Regulated Industries Research
• Reference Gartner’s research to illustrate the ubiquity of the data quality challenge.
Estimated annual costs for data quality issues
$100M+ $50M+ $10M+ $5M+
Don't Know
Business Strategies Call for Revenue Growth, Reduced Costs and New Connections With Customers
* Not an option that year
Business Strategies
Ranking 2011 2010
Ranking of business strategies CIOs selected as one of their top three in 2012.
2009 2008
2012
Increasing enterprise growth 1 1 * * *
Attracting and retaining new customers 2 2 5 4 2
Reducing enterprise costs 3 3 2 2 5
Creating new products or services (innovation) 4 4 6 8 3
Delivering operational results 5 9 * * *
Improving efficiency 6 8 * * *
Improving profitability (margins) 7 21 * * *
Attracting and retaining the workforce 8 12 4 3 6 Improving marketing and sales effectiveness 9 18 * * * Expanding into new markets and geographies 10 11 13 10 4 Improving governance, compliance, risk, security 11 10 11 12 14 Implementing finance and management controls 12 25 * * *
Improving business processes 13 5 1 1 1
Governance Foundations
Legal charter
Amendments and regulations
Data governance
Standards
Policies
Guidelines Principles
The Goldilocks Principle of Governance
Find the Burning Bridges
Translate Tech Lingo into the
Business Language
10
Tech Lingo Business Language
We improved data quality by remediating CR
#I8-123487 that occurred due to the contradictory data in the authorization message. The issue in fields 18.1 and 22.2 combined was uncovered by the data quality monitor and consequent steps of the DMO together with Region West
$1,825,000 is the annual recovered transaction value delivered jointly by the Data Governance Program and Western Region
• Businesspeople and IT people each have their own perceptions but data governance is about business outcomes
(50) 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 0 5 10 15 20 25 30 35 V a lue re c ov e re d ($, 0 0 0 )
No. calendar quarters since governance launch
IQ Cumulative Value Net of Cumulative Costs
Build a Compelling Business Case
Report the Business Value of the Program
Think Big but Start Small
• Establish a tactical team
• Choose one problem
- One concept: homeowner, corporate codes
- Three systems: one source, 2 targets
• Articulate the business’s requirements
• Develop a business case
• Design the CDO program
• Establish controls
The “3-Ms”: Models, Metadata, Measures
Standards for Models & Architectures
Standards for Data & Metadata Standards for Analysis of Data Statistical Process Control (6 Sigma) Behaviors
Critical Competencies
• Strong commitment to data management
• Business executive support seems quite strong
• Critical data elements are known
• Senior business executives care
• CIO is engaged
• Data quality is reasonably good
• Talented technical people in the program
Critical Deficiencies
• “We’ve been working on this since 199n”—leads to early despair and the “let’s go to the next page of the report” syndrome
• Progress reports contain details of peoples’ jobs not their business accomplishments
• The program design is tactical whereas it should be strategic
• The program is IT, not business, focused
• The Steering Committee and Working Groups are not focused on the right things—policy and monitoring
Critical Deficiencies
• The program is tackling far too many data elements—don’t boil a lake or an ocean
• No business rationale or explanation for actions.
• Line of business managers not properly engaged
- Need senior business data stewards - Need senior IT data custodians
• Management and personnel changes result in strategy and implementation discontinuities
• Decision-making authorities for data and metadata are not articulated clearly
Critical Deficiencies
• Metadata is NOT an executive concern
• Data architecture is critical and not on track
• No master data management program.
• Analytics is disconnected from the program.
• Too many data owners—pick one per concept
• Improper data integration model
• BI infrastructure disconnected from the program
• Education and training program missing
MDM and the Art of Motorcycle
Maintenance
Master Data Management (MDM) is an IT process in which business takes
leadership to specify, improve, and maintain
current, reliable, accurate, consistent, and valid lists of the enterprise’s critical information – its Master Data
Foundations of Data Governance
Knowledge Domain v. IT Responsibiliti es
Architecture Infrastructure Applications & Services
IT Portfolio Management
Strategy Write policies and standards for master data design
Establish a master data program for the enterprise Develop data services platform (DSP) capability Data will be treated as a strategic asset
Roles Chief data architect must approve new database schema
Chief data officer is responsible for all data services
Data management organization (DMO) delivers data architectures Enterprise architecture (EA) delivers conceptual data models
Behaviors Use only an approved data modeling tool Services must use data services platform (DSP) Applications must access data using the DSP Use software as a service (SaaS) for email servers
Funding Data governance council may veto project funding DBMS licenses require 5 year of amortization DSP costs will be allocated across all projects
Master data must be used for all services
Business Strategy, Risks, Goals, and Priorities
Legal Framework
Management Preferences
Data Quality Management
• Understand master data
and important data fields
• Apply Data Quality best
practices
Develop a Master Data Error Alert Process
Is the root cause internal or external? Monitor raises a data error alert
Data from source
Work with trading partners on a solution plan
Partner supports a solution plan?
Work with IT on solution
Monitor benefits
Assist the trading partner’s IT group with problem resolution
Yes No
Yes
No
A Data Governance Roadmap
• Build a vision for success – raise awareness
• Survey data creators, consumers, and custodians
• Analyze selected data element and systems
• Learn the compelling stories – repeat them often
• Choose a problem and correct it
• Learn from failures and roll-out governance
Data Governance and Management
• Build a roadmap for successful data management.
- Think big, but start small.
- Get buy-in from all stakeholders.
- Formalize the data governance processes and teams.
• Find one important problem and fix it.
- Assign stewardship and custodianship responsibilities. - Determine the value of the solution.
• Advance and improve more data and systems
Recommendations
Reorganize the program
Assign data decision-making and accountability to line
business and IT managers
Adapt data governance to the business culture.
Remember that behavioral change forces people to
automatically erect roadblocks.
Remove roadblocks to program success.
Execute data governance programs using:
Rationalize data sources for remediation
Build data definitions – conceptual, logical, and physical
Create quality metrics and measure business value
