Why (and When) to Buy External Data

Buying external data is no longer marginal: the European data market exceeds €115 billion and grows by 11.6% per year (EU Commission), and the global data broking market is estimated at around $434 billion in 2025, heading towards $617 billion in 2030. For a company, external data has become a strategic input.

The use cases are numerous: training or refining AI (corpus, labeled data, RAG), enriching CRM and prospecting, performing market intelligence (market size, competitive monitoring), feeding risk scoring, or personalizing products and targeting. The same data often serves multiple purposes, which explains its value. In the era of generative AI, proprietary or exclusive data constitutes a defensible competitive advantage — a 'moat' — to the point that major players are already buying it outright (Reddit signed an agreement with Google for approximately $60 million per year).

Should you buy or produce? The rule of thumb: buy when the data already exists elsewhere, is fresher, broader, or more complete than yours, and would cost more to recreate internally; otherwise, produce it. On the side of what to buy, data falls into seven monetizable families (transactional, behavioral, operational, sensor/IoT, geolocation, aggregated HR, content) — detailed in the guide 'The 7 Data Assets'.

The essential remains: buying with confidence. Poorly sourced data (unclear rights, unmanaged GDPR, questionable quality) is a risk, not an asset — hence the importance of buyer due diligence, the subject of a dedicated guide. The first concrete step: explore the datasets available on d-nvest.