Checklists and Glossary

The appendix to the 12-part series "Databases for Non-Developers". Everything covered in the main text, organized as checklists and a glossary you can pull out and use at work right away.

Appendix 1. Database Decision-Making Checklist

Purpose and value (Part 1)

You can explain in one sentence what work and user problem this solves
You have decided what data to store and what not to store
You know the lookups and changes you will do most often
You can describe the business damage if this data is lost or wrong

Accuracy and quality (Parts 2, 6)

Definitions of key terms and metrics are written down
Each piece of data has a designated system of record
You have rules that prevent or catch missing values, duplicates, and format errors
There is a business owner responsible for fixing quality problems
You have metrics that measure data quality (missing rate, duplicate rate, and so on)

Performance and recovery (Parts 2, 5)

You have estimated normal and peak usage
You have set an acceptable response time
You have set a recovery time objective (RTO) and recovery point objective (RPO)
You have tested actually restoring from a backup

Governance and personal data (Parts 7, 8)

Every important piece of data has a business owner
The purpose of collecting personal data and how long it is kept are defined
You know the flow of data sent to outside vendors
You have procedures for deletion requests and deletion when a contract ends
You have defined data classification levels (public / internal / confidential / highly sensitive)

Security (Parts 9, 10)

Administrator and user permissions are set to the minimum necessary
Multi-factor authentication is enabled for important accounts
Data at rest, data in transit, and backups are properly encrypted
You manage updates, audit logs, and alerts for unusual activity
You have a breach response contact list and procedures, and you have practiced them

Cost and sustainability (Parts 5, 11)

You have calculated costs beyond storage: requests, backups, replication, logs, and transfer
You have confirmed the technology is one your team can operate
You know how to export your data and what it costs if you switch providers
You have checked that the reason for adding a new database outweighs the added operational complexity

Using AI (Part 12)

You have decided which official sources AI answers may use and which they must not
User permissions are enforced from the AI retrieval stage onward
AI answers show the original source and its date alongside the answer
There is a procedure for routing high-risk questions — where a wrong answer is dangerous — to a human

Appendix 2. A Plain-Language Glossary

Term	Plain explanation
Data	Observed or recorded values and facts (e.g., 14258888282)
Information	Data with context and meaning attached (e.g., +1 425 888 8282, a US phone number)
Knowledge	Information that becomes the basis for judgment (e.g., the fact that this is the author's phone number)
Wisdom	Acting on knowledge (e.g., calling to solve the problem)
Database	A system for storing, finding, and connecting data in an organized way
DBMS	The software that runs a database (PostgreSQL, MySQL, and so on)
Schema	The structure that defines which items and relationships the data is stored in
Query	A command that asks a question of the data or requests an operation
SQL	The standard language for querying and managing relational databases
CRUD	Create, Read, Update, Delete — the four basic operations of a database
Transaction	A bundle of operations that must all succeed or all be cancelled together
Constraint	An input rule that prevents bad values from getting in
Index	A lookup structure that makes it fast to find the data you want
Replication	Copying data to another location so the service can keep running
Backup	A separate copy kept so you can restore a past state if something goes wrong
RTO	The target time for restoring service after a failure
RPO	The maximum amount of data loss you can tolerate, measured as a time window
GIGO	Garbage In, Garbage Out — bad data in produces bad results
Data model	The design that defines which items and relationships represent real-world things
System of record	The system agreed to be the "original" for each item of data
Metadata	Data that describes data — its meaning, source, owner, and so on
Data governance	The system for deciding decision rights, responsibilities, and principles around data
Data quality	How accurate, complete, consistent, and ready-to-use the data is
Data minimization	The principle of collecting and keeping only the personal data needed for the purpose
Pseudonymization	Replacing identifiers with other values while keeping a way to link them back with separate information
Anonymization	Processing data so a specific person can no longer be identified
Encryption	Scrambling content so it cannot be read without authorized means
Least privilege	The principle of allowing access only to what is needed for the job
Multi-factor authentication (MFA)	An authentication method that requires confirmation beyond a password
Audit log	A record of who accessed or changed what, and when
Defense in depth	A design where the next layer of defense still holds even if one fails
Data breach	An incident involving unauthorized access, disclosure, exfiltration, alteration, or destruction
Data warehouse	A store of data from many systems, organized for analysis
Data lake	A store that holds a wide variety of raw material — tables, documents, images, logs
Managed database	A service where a cloud provider handles part of the operations work
Serverless	An approach that reduces server management and automatically scales resources with usage
Vendor lock-in	A state of deep dependence on one vendor's technology that makes switching expensive
Embedding	A numeric representation of a sentence or image's features that makes them comparable
Vector search	A way of finding items with similar meaning by measuring the distance between their numeric feature representations
RAG	Retrieval-Augmented Generation — finding relevant material first, then having the AI answer based on it
Hallucination	The phenomenon of AI confidently generating content with no basis in fact

Key References

This series is general educational material. Specific legal requirements — personal data protection, breach notification, retention obligations — vary by country, state, industry, and contract, so always confirm with a qualified professional in the relevant field and with the latest official sources.

Introduction

Understanding AI

Data & Prompts

Building

Shipping

Future

Checklists and Glossary

Appendix 1. Database Decision-Making Checklist

Purpose and value (Part 1)

Accuracy and quality (Parts 2, 6)

Performance and recovery (Parts 2, 5)

Governance and personal data (Parts 7, 8)

Security (Parts 9, 10)

Cost and sustainability (Parts 5, 11)

Using AI (Part 12)

Appendix 2. A Plain-Language Glossary

Key References

About the Author

Jaehee Song