Most companies have trouble extracting the data they have already been collecting (for decades), let alone coming up with ways to even begin tracking more granular data.
Most examples you see of big data, are in fact, not big data at all. It’s just data. Calling ‘big data’ does make it sound a bit more impressive I guess.
What’s really going on here is that some companies are starting to use data to gain insight into their business (‘Hooray!’ cries every Lean Six Sigma practitioner ever).
Here are some examples of ‘big data’ that aren’t big at all, just good use of data:
- Morton’s The Steakhouse – a story that went viral. A customer tweeted the company (joking) that they’d love dinner to be sent to the airport when they land. The company picked up the tweet, found his flight, and sent food to the airport accompanied by a tuxedo-clad waiter. Great publicity stunt, but exactly how many Exabyte’s of data were mined to achieve this?
- Express Scripts Holding Co processes pharmaceutical claims. They realized that the people who most need to take their medicines often forgot, so they created a new product, Beeping Medicine caps and automated phone calls.
Great idea, but did big data need to tell us that the reason some people needed to make a health claim was because they forgot to take their meds?
These are really just examples of using data to provide insight – not big data. Whilst there is no official definition of ‘big’ in terms of data, think of it this way:
‘Big Data’ is so much data that it is beyond the abilities of most commonly used software to capture, manage process and analysis the data.
If it doesn’t meet that definition, then you’re just looking at data. And you know what…?
That’s perfectly ok.