From Data to Decisions: Architecting an Intelligent BI Strategy with AI & Data Science.pdf

This strategic presentation, developed during my MSc Data Science and AI coursework, provides a comprehensive framework for transforming raw organizational data into a competitive advantage through a modernized Business Intelligence system.

It moves beyond traditional reporting by integrating advanced AI and Data Science methodologies, demonstrating expertise in the end-to-end process: from advanced data mining and feature engineering to the implementation of adaptive AI algorithms that enable real-time, predictive decision-making.

Furthermore, the work highlights the critical role of data visualization and domain-specific expertise in translating complex analytical outputs into actionable, executive-level insights, coupled with practical implementation strategies covering team assembly, tool selection, fostering a data-driven culture, and measuring the crucial Return on Investment (ROI) of the BI initiative.

1. “Operating a business based on intuition
instead of data is like taking a cross-country
road trip with a broken speedometer.”

2. $ 33
Billion
Global business
intelligence market
size in 2024
Source: Mordor Intelligence Source: Cloud Computing and Business Intelligence Market Study
Future reach of
Business Intelligence
as predicted
13%
CAGR
(2024 -2029)
84%
of enterprises have
launched advanced
analytical initiatives to
bring greater accuracy
and accelerate their
decision-making

3. Unleashing the
Power of Business
Intelligence
Gaining a Competitive Edge in the
Digital Era
G.D.E.Lakmali
MSC/DSA/063

4. Introduction to Business Intelligence (BI)
“Business Intelligence (BI) is a strategic approach to transforming raw data into
actionable insights, enabling organizations to make informed decisions. It
encompasses a set of technologies, processes, and tools that play a crucial role in
the decision-making processes of modern businesses.”
Facilitates
Informed Decision-Making
Helps to Gain
Competitive Advantage
Enhance
Efficiency & Productivity

5. Data Mining Artificial Intelligence (AI)
Key Factors
in
Business Intelligence
Visualization Expert Knowledge
By Erandika Lakmali

6. Visualization
Extracting Valuable
Insights from Raw Data
Integration of
AI Algorithms
Enabling Adaptive Learning
and Real-time Decision-
making
Enhancing the
Understanding,
Relevance, and
Accuracy of Insights
Derived
Raw Data
Domain-specific
Expertise
Contextual Output
for
Decision-Making
Data Mining
Translating Analytical
Findings into Visually
Intuitive
Representations
Intelligent BI System
By Erandika Lakmali

7. Why Should You
Care About BI?
oInformed Decision-Making
oCompetitive Edge
oEfficiency and Productivity
oCustomer Understanding
oFinancial Performance

8. How BI Drives Competitive Edge
Early Trend Identification
•BI tools analyse historical
and real-time data to detect
emerging trends and shifts in
market demand.
•Early awareness enables
proactive adjustment of
strategies and offerings.
Competitor Analysis
•BI allows monitoring of
competitors' activities and
performance.
•Data-driven insights identify
strengths and weaknesses,
enabling effective responses
to opportunities and threats.
Market Segmentation
•BI helps segment target
markets precisely based on
unique needs and
preferences.
•Tailoring products, services,
and marketing efforts
increase appeal and
competitiveness.

9. How BI Drives Competitive Edge (cont.)
Product Development
•BI insights guide the
development of products
and services aligned with
market demand.
•Identifying gaps, refining
offerings, and introducing
innovations to outpace
competitors.
Pricing Strategies
•BI optimises pricing based
on market dynamics,
competitor pricing, and
customer willingness.
•Ensures competitive pricing
while maintaining
profitability.
Supply Chain Optimization
•BI streamlines supply chain
operations, reducing lead
times and minimising
inventory costs.
•Enhances efficiency,
enabling swift response to
market changes.

10. How BI Drives Competitive Edge (cont.)
Customer Experience
Enhancement
•Understanding customer
behaviours and preferences
through BI enables
personalised interactions.
•Satisfied customers remain
loyal and recommend over
competitors.
Risk Mitigation
•BI identifies and assesses
potential risks in advance,
aiding in developing risk
mitigation strategies.
•Ensures business continuity in
the face of market volatility
Agility and Adaptability
•BI fosters an agile and adaptive
organisational culture.
•Regular monitoring of key
performance and market
indicators enables quick
pivoting to capitalise on
opportunities

11. Success
Stories of
Business
Intelligence
Several real-world businesses have
successfully leveraged Business
Intelligence (BI) to achieve
remarkable growth and innovation.

13. Starbucks: Brewing
Success with Intelligent BI
Data Mining for Store Location Strategies
oStarbucks utilizes data mining to analyze;
•Consumer Demographics
•Population Density and Income Levels
•Traffic Patterns and Public Transport Hubs
oThis comprehensive analysis guides strategic decisions
for selecting successful store locations
Benefits:
oInformed Decision-Making: Data-driven insights
enhance ability to make informed decisions about where
to open new stores
oOperational Efficiency: Efficiently placing stores based
on data analysis contributes to operational success

14. Starbucks: Brewing
Success with Intelligent BI
AI in Customizing Marketing Strategies
oStarbucks leverages AI algorithms to customize marketing
campaigns based on regional preferences and weather
conditions
oExample:
•“Summertide - grab a Frappuccino” promotion during a
heatwave in Memphis
Benefits:
oTargeted Promotions: AI-driven customization ensures
promotional efforts align with local preferences and events
oIncreased Engagement: Dynamic campaigns capture
local market interests, leading to increased customer
engagement

15. Starbucks: Brewing
Success with Intelligent BI
Visualization for Consumer Insights
oStarbucks employs visualization tools to interpret data
gathered from loyalty programs like “My Starbucks
Rewards”
oVisualization aids in understanding consumer behavior,
preferences, and usage patterns
Benefits:
oPersonalized Offers: BI-driven visualizations enable
Starbucks to generate tailored customer offers during
peak purchasing times
oEnhanced Customer Experience: Visualization tools
contribute to a more personalized and engaging
customer experience

16. Starbucks: Brewing
Success with Intelligent BI
Infusing Domain Expertise for Menu
Customization
oStarbucks infuses domain expertise to customize menus
based on regional preferences and cultural nuances
oExample:
•“Starbucks evenings” introduced in areas with high
alcohol consumption
Benefits:
oCultural Sensitivity: Expertise-driven menu adaptations
showcase Starbucks' cultural sensitivity and
responsiveness
oIncreased Sales: Aligning with local preferences results
in increased sales and customer satisfaction

17. Starbucks: Brewing
Success with Intelligent BI
AI & Data Analytics in Supply Chain
Optimization
oData-driven inventory management ensures stock aligns
with customer preferences
o Starbucks uses AI to optimize order fulfillment, reducing
wait times & enhancing the overall customer experience
Benefits:
oCost Savings: AI-driven supply chain optimization
results in significant cost savings through waste
reduction
oImproved Customer Experience: AI in order fulfillment
reduces wait times, enhancing the overall customer
experience

18. oDefine Clear Objectives
•Set specific goals for BI implementation.
•Align objectives with organisational priorities.
oSecure Executive Support
•Gain buy-in from senior leadership and ensure
commitment of resources and support.
oAssemble a Skilled Team
•Build a cross-functional team with diverse skills.
•Foster collaboration between IT and business
departments.
oSelect the Right BI Tools
•Choose tools that meet your organisation’s needs.
•Consider factors such as scalability and data
integration capabilities.
BI Implementation Tips

19. BI Implementation Tips
(cont.)
oStart Small, Scale Gradually
•Begin with a pilot project or limited scope.
•Expand implementation based on validated
benefits.
oUser Training and Adoption
•Provide training to users on BI tools.
•Create user-friendly dashboards and reports.
oContinuous Monitoring and Improvement
•Regularly monitor system performance and user
feedback.
•Adapt BI solution based on changing business
needs.

20. BI Implementation Tips
(cont.)
oEncourage Data-Driven Culture
•Promote data-driven decision-making.
•Recognize and reward data-driven achievements.
oCommunication and Collaboration
•Foster communication between IT and business
teams.
•Involve stakeholders in decision-making processes.
oMeasure ROI
•Establish KPIs to measure ROI of BI implementation.
•Assess effectiveness in delivering expected
benefits.

21. Thank You!