Data Modelling & Optimisation
Ever wondered how apps like Zomato know your favourite cuisine, or how Netflix recommends shows you actually like? Behind the scenes, it’s all about how data is modelled, stored, and optimised. Let’s break it down.
What Is Data Modelling?
Imagine you're designing a city. You need to decide:
What buildings exist (entities),
What each building contains (attributes),
How roads connect them (relationships).
Data modelling is like city planning for information systems. It’s the art of visually organising data so developers, analysts, and business teams speak the same language.
Why It Matters?
Ensures clarity in how data flows.
Reduces errors in software and database design.
Improves performance and communication across teams.
Types of Data Models
| Model Type | What It Does | Real-World Analogy |
| Conceptual | Big-picture view of entities and rules | Whiteboard sketch of your app idea |
| Logical | Adds detail, no tech constraints | Blueprint with room sizes, no furniture |
| Physical | Final schema for database implementation | Actual house plan with plumbing and wiring |
The Data Modelling Process
Let’s say you’re building a student portal:
Identify entities → Student, Course, Instructor
Define attributes → Student has Name, Roll No., Email
Map relationships → Student enrols in Course
Assign keys → Roll No. as primary key
Normalise → Avoid storing the same email twice
Iterate & validate → Review with devs and stakeholders
Types of Data Modelling Techniques
| Technique | Description | Example |
| Hierarchical | One-to-many tree structure | Company → Departments → Employees |
| Relational | Tables joined via keys | Orders table linked to Customers |
| ER Models | Diagrams showing entity relationships | Visualising a school database |
| Object-Oriented | Complex relationships with inheritance | Social media app with Posts, Comments, Likes |
| Dimensional | Optimised for analytics | Sales dashboard with Time, Product, and Region dimensions |
Optimisation Tips
🔹 On the Data Side
Pull only what you need → Don’t fetch entire tables for one report.
Use correct data types → Avoid storing numbers as strings.
Clean the trash → Remove nulls, duplicates, and outdated entries.
Cache smartly → Speed up repeated queries.
🔹 On Refresh Schedules
Time it right → Refresh during low-traffic hours.
Let users trigger refreshes → Avoid unnecessary auto-updates.
Watch heavy datasets → Split or archive when needed.
Modelling Uncertainty and Risk
What Is Uncertainty and Risk?
Imagine you're planning a picnic. You check the weather forecast, but it says there's a 40% chance of rain. You’re unsure whether to go ahead or postpone. That feeling of not knowing what will happen? That’s uncertainty. If you decide to go and it rains, ruining your food and mood, that’s risk.
In technical terms:
Uncertainty is about unknowns; things we can't predict with full confidence.
Risk is about the chance of something going wrong and the impact it might have.
Understanding and modelling these concepts helps us make smarter choices; whether you're a doctor, investor, engineer, or simply someone planning a weekend getaway.
Types of Uncertainty
There are two flavours of uncertainty:
| Type | Description | Real-Life Example |
| Aleatory | Randomness that can't be eliminated | Tossing a coin or rolling dice |
| Epistemic | Lack of knowledge that can be reduced | Not knowing how a new medicine will affect you until more trials are done |
Risk vs. Uncertainty: What’s the Difference?
Think of driving in fog:
Uncertainty is not knowing if there’s a car ahead.
Risk is the possibility of crashing into it.
Risk is quantified uncertainty; we try to measure how bad things could get and how likely they are.
Techniques to Model Uncertainty and Risk
Let’s explore some powerful tools that help us deal with unpredictability:
1. Probability Theory
This is the math behind chance. It helps us assign numbers to outcomes.
Example: If there's a 30% chance of rain tomorrow, you might carry an umbrella just in case.
2. Bayesian Models
These models learn and adapt. They update predictions as new information comes in.
Example: A doctor starts with a general idea of what illness you might have, but adjusts the diagnosis as test results come in.
Bayes’ Theorem: P(A|B) = (P(B|A)*P(A))/P(B)
This formula helps update beliefs based on evidence.
3. Monte Carlo Simulation
This technique runs thousands of random scenarios to see what might happen.
Example: A financial planner simulates different market conditions to estimate how your investments might perform over 10 years.
4. Decision Trees
These are like flowcharts that map out decisions and their possible outcomes.
Example: Should you take a job offer in another city? A decision tree can help you weigh salary, relocation costs, and career growth.
5. Scenario Analysis
This method explores best-case, worst-case, and most likely outcomes.
Example: A company planning a product launch might prepare for booming sales, average interest, or total flop and plan accordingly.
Where Is This Used?
Modelling uncertainty and risk isn’t just for scientists; it’s everywhere:
Finance: Predicting stock returns, managing investment risks.
Supply Chain: Handling demand spikes, avoiding overstock or shortages.
Healthcare: Forecasting patient flow, planning emergency resources.
Environment: Predicting floods, earthquakes, and climate shifts.
Whether you're choosing a career path, investing money, or managing a hospital, uncertainty is part of life. But with the right tools like probability theory, Bayesian models, Monte Carlo simulations, and decision trees, we can turn unpredictability into informed action.