Data repurposing

When talking about repurposing, I mean data being reused for something beyond the reason it was collected.
It’s often lawful, sometimes beneficial, and usually invisible.

To get an idea on the scale of data repurposing I’ve looked up some ballpark figures on how much our data is worth:

Meta (Facebook / Instagram / WhatsApp) made around $164 billion in 2024, largely from advertising which depends on user data.
Google (Alphabet) made over $250 billion in advertising related revenue in 2024.
Others like credit agencies, supermarkets and data brokers all turn insights from user data into commercial value.

1 | Meta (Facebook, Instagram, WhatsApp)

Meta’s platforms seem like they’re about connecting people but every like, pause and share also trains algorithms that decide what you’ll see next and which ads appear.

Meta have started using public posts to train AI models. It’s a prime example of data evolving beyond its original context.

2 | Google (Search, Maps, YouTube, Gmail, Android,Chrome)

Google’s tools help you search, navigate and remain productive – the activity behind them fuels personalised ads and product improvements.

Because one Google account links many services, the repurposing happens mostly internally but this also means the dataset is vast.

3 | Credit Reference Agencies (Experian, Equifax, TransUnion)

These organisations hold financial data on almost every UK adult.

They use it for credit scoring but also to offer marketing and analytics services, turning financial insights into broader consumer profiles.

4 | Supermarkets (Tesco Clubcard, Sainsbury’s Nectar, etc.)

I’m not a fan of loyalty card but I’m told they are supposed to make shopping cheaper and more convenient.

They also log every purchase against your profile.

That purchase history is used for stock planning, offer design and, increasingly, personalised pricing.

5 | Data Brokers & Advertising Networks

Companies like LiveRamp and Oracle Data Cloud specialise in combining datasets from multiple sources like loyalty programmes to create marketable audiences.

Most of us have never heard of them, but they’re a core part of the online advertising ecosystem.

How

1. Inference data

Even when a company doesn’t store your exact words or photos, it may infer interests, habits or attitudes from your behaviour — and those inferences drive ads, offers and recommendations.

2. AI training

Public posts, product reviews and images can be reused to train machine‑learnings models, developing incredibly powerful models with your data.

Large ecosystems like Google, Meta, Amazon share insights across their own products to improve features and advertising efficiency. The more you use, the more the network learns.

Thoughts

When I first started writing about data privacy, I thought it was all about keeping data safe as that has been my professional focus for so many years but I think it’s increasingly critical we all have an understanding of where our data goes and what the impact is of trading our data for services.

Repurposing isn’t automatically bad as it keeps many services free and it’s how the digital economy has evolved.
But it raises important questions about transparency, control, and purpose.
Understanding it helps us make informed choices about what we share and with whom.

The UK’s data protection laws give everyone rights to access and challenge how their data is used. Most large organisations now offer dashboards where you can review, export or delete your information.