If you used Excel or Access in the early 2000s, you already know the core of data analysis. AI builds on the same ideas; it does not replace them.
What you already know
- A spreadsheet holds rows (records) and columns (fields). Sorting, filtering and a pivot table answer "how much, by what, over when".
- VLOOKUP, and later INDEX/MATCH, joined two lists on a shared key, such as a supplier number. An Access query did the same with joins between tables.
- Charts turned columns of numbers into trends you could see.
- The rule never changed: rubbish in, rubbish out. The answer is only as good as the data and the question.
What changed
- Volume: instead of a few thousand rows, there are millions of records from customs filings, shipping notices, government gazettes, prices and news.
- Variety: much of today's useful information is text (laws, press releases, news, filings), not neat tables. AI can read and sort text the way a pivot table sorts numbers.
- Speed: sources change daily. Automated monitoring reads them on a schedule and records what changed, instead of someone re-downloading files.
- Patterns: statistical and machine-learning models find relationships across many variables at once and show the uncertainty around a forecast.
What did not change
- You still need clean, well-labelled data with a known source and date.
- You still need a clear question.
- A person still has to judge whether the answer makes sense and decide what to do with it.
How AetherVett uses this
AetherVett collects public information from official sources first, records where and when each item was read, joins it the way a database join would (company to owners, product to tariff line, route to ports), and uses AI to read text and highlight what matters. A person reviews every report before it is released.
Not legal, financial or other regulated advice.