Assignment #02
Course title:
Total Quality Engineering
Course code:
SE- 317
Submitted to:
Mam Faiza Nawaz
Submitted by:
Moeez Ahmad
21011598-155
Semester:
Semester (8th Spring 2025) Section C
Department of Software Engineering
Information Quality Issues & It’s Significance
1. Sufficiency:
Sufficiency isn’t just about quantity—it’s about context. In AI systems, overloading a model
with too much irrelevant data can degrade performance as much as missing information.
Example:
In a smart city traffic management system, collecting vehicle count alone isn't sufficient. Data
on weather, public events, and road repairs must also be included to make intelligent
predictions.
Modern Relevance:
As systems grow complex, sufficiency means holistic data collection, not just more data.
Systems need "just enough" context-rich information to stay efficient and intelligent.
2. Accuracy:
Accuracy is not static—data that was once accurate can decay. In a world of real-time
streaming data, outdated "accurate" data is no longer valid.
Example:
Self-driving cars rely on sensor fusion. An accurate but slightly delayed GPS signal can be
more dangerous than an approximate but real-time LIDAR scan.
Modern Relevance:
Accuracy today means not only correctness but also precision in time and space—especially
in systems with real-time decision-making like robotics, drones, and autonomous services.
3. Timeliness
Timeliness isn't just about speed; it's about relevance at the moment of action. Data
delivered too early or too late can be equally useless.
Example:
In algorithmic trading, a millisecond delay in stock price feeds can trigger flawed trades. But
receiving market analysis 2 hours before the market opens may not be helpful either—timing
must align with context.
Modern Relevance:
In edge computing and IoT, timeliness defines whether decisions are made at the edge, on
the cloud, or not at all. It dictates architecture choices.
4. Security:
Security has evolved from walls and passwords to zero-trust models and behavioral
verification. Information security is now tied directly to user patterns and intent.
Example:
Behavioral biometrics—like how you type or swipe—are now used to secure banking apps.
It's not just about what you know (password), but how you behave.
Modern Relevance:
Security is about continuous validation, not one-time authentication. In distributed systems
and APIs, every microservice must verify the authenticity of every other.
5. Cybercrime:
Cybercrime isn't just technical anymore—it’s psychological warfare. Social engineering is
more dangerous than brute-force hacks.
Example:
Deepfake phishing scams trick employees into thinking their CEO is calling them. This blend
of AI and social manipulation is becoming a mainstream threat.
Modern Relevance:
Traditional firewalls can't block intent manipulation. Information systems must now include
human behavior modeling as part of cybersecurity frameworks.
6. Privacy:
Privacy is shifting from being a compliance checkbox to a product differentiator.
Companies like Apple use privacy as a brand asset.
Example:
The “App Tracking Transparency” feature on iPhones lets users opt out of being tracked
across apps, forcing companies like Facebook to change their ad models.
Modern Relevance:
Privacy now requires user empowerment—not just hiding data, but letting users control
and audit it. Future systems must include privacy dashboards as a core feature.
7. Creativity
Creativity in information systems is not just about content generation—it’s about designing
new ways of interpreting and connecting data.
Example:
Spotify’s “Wrapped” feature creatively visualizes your music habits, transforming raw data
into emotionally resonant storytelling.
Modern Relevance:
AI-driven tools (like GPT-4) enable creative outputs, but creativity also lies in how data is
visualized, personalized, and embedded in user experiences.
8. Control and Prevention:
Control is no longer about top-down enforcement—it's about autonomous governance.
Prevention happens when systems self-diagnose and auto-correct.
Example:
Netflix uses chaos engineering to intentionally break its own systems and learn how to
prevent real failures. This proactive model shifts control from human oversight to system
resilience.
Modern Relevance:
Modern systems require adaptive control, where AI monitors data drift, performance, and
risk—ensuring prevention is a living process, not a static rulebook.
Conclusion:
The modern landscape demands fluid, context-aware, and adaptive information
management. These quality issues are interconnected—timeliness affects accuracy,
security influences privacy, and control enables sufficiency. Recognizing their dynamic
nature is the key to building resilient, intelligent, and user-focused systems.
References:
www.gartner.com
https://www.researchgate.net
