Data

Data (raw facts), information (data with context), knowledge (a pattern you can act on), wisdom (knowing whether you should).

Raw facts, figures or symbols with no context to give them meaning.

Data that has been given context, so that it means something.

Wisdom. They can find patterns, but deciding whether acting on a pattern is right is a choice about values.

Quantitative is numbers you can count or measure. Qualitative is words, images and sounds — descriptions.

Data about other data — the time, place, device or author attached to a file rather than its content.

It shows who, where, when and how often, without anyone reading a word — which is enough to map someone's life.

Cultural, financial, geographical, medical, meteorological, transport, scientific and statistical.

Finding trends, patterns, connections and links; and collecting measurable facts about people and communities.

A link means two things move together. A cause means changing one changes the other — which needs a controlled test, not a chart.

The pattern is real, so the system looks right, while the reason behind it may not exist at all.

Amazon’s scrapped CV tool learned from a decade of mostly male applications and downgraded CVs containing “women’s” (reported 2018).

Create/collect/extract, store, process, analyse, access, preserve, reuse.

Reuse — data collected for one purpose used for another the person never agreed to.

Cleaning can drop the awkward records, so the result looks tidier and more certain than the truth.

Because reuse is the stage that changes what the data does to a person, so each purpose needs its own lawful basis (in force 2018).

Primary: collected first-hand for your own question. Secondary: collected by someone else for theirs.

Organises and structures data so it is accessible, manageable and capable of being updated.

Who is NOT in it? Data from app users leaves out everyone without the app.

The categories decide what can be counted. An answer the form does not offer can never appear in the results.

Charts, tables, reports, infographics and visualizations.

Does the axis start at zero? What time range? Are the areas honest? Count or rate? What is missing?

A truncated axis, a chosen start date or an area scaled in two dimensions changes what the reader sees while every figure stays true.

Global Fishing Watch (2016) collected nothing new — publishing existing signals as a map moved power from fleets to the public.

Encryption (lock it with a key), masking (replace it with realistic fakes), erasure (destroy it so it cannot be recovered).

Whether a record has been changed since it was written. NOT whether it was true when written, and not privacy.

Deleting usually removes only the pointer; the contents stay until overwritten. Erasure overwrites the space.

Ordinary controls not kept up — a reused password (Colonial Pipeline, 2021), an unapplied patch (WannaCry, 2017).

Volume, variety, velocity, veracity.

Veracity — whether the data is right. Volume does not fix errors, it hides them.

Predicting behaviour, modelling what would happen under other conditions, and understanding past, present and future behaviour.

A group never collected from is still absent, so the blind spot stays while the model looks more reliable.

Bias, reliability and integrity; control, ownership and access; privacy, anonymity, surveillance and personally identifiable information.

A few details together — postcode, date of birth, sex — identify most individuals, and joining datasets makes the link easy.

The harm and the benefit come from the same cause, so removing one removes the other.

Quality: COMPAS. Ownership: the right to be forgotten (CJEU 2014). Surveillance: San Francisco's facial recognition ban (2019).