The document explores the transformation of business operations through Business Intelligence (BI), emphasizing the importance of data-driven decision-making. It highlights the challenges in BI implementation, such as data quality issues, insufficient company-wide adoption, and the illusion of BI efficiency, along with best practices for overcoming these barriers. The document also discusses various management models, strategies for enhancing data utility, and the need for a structured BI approach to improve organizational efficiency.

1. Exploring Business Intelligence:
How BI Transforms Business
Operations and Fuels Growth

2. • Introduction
• Challenges in Business Intelligence
• Illusion of BI Efficiency
• BI Best Practices Overview - DAMA DMBOK
• Management Models of BI
• Leveraging Business Intelligence
• Q&A and Closing Remarks
Webinar Agenda

3. Understanding
Business Intelligence

4. BI is a process that transforms data into actionable
information and insights to aid business decisions.
Goal: To enable data-driven decision-making by
providing valuable business insights.
Introducing BI
DATA SOURCES INTEGRATION
STORAGE &
PROCESSING
DATA
ANALYTICS
REPORTING

5. COMMON ISSUES
STRATEGIC & OPERATIONAL
CHALLENGES
§ Insufficient company-wide adoption
§ Slow adoption of mobile BI solutions
§ Poor data quality
§ Lack of a clearly defined BI strategy
§ Limited functionality in the BI system
§ Slow queries and inadequate
database performance
BI Implementation Challenges
TRAINING AND COMPETENCIES
TRANSITION AND ROI
EVALUATION
§ Lack of willingness to learn BI tools
§ Shortage of BI competencies
§ Data visualization and dashboarding
issues
§ BI system replacement challenges
§ ROI assessment and project approval
§ Implementing analytical projects
with data from various systems

6. Overcoming Top BI Implementation Challenges
Source: bi-survey.com

7. Impact of Company Size on BI Challenges
Source: bi-survey.com

8. 1. Lack of data governance
2. Inadequate data literacy
3. Insufficient analytical tools
4. Data silos
From Data Possession to Effective Utilization
KEY CONTRIBUTING FACTORS:
Identifying the Gap:
Despite being data-rich,
many companies are not fully leveraging
their data for insightful decision-making
due to various barriers.

9. ~80% of reports die within 3
months due to process
changes, business focus shifts,
or the departure of key
personnel.
~10% of reports experience
minimal traffic — a handful or
dozens of users per month.
~2% deliver stable targeted
results.
80% 10% 2%
The Illusion of Efficiency in BI Implementation

10. BI Best Practices

11. • Assessing organizational stage
• Charting a clear path forward
• Fostering systematic improvement
Unveiling the DMBOK2 Pyramid
THE PATH TO EFFECTIVE DATA MANAGEMENT:

12. Overview of DAMA Wheel

13. KEY BENEFITS OF DATA GOVERNANCE:
ü Improved data quality
ü Compliance assurance
ü Informed decision-making
Data Governance: Holistic Asset Management
TECHNOLOGIES PEOPLE PROCESSES POLICIES AND
STANDARDS

14. § Data Accuracy
§ Accessibility
§ Comprehensibility
§ Reliability
§ Timeliness
§ Security
CHALLENGES IN DATA UTILIZATION:
Enhancing Data Utility
§ Improving Accuracy
§ Ensuring Accessibility
§ Boosting Comprehensibility
§ Augmenting Reliability
§ Maintaining Timeliness
§ Upholding Security
STRATEGIES TO ENHANCE DATA UTILITY:

15. Key Stages of a Data Governance Project
INITIATION
§ Business Case
§ Sponsorship and
Leadership
§ Vision and Program
Document
§ Funding
§ Team
§ Launch
DESIGN
§ Standards/Policies
§ Roles
§ Responsibilities and
Accountability
§ IT and Business Rules
§ Glossaries and Data
Catalogs
§ Collaboration
EXECUTION
§ Research
§ Procedures
§ Rules
§ Activities
§ Measurements
§ Decisions
§ DQ Projects
§ Administration
DEVELOPMENT
§ Data, Business,
Personnel, Technology
Changes
§ Training and Growth
§ DG Maturity
§ Monitoring/Audit
§ Proactive Practices
§ ROI

16. VISION
D&A OF BUSINESS SENTIMENT
PROCESSES AND SERVICES
PEOPLE AND ROLES
TOOLS AND OPERATIONS
EFFICIENCY
ROADMAP
BI Strategy: Foundation for Data Analysis

17. Life with a BI Strategy
§ Proactivity
§ Effective Discussions and Decision-Making
§ Attracting and Retaining Key Experts
§ Enhanced Onboarding
§ Resource and Budget Protection
§ Directional Clarity
§ Enhanced Efficiency
§ Sustainable Future
ADVANTAGES OF A BI STRATEGY:
REGULAR UPDATES AND ANALYSIS
PROFESSIONAL MOTIVATION
CLARITY AND GOAL SETTING

18. Management Models of
Business Intelligence

19. BI Management Models
CENTRALIZED DELEGATED SELF-GOVERNING

20. Centralized Reporting Challenges
SINGLE VERSION OF TRUTH & HYBRID CERTIFICATION
PROACTIVE BUSINESS & DATA CONSULTING
SCALING SERVICE VIA ROLE-BASED WORKSTATIONS
ROLE-BASED ACCESS & ROW-LEVEL SECURITY
COMMUNICATION STRATEGY FOR UPDATES
ROBUST DATA QUALITY CONTROL
ENHANCED DATA VISUALIZATION EXPERTISE

21. • Lack of Quality Governance
• Loss of a "Single Version of Truth"
• New IT Responsibilities
• Reduced IT Burden
• Democratization of BI Access
• BI Competence Development
NEGATIVE ASPECTS
POSITIVE ASPECTS
Exploring Self-Service Business Intelligence (SSBI)

22. § Lack of data-handling competencies
§ Lack of specific reporting development competencies
§ Users still relying on BI teams for report modifications
§ Creation of too much content, most of which is
irrelevant
§ Reluctance to spend much time away from
primary work
Causes of SSBI Fails
USER-RELATED CHALLENGES

23. Causes of SSBI Fails
§ Difficulty in discovering available data sources
§ Difficulty in selecting the right source among
many similar ones
§ Difficulty in understanding data, field names,
and values
§ Field addition, source modification requires
IT support
SERVICE-RELATED CHALLENGES

24. How to Increase Efficiency in SSBI
REDUCING DEPENDENCY ON POWER USERS
STRUCTURE USER SUPPORT PROCESS
INVEST IN CONTENT REGULATION
FOCUS ON USER TRAINING PROGRAM
CREATE A BI COMMUNITY
RE-THINK CENTRALIZED ANALYTICS

25. User Classification in SSBI: Aligning Tools to Needs
CASUAL USERS POWER USERS SUPPORT FOR SELF-SERVICE
§ Data Consumers (60% of emp)
§ Data Experts (30% of emp)
§ Data Analysts (8% of emp)
§ Data Scientists (2% of emp)
§ Data Curators
§ Data Engineers and Architects

26. RELUCTANCE TO SHARE DATA COMPLEX ACCESS MODEL NEED FOR CENTRALIZED SUPPORT
Departments might be reluctant to
share their reports due to the
additional requirements & support
needs from other user groups.
Designing and managing a role-
based access model can be complex
and resource-intensive, particularly
for individual departments.
Some functions might not be
ready for self-service BI solutions
and would prefer relying on a
specialized centralized BI team.
Why SSBI Is Ineffective

27. Governed Self-Service + Guided Reporting = <3
Source: data-nature.com

28. The Ways to Leverage
Business Intelligence

29. The Ways to Leverage Business Intelligence
DATA QUALITY
MANAGEMENT
PROCESS
CONTENT
MANAGEMENT
UX PROBLEMS
IMPLEMENTATION
OF ANALYTICAL
WORKSTATION
DESIGN GUIDE
CONTENT
PROMOTION
CONTENT
UTILIZATION
DATA LITERACY BI CHAMPION
D&A SERVICE
CATALOG
MAPPING TOOLS
WITH POWER APPS
BI IN SLACK AND
TEAMS
AUTOML &
SELF-SERVICE ML
AGILE BI
DEVELOPMENT

30. Analytics Maturity
Assessment

31. Analytics Maturity Matrix Across Projects
Source: data-nature.com

32. Analytics Maturity Model
Source: Gartner

33. § Highlighted challenges in BI implementation
and usage
§ Unveiled the gap between data possession and
effective utilization
§ Introduced DAMA DMBOK best practices and BI
strategy approaches
§ Discussed various BI management models and
efficiency improvement strategies
§ Analyzed analytics maturity across departments
to gauge current BI capabilities
Conclusion and What is Next
BI STRATEGY
DEVELOPMENT
BI PROJECT
MANAGEMENT
DEVELOPMENT OF
BI
ORGANIZATIONAL
FRAMEWORK
CREATION AND
MANAGEMENT
OF ANALYTICAL
CONTENT
INTEGRATION
AND
MANAGEMENT
OF DATA ASSETS
D&A ASSET
MANAGEMENT
WRAPPING UP

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