Building Effective Data Governance: Capstone BI Summit

Capstone BI Summit
Building Effective Data Governance

Jeff Block
Capstone Consulting
Thursday, December 4, 2008

© Copyright 2008 Capstone Consulting, Inc.
Capstone Consulting, Inc

What is Business Intelligence?

BI is not about delivering access to a massive
repository of data, often unconstrained and
overwhelming. BI is about delivering highly relevant,
highly valuable applications, reports, and dashboards,
designed to maximize the user’s ability to gain specific,
actionable knowledge from corporate data.
Of course – and this can’t be overstated – this requires
a culturally-relevant, well-supported governance
model and a highly-specialized, flexible data
architecture, both optimized for this purpose.


What is Data Governance?

A system of decision rights and accountabilities for
information-related processes, executed according
to agree-upon models, which describe who can
take what actions with what information, and when,
under what circumstances, using which methods.
Data Governance is the organizational bodies,
rules, decision rights, and accountabilities of people
and information systems as they perform
information-related processes.



Data Governance is
“how to decide how to decide about our data”.



• Information-related…
– Systems
– Processes
– Rules
– Decision rights
– Organizational structures
– Accountabilities
• … which govern enterprise information


Data Governance Key Attributes

• Commitment has to be to the long term
• Development will be systematic and continuous
• An “initiative”, not a “project”
• Must be personalized to fit the business
– Structured
– Formally defined (signed charter)
– Documented processes
• Must be compatible with the culture
– Will succeed and become part of it, or fail
– Rarely is there middle ground


Why do Organizations
Implement Data Governance?

Data Governance is
typically motivated by one of six key “Focus Areas”


Data Governance Focus Areas

• Policy, Standards and Strategy
– Provide consistent cross-functional leadership
• Data Quality
– Provide improved quality, integrity, data usability
• Architecture and Integration
– Support established enterprise architectural
policies and standards
– Focus cross-functional attention on integration
challenges


Data Governance Focus Areas

• Privacy, Compliance and Security
– Assess risk, ensure privacy and security of data,
legal, regulatory, contractual, and architectural
compliance
• Data Warehousing and Business Intelligence
– Support BI/EDW initiatives by managing and
providing oversight over EIM
• Management Support
– Facilitate alignment between the business, IT and
leadership of data-related initiatives


How do we Build
Data Governance?

Data Governance is
built systematically and has a lifecycle


Building a
Data Governance Program

DGI’s Seven Lifecycle Phases
1. Develop a value statement
2. Prepare a roadmap
3. Plan and Fund
4. Design the Program
5. Deploy the Program
6. Govern the Data
7. Monitor, Measure and Report


Vision … Critical and First Develop a Value
Statement

• Ask yourself
– What will the organization look like if we have
effective data governance?
• Or, ask yourself
– What if we still DON’T have it five years from now?

• Align vision with strategic values of the business
– Give answers only in the language / currency the
business cares about


Universal Business Drivers Develop a Value
Statement

• Increase revenue and value
• Reduce cost and complexity
• Ensure survival by mitigating risk and
enforcing constraints
– E.g. – vulnerabilities, compliance, security,
privacy, etc

Every initiative, including data governance,
must serve these fundamental drivers.


The Three Branches of Develop a Value
(Data) Government Statement

• Executive
– Provide ongoing services to data stakeholders
• Legislative
– Proactively define rules
• Judicial
– Resolve issues arising from non-compliance

These three course-grained activities define
the governance board’s mission to
accomplish the vision.


Strategic Goals Develop a Value
Statement

• Strategic goals fall out of mission
• Write goals for the 4 P’s
– People (data stakeholders)
– Programs (other initiatives)
– Projects (software development or otherwise)
– Processes (sometimes also called “professional
disciplines”)
• Make them SMART
– Specific, measurable, actionable, relevant, timely


Costs and Benefits Develop a Value
Statement

• Benefits first
– How will it benefit the organization?
• Costs second
– What will it cost the organization?
• Typical units of measure
– Dollars
– Time
– Resources
– Politically
– Attrition and other human costs
• Speak your management’s language


Data Governance Roadmap Prepare a Roadmap

• Targeted at executive management
• Timeline of milestones
– What value is delivered at each milestone?
• Management must know
– When will we return on our investment?
– What are the hurdles along the way?
– What will we get in the short- and long terms?
– What are the costs and benefits?


Universal Components of a
Plan, Fund, Design

• Purpose and Rules of Engagement
– Vision
– Mission
– Strategic Goals
– Key Success Criteria / Metrics
– Funding Strategy
– Data Rules and Definitions
– Decision Rights
– Accountability


Universal Components of a
Plan, Fund, Design

• People and Organizational Bodies
– Data Governance Office
• Architects, Managers, Stewards
– Data Stakeholders
• Ongoing Processes
– Data Quality
– Data Cleansing and Conformity
– Master Data and Metadata Management
– Change Management


Program Funding Strategies Plan, Fund, Design

• New IT expense line item
– BI / EIM gets its own budget
– Continual game of justifying expense
• Shared usage model
– Business functions who use BI / EIM systems
each contribute to budget
– Multiple variations
• Cost proportional to usage and/or data volume
• Cost evenly distributed


Program Funding Strategies Plan, Fund, Design

• Invisible IT expense
– Just keep increasing IT’s budget as BI / EIM
systems grow
– Bad plan!
• Lots of room for unique culture of the
organization
– Other methods used?


What’s being funded? Plan, Fund, Design

• Data Governance Office
– Several full time roles in even smaller organizations
• Time / effort of existing business and IT resources
– Might ultimately hire more people for existing jobs (DBA, etc)
• Additional hardware
– DW appliances (e.g. Teradata, Cognitio, etc)
– More application and database servers
• Additional software
– Additional database licenses (e.g. Oracle, SQL Server, etc)
– BI presentation (e.g. Cognos, Hyperion, Alalysis Srvcs, etc)
– ETL tools (e.g. Oracle Data Builder, Tibco, etc)
• Contractor resources to guide process


Roles on the
Plan, Fund, Design
Data Governance Board

• Chairman
– Leads the overall EIM / data warehouse initiative
• Business Function Directors
– Representatives from each business area
• Marketing Director
– Responsible for corporate marketing programs
• User Support and Education Director
– Responsible for user education, consulting, and support
• Data Quality Director
– Responsible for data quality programs, policy, and process


Roles on the
Plan, Fund, Design

• Security Director
– Responsible for security programs, policy, and process
• Data Warehouse Manager
– Responsible for leveraging data warehouse data to serve the
business user community
• Data Stewardship Manager
– Team leader for data stewards
• PMO Director
– Responsible for all EIM project management, including
leading PM team
• Lead Data Architect
– Responsible for all data architecture, including leading data
architect team


Roles on the
Plan, Fund, Design

• Lead EIM Architect
– Responsible for all EIM tool architecture, including leading
EIM architect team
• Source System Liaisons
– Representatives from each source system, responsible for
communication with teams responsible for those systems
• Master Data Manager
– Responsible for master data management system
• Metadata Manager
– Responsible for metadata management system
• Data Warehouse Operations Support
– Responsible for the ongoing health, maintenance, and issue
management related to the data warehouse


Multiple Tiers Plan, Fund, Design

• Effective DGB is tiered
– Even for small organizations

• Typically two tiers
– Tier 1: Management
– Tier 2: Execution
• In very large organizations, sometimes add a
third tier
– Separate “management” and “strategic leadership”


Multiple Tiers Plan, Fund, Design

Management
Chairman Data Warehouse Manager
Business Function Directors Data Stewardship Manager
Marketing Director Lead Data Architect
User Support and Education Director Lead EIM Architect
Data Quality Director Master Data Manager
Security Director Metadata Manager
PMO Director Data Warehouse Operations Support Manager

Execution
Chairman Data and EIM Architects
Business Function Liaisons Project Leaders
Source System Liaisons Developers
Data Stewards Support Engineers


Plan for Change Plan, Fund, Design

• Change is inevitable
– Trends in business come and go
– Information needs change
– Data volumes increase
– New systems are developed
– Partnerships with third parties change
• Decisions about how to treat, access, clean,
and enforce rules about data will not only
continue, but they’ll likely also proliferate.


Iterative Process Deploy

• Iterative Process
– Continual delivery of value in short intervals
• BI Applications updated every week or two
• Data Marts updated every month or two
• Data Warehouse and associated Governance updated
every quarter or two
– Always be marketing the next release
• Never make them wait too long for value
• You will NOT conquer world hunger in one
iteration
– if you try you will fail


Phased Rollout Deploy

• Phased Rollout
– A data governance board either exists or it doesn’t
– But what it governs must start small and expand
over time
– Build governance as needed to support user
requirements as they’re deployed
– Build governance ahead of data and data ahead
of systems
• The revolutionary war approach probably
won’t work for you
– Lots of bloodshed, and the king will win


Responsibilities of the
Govern the Data

• Meet regularly on a defined, predictable schedule
• Provide high-level direction for the EIM program
(i.e. – strategic guidance to all teams involved)
• Develop information process and policy
• Recommend changes to business processes and
policies
• Establish and maintain access rights to data
• Prioritize and oversee execution of EIM projects
• Resolve cross-functional data issues


Responsibilities of the
Govern the Data

• Establish schedules and priorities for
development phases, enhancements, and
maintenance of the data warehouse
• Manage data quality problems, production data
fixes, data stewardship issues, and other daily
project management decisions
• Monitor impact of business rules on corporate
information
• Maintain master data- and metadata
management systems
• Ensure quality of data and EIM systems, policies,
and processes


Goals of Data Governance Govern the Data

• Enable better decision-making
• Contribute to vision for data and data strategy
• Reduce operational friction and inefficiency
(therefore cost)
• Identify, describe roles for, and protect the
needs of data stakeholders
• Train management and staff to adopt
common approaches to data issues
• Build standard, repeatable processes



• Coordinate efforts across functional areas to
reduce costs and increase effectiveness
• Provide accountability and ensure
transparency of processes
• Create, review, approve, monitor and align
policies and standards
• Establish and enforce decision rights, access
management, and security policy
• Contribute to business rules involving data



• Ensure consistent data definitions (single
source of truth)
• Measure and clarify the value of data assets
and data-related efforts
• Provide oversight and assistance for data-
related projects


Ongoing Processes Managed by
Govern the Data
the Data Governance Board

• Aligning Policies, • Resolving Issues
Requirements and • Specifying Data Quality
Controls Requirements
• Establishing Decision • Building Governance into
Rights Technology
• Establishing • Stakeholder Care
Accountability • Communications
• Performing Stewardship • Measuring and Reporting
• Managing Change Value
• Defining Data


Do you make watches
Monitor, Measure, Report
or wear them?

Watch makers
make watches and never see them again

Watch wearers
get the watch they need and use it ever day to
understand what time it is


Care and Feeding Monitor, Measure, Report

• DGO requires significant care and feeding
• Can’t just wind up like a clock then go on
vacation
– Monitor
• What’s working / failing? Who’s involved / aloof?
– Measure
• How successful are we? How good or bad is it?
• Do we objectively know where we are in relation to our
goals?
– Report
• Does executive management see the value? Current and
future users? Customers?


Care and Feeding Monitor, Measure, Report

• Once you know…
– Because you monitored and measured
• Pour into what’s working
– Play to your strengths
• Fix what’s broken
– Iteratively, methodically
• Commission the next iteration
– Don’t stop now; you’re always “just getting
started”


Q&A Group Discussion


Building Effective Data Governance: Capstone BI Summit

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