Term

Big Data

Big Data is the term Doug Laney coined in 2001 when he framed three Vs (volume, velocity, variety) as the axes along which data was outgrowing the tools of the day. Later writers added veracity and value. Through the 2010s the phrase became the rallying flag for an infrastructure wave: Hadoop, Spark, Kafka, NoSQL, columnar warehouses. By the 2020s the marketing language largely got replaced by "data platform", "data lake", "lakehouse", and now "AI infrastructure", but the underlying problem space did not go away, it just stopped needing a banner.
Reviewed by 7wData

Why it matters

Even when it is out of fashion as a buzzword, the 3-Vs frame is still the cleanest way to scope a data problem. I find that asking “is this a volume problem, a velocity problem, or a variety problem” picks the right tooling roughly eight times out of ten before any vendor conversation starts. For AI specifically, the volume and variety axes are where model training lives, at scales that make the 2012 Hadoop era look quaint. The vocabulary aged, the physics did not.

Where you’ll encounter it

Three contexts. A senior data leader who says “back in the Big Data days” is signalling they were in the trenches between roughly 2010 and 2018, a useful credential filter. A vendor positioning against the “legacy Big Data stack” is almost always trying to sell you a lakehouse or a managed cloud service, read the pitch with that in mind. And a job description still asking for “Big Data experience” is using the phrase as a proxy for distributed-systems chops, not a literal technology requirement.

Part of the 7wData AI Glossary. Tracking how concepts like this move in the expert conversation: daily signals at ins7ghts.com.