gathering bi requirements -...

Gathering BI Requirements

Jonathan Geiger

Intelligent Solutions, Inc.

November 5, 2014

Sponsor

3

Speakers

Jonathan Geiger

Senior Vice President,


Mike Klaczynski

Senior Data Analyst,

Tableau

Copyright © 2014 Intelligent Solutions, Inc., All Rights Reserved

Gathering BI Requirements

Jonathan G. Geiger

Executive Vice President


[email protected]

www.IntelSols.com

Copyright © 2014 Intelligent Solutions, Inc., All Rights Reserved 5

Session Overview

Need for Requirements Discovery

Knowledge and Skills

Discovery Techniques

Challenges


Role

Requirements definition establishes WHAT

needs to be provided

Within the context of the project scope

To provide the basis for the technical specifications

Requirements definition establish expectations

Based on the analyst’s interpretation of input from

business representatives

I understand what

we need to deliver I’m confident that

my expectations

will be met


Requirements

Characteristics

Specific

Clearly stated

Complete

Fully convey requirement

Traceable

To source

Upstream and downstream

Verifiable

Accurate

Testable


Requirements

Characteristics

Is this realistic for BI?


BI vs. Traditional Projects

Traditional Projects

Process-centric

Definable requirements

Stable requirements

Value often quantifiable

BI Projects

Data-centric

Unknown requirements

Evolving requirements

Value often difficult to quantify


Data Centricity

Data is cross-functional in nature

No single area has full responsibility

Responsibility may not be fully defined

Data governance addresses this

Multiple stakeholders

Not all have stake in current initiative

Not all should have equal voice

Consensus is desirable

Takes time

Not always achievable

Compromises may be needed


Unknown Requirements

Analysts must be business-savvy

Don’t ask: “What do you want?”

Seek business questions to be answered

Based on questions, seek needed data content

Pursue reasons for business questions

Helps identify additional requirements

Consider asking

How would success be measured?

How would others measure your success?


Evolving Requirements

“Now that you gave me what I asked for, let me

tell you want I really want.”*

Business representatives are serious

Needs often evolve during prototyping or as the

project moves forward

Architecture is resilient

Critical consideration is granularity

* Source: Bill Inmon (early 1990’s)


Value Quantification

Tangible savings

Intangible benefits

Market position improvements

Decision improvements

Timeliness improvements

Efficiency improvements

Effectiveness improvements


BI vs. Traditional Projects

BI Projects

Data-centric

Unknown requirements

Evolving requirements

Value quantification


Session Overview




Challenges


Knowledge and Skills:

Industry Knowledge

Business Environment

Competitive Environment

Business Drivers



Company Knowledge

Goals

Strategy

Priorities

Major Functions



Data Knowledge

Data Subjects

Major Entities

Applications

Content Characteristics

Sources

Quality



Business Intelligence

Reports and Queries

Online Analytical Processing (OLAP)

Scorecards

Data visualization

Dashboards

Analytics

Data Mining

Technology



Development and Sustenance

Data Management

Data Quality Management

Data Security

Information Discovery Techniques



Importance

Credibility

Synergy during information gathering

Proactive prioritization


Proactive Prioritization

High

High Satisfaction

High Impact Low Satisfaction

Low Impact Low Satisfaction

High Impact High Satisfaction

Low Impact High Satisfaction

I

m

p

a

c

t Low

Low

•Identify opportunities

aimed at highest

priorities

•Perform initial

screening

•Expand information on

top initiatives

•Select initiatives


Session Overview




Challenges


Interviews

Tailored to participants

Try to understand purpose behind requirements

Gather information on additional contributors

Prepare questions – but be flexible

Should be open-ended

Should not be leading – except for pursuing synergy

May be direct or indirect

Should be adjusted based on responses


Prototyping

Determine high-level requirements

Set expectations

Develop and exercise prototype

Document results

Determine and pursue follow-up actions

Data access and virtualization capabilities

valuable


Agile

Observe agile manifesto

Individuals and interactions over process and tools

Product over comprehensive documentation

Collaboration over negotiation

Responding to change over following a plan

Working sessions

Business and IT involvement

Prototyping common


Other Common Techniques

Facilitation

Data Profiling

Current State Analysis

Surveys


Participants and Roles

Steering Committee Members

Sponsor

Executives

Managers

Analysts


Session Overview




Challenges


Technique Choices

Multiple techniques are available

No single project will use them all

Select appropriate technique based on

Types of information available

People with needed information

Number

Position

Characteristics

Logistics

Timeframe available

Budget


Business Environment

Business changes may take place

Direction may be confidential

Urgency may dictate quick development

Conflicting requirements may exist

Confidentiality issues may exist

Skills may be lacking


IT Environment

Methodology

Traditional

Agile

Technology

Skills


Organizational Issues

Cross-functionality often requires involvement

beyond requesting area

Priorities may differ

Agreement on terms and calculations is needed

Liaisons may impede access to key people

People may resist change

Key people may refuse to get involved


Nature of Requirements

Often complex

Often unknown in advance

Often uncertain

Often evolving

Unplanned data combinations


Summary

BI requirements need to be defined

But the approach requires discovery

Business knowledge is extremely important

Multiple techniques are available

Adjustments are needed for discovery

37

Questions?

38

Contact Information

If you have further questions or comments: Jonathan Geiger [email protected]

Mike Klaczynski, Tableau [email protected]

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