition and usage of each hierarchy / relationship structure
Audit Trails and History • How long do we have to keep track of changes
Data Contributors • What type of data do they supply • Why is this needed • At what frequency should they supply it • What should be taken for Initial load versus ongoing
pg 27 Proprietary and Confidential
MDM Decisions Made by DG (cont.)
Recommendations Meeting – Master Data Management (MDM) Assessment 071411 Category Decision
Data Quality Targets • How good does the data have to be • Root cause analysis
Data Consumers • Who needs the data and for what purpose • What do they need and at what frequency
Survivorship • What should happen when…
Lookups • Which attributes are lookup attributes • What are the allowable list of values per attribute • How different are the values across the applications
and how do we deal with inconsistencies
Types of Users and Security • What types of users have to be catered for • Can they create, update, delete, search • Can they merge, unmerge
Delete • How should deletes be managed
Privacy and Regulatory • Privacy and regulatory issues
pg 28 Proprietary and Confidential
Customer Data Governance Stewardship Activities
Business Request Change Request
Hierarchy Management Match/Merge
Data Quality Customer Search
Data Stewardship
pg 29 Proprietary and Confidential
[ ENSURING SUCCESS ]
pg 30 Proprietary and Confidential
Seven Reasons Why MDM Needs Data Governance
Source TDWI - Seven Reasons Why MDM Needs DG
1 • MDM needs DG’s collaborative environment
2 • MDM needs DG’s stewardship capabilities
3 • MDM needs DG’s change management process
4 • MDM needs DG’s mandate
5 • MDM needs DG as it grows into enterprise scope
6 • MDM needs DG’s guidance as it matures into new generations
7 • MDM needs DG to support its priorities
pg 31 Proprietary and Confidential
Success Measures
People
# of DG decisions backed up by the Steering Committee (SC) # of approved projects from DG
# of issues escalated to SC and resolved # of data owners and data managers identified
DG adoption rate by enterprise personnel
Process
# of data consolidated processes # of approved and implemented standards, policies, and processes to effectively manage core
business data # of consistent data definitions (consistency on how core business data is defined and used
across enterprise, independently of a specific initiative or context) Existence of and adherence to a business request escalation process to manage disputes
regarding data
Integration into the project lifecycle process to ensure DG oversight of key enterprise initiatives
Technology
# of consolidated data sources consolidated # of data targets using mastered data Presence and usage of a unique identifier(s) # of address exceptions Data integrity across systems
Records/data aged past target MDM Hub availability # of hours spent investigating, cleaning
master data % of matched records # of new records loaded
Proprietary & Confidential
The First Step in EIM
Contact Info www.firstsanfranciscopartners.com
Kelle O’Neal [email protected]
415-425-9661 @1stsanfrancisco