Q.Identify data required to be maintained to perform the following services:

Data Management Requirements

Think about your own life for a moment. You have a phone contact list, a collection of class notes, a set of saved passwords, maybe a folder of photos. Each of these is a small data system. Now imagine your college had to manage the records of ten thousand students — admissions, attendance, exam scores, fee payments, hostel allocations, library books borrowed. That is a different scale entirely. The moment you move from a handful of items to hundreds, thousands, or millions of records, you cannot just "keep it in your head" or scribble it on a scrap of paper. You need a system. And that system must meet certain data management requirements — the essential conditions that make the data useful, safe, and reliable.

The Core Idea

A data management requirement is simply a condition that the handling of data must satisfy for the system to work properly. It is not about what the data says (that is the content), but about how the data is stored, accessed, updated, protected, and eventually discarded. These requirements are decided before any software is built or any database is designed. They are the rules of the game.

If you run a small shop and write sales in a notebook, your requirements are minimal — you just need a pen and paper. But if you run a bank with millions of customers, your requirements become strict and numerous: you must know exactly who accessed an account, you must never lose a transaction, you must be able to retrieve any customer's statement from five years ago within seconds. Those are data management requirements.

Why They Matter

Without clear requirements, data becomes a mess. Duplicate records, lost information, wrong addresses, payments that vanish, privacy breaches — all of these trace back to poorly thought-out data management. In the business world, bad data costs money and trust. In government, it costs public confidence. In healthcare, it can cost lives. So organisations invest heavily in defining these requirements upfront.

The Main Categories of Requirements

Data management requirements fall into several broad buckets. Each addresses a different kind of risk or need.

1. Data Integrity

This is the requirement that data be accurate and consistent throughout its life. If a student's name is "Rahul Sharma" in the admission form, it should be "Rahul Sharma" everywhere — not "Rahul" in one place and "R. Sharma" in another. If a bank balance is ₹10,000 after a withdrawal, every part of the system should show ₹10,000, not ₹9,500 in one screen and ₹10,500 in another.

Integrity also means that relationships between data are preserved. If a customer places an order, that order must be linked to a real customer who actually exists. You cannot have an orphan order floating around with no owner.

2. Data Security and Privacy

Not everyone should see everything. A hospital's data management system must ensure that a nurse can see a patient's medical history but not their credit card number, while the billing department sees the credit card number but not the diagnosis. This is access control — who can read, write, or delete what.

Security also covers protection from external threats: hackers, viruses, physical theft of servers. And privacy is a legal requirement in many countries — you cannot collect or store personal data without the person's consent, and you must protect that data from misuse.

3. Data Availability and Reliability

Data must be there when you need it. If a customer walks into a bank branch at 3 PM and the system is down, that is a failure of availability. If an e-commerce site crashes during a sale, that is lost revenue.

Reliability means the data is not corrupted and the system does not give wrong answers. A reliable system returns the same correct result every time you ask the same question.

4. Data Consistency

This is closely related to integrity but focuses on rules and constraints. For example: a person's age cannot be negative. A flight cannot have more passengers than seats. A student cannot be enrolled in two classes at the same time if the timings overlap. These are business rules that the data must obey, and the system must enforce them.

5. Data Retention and Archival

How long must data be kept? Tax records might need to be stored for seven years. Medical records might need to be kept for the patient's lifetime plus a few years. Old exam answer scripts might be kept for one year after results are declared. After that, the data can be archived (moved to cheaper, slower storage) or deleted.

This requirement is often driven by law. Keeping data longer than necessary creates unnecessary risk and cost. Deleting it too early can lead to legal trouble.

6. Data Backup and Recovery

If a hard drive crashes or a fire destroys the server room, can you get the data back? Backup requirements specify how often copies are made, where they are stored (often in a different physical location), and how quickly the system can be restored. A bank might back up transactions every few minutes. A school might back up attendance records once a day.

7. Data Quality

This is about the fitness of data for its purpose. Quality includes:

• Accuracy: Is the data correct?
• Completeness: Are all required fields filled?
• Timeliness: Is the data up to date?
• Uniqueness: Are there duplicate records?

A database full of old addresses is not high quality, even if the addresses were correct when entered. Quality requirements define the standards the data must meet.

8. Performance Requirements

How fast must the system respond? A railway reservation system must process a booking in under a second during peak hours. A library catalogue search can take a few seconds. Performance requirements set the speed and capacity targets — how many users can access the system at once, how many transactions per second it must handle, how much data it can store.

How Requirements Are Decided

Data management requirements are not invented by the IT department alone. They come from:

• Business needs: What does the organisation need to do with the data?
• Legal and regulatory obligations: What does the law require?
• Industry standards: What do competitors or peers do?
• Risk assessment: What happens if data is lost, leaked, or corrupted?

A hospital and a retail store have very different requirements because their risks are different. Losing a patient's allergy information is far more serious than losing a customer's shopping history.

A Simple Example to Tie It Together

Imagine a college that wants to build a system to manage student exam results. The data management requirements might include:

• Integrity: Each student's marks must be entered exactly as on the answer sheet. No one can edit marks after the results are published without a formal correction process.
• Security: Only the exam controller can enter marks. Teachers can view only their own subject's marks. Students can view only their own results.
• Availability: The system must be accessible during result declaration week without crashing.
• Retention: Results must be kept for at least five years after the student graduates, then archived.
• Backup: A daily backup during the exam period, weekly backup otherwise.
• Quality: Every student must have a unique roll number. No marks field can be left blank — if a student was absent, it must say "Absent", not be empty.

These requirements shape everything: how the database is designed, who gets passwords, what software is chosen, how much the project costs.

The Big Picture

Data management requirements are the foundation of any system that handles data. They are not technical details for programmers to worry about later. They are business decisions that determine whether the system will serve its purpose or fail. A system built without clear requirements is like a building constructed without a blueprint — it might stand, but it will almost certainly have problems that are expensive to fix later.

For a commerce or humanities student, understanding these requirements matters because in any organisation you will eventually deal with data — customer lists, inventory records, employee files, financial transactions. Knowing what makes data well-managed helps you ask the right questions, spot problems, and communicate effectively with the technical teams who build and run these systems.