Unit 3.1
Unit 3 Intro: Data and Knowledge Management | IT 233 Notes
Learning Objectives
By the end of this introductory chapter, you will be able to:
- ✅ Differentiate between data, information, and knowledge.
- ✅ Identify the key challenges of managing organizational data.
- ✅ Define the core concepts of Big Data, data warehouses, and data marts.
- ✅ Explain the purpose and importance of knowledge management in a modern organization.
The Information Economy's Most Valuable Asset
In today's business landscape, data is often called "the new oil."
However, raw data by itself is not useful. It must be refined to create value.
This unit explores the journey from raw facts to strategic advantage.
Data ➡️ Information ➡️ Knowledge ➡️ Business Value ⚡
The Data Hierarchy
Understanding the progression is crucial.
1. Data 📊
Raw, unorganized facts and figures. Lacks context.
Example: 120, 150, 95
2. Information ℹ️
Data processed to be useful. It has context and meaning.
Example: Sales for Product A in the last 3 days were 120, 150, and 95 units.
3. Knowledge 🧠
Application of information and experience to make decisions.
Example: Seeing the declining sales trend, we know we should launch a marketing promotion for Product A.
⚡ DIKW Sorter: Nepal Edition
Select a card, then click its correct category bucket. (Figures are approx./illustrative.)
🚀 Real Case: When Data Lost Its Context
- 1999: NASA's Mars Climate Orbiter was lost as it arrived at Mars.
- The data: a contractor's ground software (Lockheed Martin) reported thruster impulse in pound-force seconds (imperial units).
- The assumption: NASA's navigation software read the same numbers as newton-seconds (metric units).
- The result: the spacecraft flew far too close to Mars and was destroyed.
The numbers were correct; the context (the unit) was missing. Data only becomes information when everyone agrees on what it means.
🧁 Worked Example: Climbing the DIKW Ladder
Illustrative: a hypothetical bakery chain in Pokhara. The numbers are made up for teaching.
- Data: 42, 38, 75, 80
- Information: cakes sold at the Lakeside branch on Thursday, Friday, Saturday and Sunday last week.
- Knowledge: weekend sales are roughly double weekday sales, because tourists and families shop on weekends.
- Wisdom / action: bake more on Friday night, add a weekend staff shift, and cut weekday waste.
Exam tip: for Q1, give one example and carry it through every level, as here.
🎬 Watch: Climbing the DIKW Pyramid
The Data Deluge: Key Challenges
Why is managing data so difficult?
- Volume Explosion: Data is generated at an unprecedented rate from many sources (web, mobile, IoT).
- Data Silos: Information is often trapped in isolated systems across different departments (e.g., Marketing, Sales, HR).
- Quality & Consistency: Ensuring data is accurate, complete, and up-to-date is a constant battle.
- Security & Privacy: Protecting sensitive data from breaches and complying with regulations is critical.
⚡ Data Challenge Identifier
Company figures in these scenarios are approximate.
Solution: The Database Approach
Moving from chaos to a structured, centralized system.
Problem: Traditional Files
- Data Redundancy
- Data Inconsistency
- Difficult to Access
- Poor Security
Solution: Database
- Centralized Management
- Minimal Redundancy
- Enforced Standards & Integrity
- Improved Security & Sharing
A Database Management System (DBMS) is the software used to create, manage, and query a database.
🧩 Quick Activity: Match the Problem to the Fix
For each file-system problem, which database feature solves it?
- A supplier's phone number is typed into four different spreadsheets. → Centralized storage: store it once, minimal redundancy
- One spreadsheet says a product costs Rs. 500, another says Rs. 550. → Integrity rules and one source of truth
- An intern can open the whole payroll file. → DBMS access controls (user permissions)
- The manager waits two days for IT to combine two files into one report. → Shared data and a query language (e.g., SQL)
Beyond the Spreadsheet: The Rise of Big Data
Big Data refers to datasets so large and complex that traditional data-processing application software is inadequate to deal with them.
Characterized by the "3 V's":
Volume
Massive scale of data (terabytes to zettabytes).
Velocity
High-speed at which data is generated and processed.
Variety
Different forms of data (structured, unstructured, semi-structured).
⚡ Big Data 3V's: Classify the Example
Select a card, then click the correct V to classify it. (Figures are approx./illustrative.)
🎬 Watch: Volume, Variety, Velocity
Organizing for Insight: Warehouses & Marts 🔍
Specialized databases designed for analysis, not for day-to-day operations.
Data Warehouse
A large, centralized repository of integrated data from various sources.
- Scope: Enterprise-wide
- Subject: Multiple subjects
- Purpose: Strategic, long-term decisions
Data Mart
A subset of a data warehouse, focused on a specific business line or department.
- Scope: Departmental (e.g., Sales, Marketing)
- Subject: Single subject
- Purpose: Tactical, specific analysis
⚡ Warehouse or Mart? You Decide.
The Final Step: Knowledge Management (KM)
Knowledge Management is the process through which organizations capture, share, and leverage the collective expertise of their employees to foster innovation and improve decision-making.
Why is it important?
- It converts individual, tacit knowledge (experience) into explicit knowledge (documented procedures, best practices).
- Prevents "reinventing the wheel" and retains critical knowledge when employees leave.
- Creates a "smarter" organization over time.
⚡ Tacit vs. Explicit Knowledge: Sort It Out
Select a knowledge card, then click the correct knowledge type.
🧠 How Organizations Manage Each Type of Knowledge
📄 Explicit: Codify it
- Write it down: manuals, SOPs, FAQs, templates
- Store it centrally: document systems, intranets, wikis
- Make it searchable and keep it up to date
🧠 Tacit: Connect people
- Mentoring, job shadowing and apprenticeships
- Communities of practice and lessons-learned sessions
- Expert directories ("who knows what")
These are often called the codification and personalization strategies. Most firms need both.
🎬 Watch: Gut Feel vs Guide Book
Practical Application in Nepal 🇳🇵
Real-World Scenarios
- Big Data: Ride-sharing apps like Pathao and inDrive analyze vast amounts of trip data (Volume), in real-time (Velocity), from GPS, user ratings, and payment info (Variety) to optimize pricing, match drivers, and predict demand.
- Database Approach: Nepali commercial banks like Nabil Bank or Global IME Bank use robust databases to manage millions of customer accounts and daily transactions, ensuring data integrity and security for services like mobile banking.
- Knowledge Management: IT service companies in Kathmandu use internal wikis to document project solutions and coding standards, allowing new hires to quickly learn from the collective experience of senior developers.
💬 Discussion: Is Data Really "the New Oil"?
- Oil gets used up when you burn it. What happens to data when you use it?
- Crude oil needs refining. What is the "refinery" for data in a business?
- Name one way the comparison is misleading, for example on privacy or ownership.
Unit 3.1: Key Takeaways 🎯
- Raw data is useless without context; it must be processed into information and applied as knowledge to drive business value.
- A structured database approach is essential to overcome challenges like data redundancy, inconsistency, and security risks.
- Big Data, defined by its Volume, Velocity, and Variety, requires new tools and strategies for analysis.
- Data warehouses provide an enterprise-wide view for strategic decisions, while data marts serve the specific needs of a single department.
- Knowledge Management captures human expertise, turning it into a lasting, reusable organizational asset.
Review Questions 📝
- Distinguish between data, information, and knowledge with examples.
- What is a data warehouse?
- List the Vs of Big Data.
- Explain the difficulties of managing data and how the database approach addresses them.
- What is knowledge management? Explain the difference between explicit and tacit knowledge and how organizations can manage each.
Revise the answers in the full note.
Image & Video Credits
Images
- Mars Climate Orbiter, artist's depiction: NASA/JPL, public domain, via Wikimedia Commons
- Virginia Tech data center: Christopher Bowns, CC BY-SA 2.0, via Wikimedia Commons
Videos (YouTube)
- Asset Insights, "From Data to Wisdom: Climbing the DIKW Pyramid" (26–85 s)
- Eye on Tech (TechTarget), "6 V's of Big Data" (34–97 s)
- Study_Note, "Tacit and explicit knowledge" (58–130 s)
Thank You!
Any questions?