Unit 3.2

The Challenges of Managing Data

IT 233: Business Information Systems

Learning Objectives

By the end of this chapter, you will be able to...

✅ Identify the main difficulties organizations face when managing data.
✅ Describe the traditional file-based approach to data management.
✅ Explain the major problems caused by the file-based approach.

The Core Challenge

Organizations collect more data than ever before, but managing it is a massive challenge.

Fundamental Principle: The value of data depends entirely on its quality.

"Garbage In, Garbage Out" (GIGO)

Effective management is crucial for turning raw data into a valuable business asset.

Challenge 1: The Data Deluge 🌊

Organizations are overwhelmed by the sheer volume of data from diverse sources.

📊 Internal Transactions: Sales, inventory, HR records
🌐 Web & Social Media: Clickstreams, user comments, likes
📡 IoT Devices: Sensor data, smart appliances

This creates significant technical and financial challenges for storage and processing.

Challenge 2: Data Quality 🎯

Poor data quality leads to flawed analysis and bad business decisions.

Common Quality Issues:

Inaccurate: Incorrect customer names or numbers
Incomplete: Missing contact information
Inconsistent: "Kathmandu" vs. "KTM"
Out of Date: Old addresses or phone numbers

Challenge 3: Data Security 🔒

Data is a valuable asset that must be protected from threats.

Ensuring data security and privacy is a complex and continuous effort.

Protecting against unauthorized internal access.
Defending against external cyberattacks and data breaches.
Complying with data privacy regulations.

Challenge 4: Data Governance 🏛️

Without clear rules, data becomes chaotic and loses its value.

Data Governance: A collection of policies and procedures to manage the availability, usability, integrity, and security of data.

Lack of governance leads to:

Inconsistent data definitions
Unclear data ownership
Diminished trust in data across the organization

The Traditional Solution: File-Based Approach

Before modern databases, organizations managed data in separate, application-specific files.

Sales Dept.

customer_sales.dat

orders.dat

Accounting Dept.

customer_billing.dat

invoices.dat

Marketing Dept.

customer_prospects.dat

campaigns.dat

This created "Information Silos," where departments could not easily share data.

Problem #1: Data Redundancy 💾💾

Data Redundancy: The same piece of data is stored in multiple, separate files.

Example: A customer's address exists in the Sales file, the Accounting file, AND the Marketing file.

Consequences:

Wasted storage space.
Increased complexity in data management.
Leads directly to the next, more serious problem...

Problem #2: Data Inconsistency ⚡

A direct result of data redundancy. When data is duplicated, it often becomes inconsistent.

Scenario: Address Change

A customer calls Sales to update their address.
The Sales clerk updates customer_sales.dat.
The address in customer_billing.dat and customer_prospects.dat remains unchanged.

Result: The organization now has multiple, conflicting "versions of the truth."

Problem #3: Data Isolation 🧱

Data Isolation: Data is stored in different files with different formats, making it difficult to access and integrate.

Example: A manager needs a report on "total sales per customer and their outstanding balance."

Sales data is in the Sales system.
Billing data is in the Accounting system.
Creating this report requires a complex, often manual, process of extracting and combining data.

Real-World Impact: A Nepali Context

Scenario: A Bank in Nepal Using a File-Based System

Imagine you open a savings account at a branch in Pokhara. Later, you apply for a loan at a different branch in Kathmandu.

What problems might you face?

You have to provide all your KYC ("Know Your Customer") details again.
If you update your phone number for your loan, your savings account record might not be updated, causing you to miss important alerts.
The bank struggles to get a single view of you as a customer, making it hard to offer you relevant products.

Summary & Key Takeaways 🔍

Modern data management faces challenges of volume, quality, security, and governance.
The traditional file-based approach created isolated "information silos."
Data redundancy (duplicate data) wastes resources and directly causes data inconsistency (conflicting data).
Data isolation makes it nearly impossible to get a holistic, unified view of the business.
These problems highlight the need for a modern database approach.

Thank You!

Any Questions?

Next Topic: The Database Approach

Creating a "Single Source of Truth"

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