Unit 10 Session 2: Data, AI, Analytics, and Business Intelligence | IT 231 BBA Slides

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Unit 10 · Session 2

Data, AI, Analytics, and Business Intelligence

IT 231: IT and Applications (BBA)

Today's focus

  1. Data Warehousing & Data Mining
  2. Emerging Trends in IT
  3. Data Analytics

🎯 Learning Objectives

By the end of this session, you will be able to:

  • ✅ Explain data warehousing and data mining, and how they work together.
  • ✅ Identify the major emerging trends in IT: AI, Big Data, Cloud Computing, IoT, and social media.
  • ✅ Explain the impact of these trends on business and society, including in Nepal.
  • ✅ Recognize how these technologies connect and reinforce one another.
  • ✅ Define data analytics and distinguish its four types.

Data Warehousing & Data Mining

Data Warehousing 🗄️

A large, centralized repository of historical data collected from multiple sources, designed for query and analysis.

  • Integrated: Data from different systems is cleaned and unified.
  • Time-Variant: Retains historical data to analyze trends.
  • Non-Volatile: Once entered, data is not changed.

🗄️ Four Characteristics of a Data Warehouse

Bill Inmon, often called the "father of data warehousing", defined four characteristics. Example: a supermarket chain.

  • Subject-oriented: organised around subjects like sales or customers, not around daily operations.
  • Integrated: combines data from tills, the website and suppliers into one consistent format (for example, every date stored the same way).
  • Time-variant: keeps history (sales for every month of the last 10 years) so trends can be compared.
  • Non-volatile: data is loaded and read, not edited or deleted day to day.

Memory trick: S-I-T-N: Subject, Integrated, Time, Non-volatile.

A Star Schema

A star schema diagram with a central fact table linked to surrounding dimension tables.

Data Mining ⛏️

The process of analyzing massive volumes of data to discover hidden patterns, correlations, and trends.

  • Uses AI, machine learning, and advanced algorithms.
  • Classification: Categorizing data.
  • Clustering: Grouping similar data without labels.
  • Association: Finding rules (e.g., "Customers who buy X also buy Y").

How They Work Together 🤝

The Data Warehouse provides the structured, clean data, and Data Mining provides the algorithms to extract actionable insights from it.

  • Mining requires a warehouse to avoid messy, fragmented data.
  • A warehouse is just storage without mining to unlock its value.

📊 How a Business Intelligence (BI) System Works

1️⃣ Data sources: sales system, CRM, website, spreadsheets

⬇️ 2️⃣ ETL: Extract, Transform (clean and combine), Load

⬇️ 3️⃣ Data warehouse: one trusted store of history

⬇️ 4️⃣ Analysis: queries, OLAP, data mining

⬇️ 5️⃣ Presentation: dashboards and reports (Power BI, Tableau)

⬇️ 6️⃣ Decision: managers act, for example "restock this item in Butwal"

Exam tip: draw these six boxes with arrows as your "suitable diagram".

Emerging Trends in IT

What Are "Emerging Trends"? ⚡

Emerging trends are technologies and concepts that are gaining momentum and are poised to have a significant impact in the near future.

Key Idea: They are not just theoretical; they are actively shaping industries, creating new business models, and changing how we live and work.

In this unit, we'll explore the "what," "why," and "how" of these powerful forces.

The Landscape of Modern IT

This unit covers several interconnected domains that are driving innovation:

🧠 Intelligence & Data

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Data Science
  • Big Data

🌐 Infrastructure & Connectivity

  • Cloud Computing
  • Internet of Things (IoT)
  • Social Media
  • Digital Marketing

Trend 1: Artificial Intelligence (AI)

Definition: The simulation of human intelligence in machines, enabling them to learn, reason, problem-solve, and understand language.

AI is the broad concept of creating "smart" machines.

It encompasses sub-fields like:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision

🎯 Goal: To automate tasks that typically require human cognitive abilities.

🤖 Narrow AI vs. General AI

🎯 Narrow (weak) AI: exists today

Very good at one task, and useless outside it.

Examples: face unlock, Google Translate, Netflix recommendations, chess engines, ChatGPT.

🧠 General (strong) AI: not yet built

Would understand and learn any task a human can, and transfer skills between them.

Still a research goal and a topic of debate.

Every AI product you use today is narrow AI, even the impressive ones.

Diving Deeper: ML & Data Science

Machine Learning (ML)

A subset of AI where systems learn and improve from data without being explicitly programmed.

Example: A spam filter that learns to identify junk mail based on the emails you mark as spam.

Data Science

An interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data.

Example: Analyzing customer purchase history to recommend new products.

⚙️ Rules vs. Learning: A Spam Filter

📜 Traditional programming

A programmer writes the rules:
IF subject contains "FREE MONEY" THEN spam

Rules + data → answers

Spammers change one word ("FR3E M0NEY") and the rule fails.

📈 Machine learning

Show the computer thousands of emails labelled "spam" or "not spam". It finds the patterns itself.

Data + answers → rules

Retrain on new examples and it adapts.

🎬 How Machines Learn from Data

Trend 2: Big Data 📊

Definition: Extremely large and complex datasets that cannot be easily managed or processed with traditional data-processing application software.

Often characterized by the "3 Vs":

Volume

Massive quantities of data.

Velocity

Data is generated at high speed.

Variety

Data comes in many forms (structured, unstructured).

Trend 2: Big Data 📊 (continued)

The challenge isn't just storing the data, but analyzing it to find valuable patterns.

Trend 3: Cloud Computing ☁️

Definition: The delivery of on-demand computing services—including servers, storage, databases, networking, and software—over the Internet ("the cloud").

Instead of owning your own computing infrastructure, you can rent access from a cloud provider like Amazon Web Services (AWS) or Microsoft Azure.

  • Pay-as-you-go: Only pay for what you use.
  • Scalability: Easily increase or decrease resources as needed.
  • Accessibility: Access services from anywhere with an internet connection.

Trend 4: Internet of Things (IoT)

Definition: A network of physical objects ("things") embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.

🔍 Think of it as: Giving everyday objects a digital voice.

  • Smartwatches
  • Connected home appliances
  • Smart traffic signals
  • Agricultural sensors

Trend 5: Social Media & Digital Marketing

The evolution of the web into a platform for communication and commerce.

Social Media

Platforms that allow users to create and share content or to participate in social networking.

(Facebook, Instagram, TikTok, etc.)

Digital Marketing

Using online channels to promote or market products and services to consumers.

(SEO, Content Marketing, Social Media Ads)

These have fundamentally changed how businesses connect with their customers, moving from broadcasting to engagement.

How It All Connects: A Synergy

These trends don't exist in isolation. They feed into and empower one another.

  • The Internet of Things (IoT) generates a massive Volume of data.
  • This creates a Big Data challenge.
  • This data is stored and processed using scalable Cloud Computing resources.
  • AI and Machine Learning algorithms analyze this data to find insights.
  • These insights can be used to power targeted Digital Marketing campaigns on Social Media.

Practical Applications in Nepal 🇳🇵

These global trends are having a local impact:

Fintech & AI

Companies like Fonepay and eSewa use AI/ML for fraud detection and analyzing transaction patterns to improve services.

E-commerce & Cloud

Platforms like Daraz rely on cloud infrastructure to handle massive traffic during sales events, scaling resources on demand.

Smart Agriculture & IoT

Pilot projects are using IoT sensors to monitor soil moisture and weather conditions, helping farmers optimize irrigation and improve crop yields.

Digital Marketing Boom

Countless Nepali businesses, from small cafes to large corporations, use social media for customer engagement and targeted advertising.

AI Impact on Business

  • Productivity: automate classification, chat, drafting, forecasting.
  • New products: recommendations, fraud scoring, personalization.
  • Risks: wrong decisions at scale, privacy, vendor lock-in, invented facts in generative tools.

AI is a tool inside an information system. Data quality and accountability still sit with managers.

AI Impact on Society

  • Jobs: some routine roles shrink; oversight and analysis grow. Effects are uneven.
  • Language and access: tools help, but models trained mainly on English can fail on Nepali context.
  • Fairness: biased data can repeat discrimination in hiring or credit.
  • Public life: deepfakes and misinformation need digital literacy (Unit 9).

🌍 Real Case: Amazon's Hiring AI (2014–2017)

  • Amazon built an AI to score job applicants' CVs from 1 to 5 stars.
  • It learned from 10 years of past CVs, most of which came from men.
  • The AI started marking down CVs containing the word "women's" (as in "women's chess club captain").
  • Amazon stopped using it. Reuters reported the story in 2018.

AI learns from past data, so it can copy past bias. Ask: who is missing from our data?

Humans and Data at Work

A specialist sits in a dark room looking at medical scans on several large monitors.

Key Takeaways

A quick summary of our introduction:

  • 🎯 Emerging trends are powerful, interconnected technologies reshaping our world.
  • 🎯 Data is the new fuel: AI, ML, and Big Data are all focused on extracting value from it.
  • 🎯 The Cloud provides the flexible, scalable infrastructure needed to power these trends.
  • 🎯 IoT and Social Media are expanding the digital frontier, connecting everything from devices to people.
  • 🎯 These technologies are already creating tangible value and opportunities in Nepal.

Data Analytics

What is Data Analytics? 📊

The systematic computational analysis of data to discover useful information, inform conclusions, and support decision-making.

🔬 Data Science
The discipline

⛏️ Data Mining
The excavation technique

📈 Data Analytics = Examining & interpreting the results

The 4 Types of Analytics

1. Descriptive 📋

"What happened?"
Sales reports, dashboards.

2. Diagnostic 🔍

"Why did it happen?"
Root cause analysis.

3. Predictive 🔮

"What will happen?"
Forecasting with ML models.

4. Prescriptive 🎯

"What should we do?"
Recommending the best action.

🧪 Activity: Which Type of Analytics?

1. "Sales fell 15% last month." → Descriptive (what happened)

2. "Sales fell because a competitor opened next door." → Diagnostic (why it happened)

3. "Demand for umbrellas will double in monsoon." → Predictive (what will happen)

4. "Order 500 umbrellas by June 1 and cut the price 10%." → Prescriptive (what we should do)

🎬 Four Analytics Types

Real-World Applications

  • Marketing: Optimizing ad spend by analyzing campaign performance.
  • Finance: Fraud detection using pattern recognition.
  • Supply Chain: Demand forecasting to prevent stock-outs.
  • Nepal Context: Banks like NIC Asia use analytics to assess loan risk.

Exam Practice 📝

Short answer

  1. Define artificial intelligence. Differentiate between narrow AI and general AI.
  2. How does machine learning differ from traditional rule-based software programming?
  3. Why are traditional SQL databases inadequate for managing modern IoT sensor data?
  4. Define a data warehouse. Explain its four characteristics.

Exam Practice 📝 (continued)

Long answer

  1. Discuss the impact of AI on business and society, with examples.
  2. Explain the components and process of a BI system with a suitable diagram.

Revise the answers in the full notes linked on the last slide.

Image & Video Credits

Images

  • CERN datacenter: Hugovanmeijeren, CC BY-SA 3.0, via Wikimedia Commons
  • Star schema: SqlPac, CC BY-SA 3.0, via Wikimedia Commons
  • Oracle Analytics Cloud dashboard presented on stage: GeneralAB13, CC BY-SA 4.0, via Wikimedia Commons
  • Waymo self-driving car, front view: Grendelkhan, CC BY-SA 4.0, via Wikimedia Commons
  • Data Visualization of Street Trees: Intel Free Press, CC BY-SA 2.0, via Wikimedia Commons
  • X-ray Vision (DVIDS 78078): Senior Airman Julianne Showalter, U.S. Air Force, public domain, via Wikimedia Commons

Videos (YouTube)

  • Simply Explained, "What Is Machine Learning? (Explained Simply)" (48–127 s)
  • Maven Analytics, "Descriptive vs Diagnostic vs Predictive vs Prescriptive Analytics" (62–151 s)

Thank You

Questions before we wrap this hour?

Next: Unit 10 · Session 3 — Cybersecurity, Cloud Computing, and IoT

Course Home · Next session

📖 Read the full notes: Artificial Intelligence and Its Impact on Business and Society · Data Science, Machine Learning, and Neural Networks · Big Data and Blockchain Technology · Data Warehousing and Data Mining · An Introduction to Business Intelligence (BI) · Data Analytics

📖 Related chapter notes

Read first, then revise with this deck: