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Prototype Driven Requirement Elicitation for Business Intelligence
September 11, 2015
Mike Krajnak [email protected] Strohl [email protected]
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Agenda
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Introduction
Why Projects Fail
Why Data Governance
Requirements Prototyping
Success Stories
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About Mike About Steve
• Started career Battelle on defense systems• Spent 7 years in Alaska
– Lead Architect on Exxon Valdez Oil Spill Project
– Reported BI data to national news
• Master Data Management and Data Governance Practice Lead
• Sr. Business Intelligence Architect• 35+ years of IT experience• 15+ years building analytical solutions
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Little Known Facts
ProfessionalProfessional• Business Intelligence Requirements
Practice Lead• CBIP, CDMP• 35+ years of IT experience• 20+ years building analytical solutions
Little Known Facts• Started career as system’s programmer• Scoutmaster for 7 years in the Boy
Scout troop that Steve’s father started• Met wife on trip to Israel• Traveled to China to adopt daughter
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Business Intelligence (BI) Overview
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Raw Data
Fuzzy business
rules
Unclear data Relationships
Meaningful Information
Reports
Decisions
Opportunities
Getting the right information . . .
. . . to the right people . . .
. . . at the right time.
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Business Intelligence Requirement Gathering Landmines
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o It is estimated that 85% of defects in developed software originate in the requirements*
o Fixing defects is costly
o Unrealistic scope and expectations hurt timelines o Multiple data sources and fuzzy business rules cause complexity
o Business requirements are hard to articulate (users do not know what they want)
o Lack of data governance (data quality/ Integrity) cause confusion
(* Young, Ralph R. Effective Requirements Practices. Boston: Addison- Wesley, 2001.)
$
Time
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Multiple Approaches to Solve this Problem
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Monolithic WaterfallCons Pro
Long time between requirements and deployment
Complete and robust requirement planning
Requirement changes have serious project impact
Tangible defined handoffs between stages
Linear dependencies cause project delays
Stable Processes
Pure AgilePro Cons
Business and IT working together
Loss of connection to the big picture causes requirement drift
Flexible to handle changes Lots of rework due to requirement changes
Clarify requirements as you go Sprints too small to create deployable artifacts
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Combine the Best of Both into a Hybrid Approach
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Monolithic WaterfallCons Pro
Long time between requirements and deployment
Complete and robust requirement planning
Requirement changes have serious project impact
Tangible defined handoffs between stages
Linear dependencies cause project delays
Stable Processes
Pure AgilePro Cons
Business and IT working together
Loss of connection to the big picture causes requirement drift
Flexible to handle changes Lots of rework due to requirement changes
Clarify requirements as you go Sprints too small to create deployable artifacts
Scrum
Fall
Water
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Our Approach
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Design & Build
TestRequirements & Prototype
Data Governance(common thread)
Planning Release
6 weeks
8-12 weeks
6-8 weeks 1-3 weeks 2 weeks
Total: 23-31 weeks
“Water Scrum Fall” Process
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According to the Data Governance Institute, Data Governance is …
“The organizational bodies, rules, decision rights, and accountabilities of people and information systems as they perform information-related processes.”
It refers to the operating discipline for managing data and information, including the:• People• Processes• Technology
And categorizes data as a key enterprise assets
Why Data Governance?
Technology
Process
People
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Data as an Asset:
No governing body No data policies, procedures or processes No business glossary No data quality checks No remediation process
The Problem
Badly formed or incorrect social security numbers Incorrect or obsolete addresses Incorrect dates (birth, admittance, discharge, policy etc…) No standard descriptive or type values Duplication of data across source systems Different data for the same person across source systems
The Result
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Bad Data
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Data Management
System
Data Management
System
Good Data
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GoverningBody
Roles and Responsibilities
Policies, ProcedureAnd Processes
Data Quality Rules
Data Remediation
Data Governance Program
View data as an asset!
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Our Approach– Planning
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Planning
6 weeks
• Identify the business needs, suggest projects to meet those needs
• Validate that the pre-requisites can be met on each project• Estimate the costs, and rank them by return on investment• Identify stakeholders to champion the projects. • Find people to fill the roles for the BI project
o Project managero BIBA – Business Intelligence Business Analysto Data Analysto User Interface Specialisto Report Specialist
Deliverables:• Prioritized list of projects with estimated cost and ROI• Roadmap showing project timelines
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Our Approach– Requirements and Prototype
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Requirements &
Prototype
6-8 weeks
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Our Approach– Requirements and PrototypeSME Interview Business Questions Glossary Data Governance Business Model Working Prototype
Artifacts CreatedInterview Guide List of Business Questions
Business Terms Business GlossaryFacts Qualifier MatrixSource Qualifier MatrixData Profiling Results
Logical Business Model
Physical DDL
Semantic layer
Prototype Reports
Functional Specification
Business Requirement Document
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Data Stewardship Roles
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Contributors
• Identify the people responsible for identifying the business element and data quality rules
Facilitator• Identify the person responsible for managing the workflow process
Approvers
• Identify the people responsible for approving, modifying or rejecting the data element or any part of the data element.
Reviewers
• Identify the people who will view the data elements but only have read authority
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Consumer(read only)
Reviewer(read only)
Reviewer(read only)
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BusinessTerms
BusinessTerms
BusinessTerms
Submit
Review,Submit
Eval
Eval
BusinessTermsBusiness
TermsBusinessTermsBusiness
Terms
Publish
InterviewsSME’s
Published Area
Abstain
Approved
Approved
Approved
Rejected
Rejected
We have now reached a Consensus!
All business terms have been approved.
Approved terms can now be used to build the modeling objects and prototype package
Business Q’s
Consumer(read/write)
Facilitator
Approvers
Contributor
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Approvers
Our Approach– Term & Rules Work Flow
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Prototype - Which would you rather have?
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• Manual or online requirements
• Paper based or accessible artifacts
• Wire frames or working prototype
• Which is easier to develop from?
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Consensus Tool- saves 2 weeks of time gathering requirements – links requirements to a logical model
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Consensus Accelerates Output
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Semantic Model
DDL
Documentation
Table with sample data
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Demo the Prototype and Obtain Business Signoff
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Why Prototype Requirements?
1. More Accurate Requirements
2. Less Risk
3. More Satisfied users
4. Less Cost
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Retirement6 month project (Balanced Score Card)Production results after 2 monthsContinued rollout at month 4 and 6
Restaurant4 month projectProduced dashboards across 4 subject areasColocation, management support
Ten Success Factors1. Cooperation between Business and IT2. Championing by the Business Stakeholder3. Narrow Defined Scope (No Boiling the Ocean)4. Prioritized list of Business Questions5. Having a PM that understands BI6. Mitigation Strategy for Data Integrity Issues7. Following Data Governance Principles8. Available Personnel with Correct Skills9. Using accelerator tools (Balsamiq, Consensus)10. Stand up and Status meetings
Success Stories
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Q & A
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