Asked on Quora:
When reading about Big Data, this starts with the definition of Gartner’s analyst Doug Laney (3Vs). IBM is often using 4 dimensions by adding veracity. Some people are using 6 or up to 12 dimensions. I am wondering what’s the most frequently used definition?
Answer:
Here’s my “working” definition of Big Data: if your existing 1) Tools & 2) Processes don’t support the data analysis needs then you have a Big Data problem.
You can add as many V’s as you want to but it all ties back to the notion that you need bigger and better tools and processes to support your data analysis needs as you grow.
Example:
#1. Social Media Data is BIG! It’s Text (variety) and much bigger in size (Volume) and it’s all coming in very fast! (velocity) AND business wants to analyze customer sentiments on social: OK — we have 3V’s problem and need a solution to support this. Maybe Hadoop is the answer. Maybe not. But you do have a “Big Data” problem.
#2: Your Customer Database is broken. They don’t right addresses. Google and Alphabet are showing up as two separate companies when they should be just one. Their employee count is outdated and All of these problems is confusing your business user and they don’t TRUST the data anymore. You have a veracity problem and so you have a BIG Data problem.
Everyone has a BIG DATA problem. It just depends what there “v’s” are AND it most cases “tools” alone will not solve the issue. You need PEOPLE and PROCESS to solve that. Here’s my ranking: 1) PEOPLE 2) PROCESS 3) PLATFORM (tools) for ingredients that are key to solving BIG Data problems.