Prototype Driven Requirement Elicitation for
Business Intelligence
September 11, 2015
Mike Krajnak
Steve Strohl
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mkrajnak@icct.com
sstrohl@icct.com
Agenda
Introduction
Why Projects Fail
Why Data Governance
Requirements Prototyping
Success Stories
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About Mike
Professional
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•
•
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Business Intelligence Requirements
Practice Lead
CBIP, CDMP
35+ years of IT experience
20+ years building analytical solutions
Little Known Facts
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•
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•
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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
About Steve
Professional
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•
•
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Master Data Management and Data
Governance Practice Lead
Sr. Business Intelligence Architect
35+ years of IT experience
15+ years building analytical solutions
Little Known Facts
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•
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
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Business Intelligence (BI) Overview
Raw Data
Fuzzy
business
rules
Unclear data
Relationships
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Meaningful
Information
Reports
Getting the right
information . . .
Decisions
. . . to the right people . . .
Opportunities
. . . at the right time.
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Business Intelligence Requirement Gathering Landmines
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
Time
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
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(* Young, Ralph R. Effective Requirements Practices. Boston: Addison- Wesley, 2001.)
Multiple Approaches to Solve this Problem
Monolithic Waterfall
Cons
Pro
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Pure Agile
Pro
Cons
Long time between
Complete and robust
requirements and deployment requirement planning
Business and IT working
together
Loss of connection to the big
picture causes requirement
drift
Requirement changes have
serious project impact
Tangible defined handoffs
between stages
Flexible to handle changes
Lots of rework due to
requirement changes
Linear dependencies cause
project delays
Stable Processes
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
Water
Scrum
Fall
Monolithic Waterfall
Cons
Pro
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Pure Agile
Pro
Cons
Long time between
Complete and robust
requirements and deployment requirement planning
Business and IT working
together
Loss of connection to the big
picture causes requirement
drift
Requirement changes have
serious project impact
Tangible defined handoffs
between stages
Flexible to handle changes
Lots of rework due to
requirement changes
Linear dependencies cause
project delays
Stable Processes
Clarify requirements as you go Sprints too small to create
deployable artifacts
© 2015, Information Control Company
Our Approach
“Water Scrum Fall”
Process
Design & Build
8-12 weeks
Data Governance
(common thread)
Planning
6 weeks
Requirements &
Prototype
6-8 weeks
Test
1-3 weeks
Release
2 weeks
Total: 23-31 weeks
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Why Data Governance?
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
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Data as an Asset:
The Problem
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
No governing body
No data policies, procedures or processes
No business glossary
No data quality checks
No remediation process
The Result
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




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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
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Bad Data
Good Data
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Data Governance Program
View data as an asset!
Data
Remediation
Data Quality
Rules
Roles and
Responsibilities
Governing
Body
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Policies,
Procedure
And Processes
Our Approach– Planning
• 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
Planning
6 weeks
o
o
o
o
o
Project manager
BIBA – Business Intelligence Business Analyst
Data Analyst
User Interface Specialist
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
Requirements
&
Prototype
6-8 weeks
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Our Approach– Requirements and Prototype
SME Interview  Business Questions  Glossary  Data Governance  Business Model  Working Prototype
Artifacts Created
Interview Guide
List of Business Questions
Business Terms
Business Glossary
Facts Qualifier Matrix
Source Qualifier Matrix
Data Profiling Results
Logical Business Model
Physical DDL
Semantic layer
Prototype Reports
Functional Specification
Business Requirement Document
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Data Stewardship Roles
Contributors
Facilitator
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• Identify the people responsible for identifying the business element and
data quality rules
• 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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Our Approach– Term & Rules Work Flow
Interviews
SME’s
Approvers
Approvers
Eval
Consumer
(read/write)
Contributor
Business Q’s
Eval
Reviewer
(read only)
Business
Terms
Submit
Business
Abstain 
Terms
Business
Business
Terms
Approved 
Business
Terms
Rejected 
Terms
Business
Approved 
Business
Terms
Rejected 
Terms
Approved 
Publish
Consumer
(read only)
Published Area
We have now reached a Consensus!
Review,
Submit
All business terms have been approved.
Reviewer
(read only)
Approved terms can now be used to build the modeling
objects and prototype package
Facilitator
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Prototype - Which would you rather have?
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Manual or online requirements
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Paper based or accessible artifacts
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Wire frames or working prototype
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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
DDL
Table with
sample data
Semantic Model
Documentation
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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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Success Stories
Ten Success Factors
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
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Cooperation between Business and IT
Championing by the Business Stakeholder
Narrow Defined Scope (No Boiling the Ocean)
Prioritized list of Business Questions
Having a PM that understands BI
Mitigation Strategy for Data Integrity Issues
Following Data Governance Principles
Available Personnel with Correct Skills
Using accelerator tools (Balsamiq, Consensus)
Stand up and Status meetings
Retirement
6 month project (Balanced Score Card)
Production results after 2 months
Continued rollout at month 4 and 6
Restaurant
4 month project
Produced dashboards across 4 subject areas
Colocation, management support
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