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The 5 V’s of Big Data: Veracity

I’m up to the fourth “V” in the five “V’s” of big data. Previously, I’ve covered volume, variety and velocity. That brings me to veracity, or the validity of the data that financial institutions use to make business decisions.

While volume, variety and velocity are considered the “Big Three” of the five V’s, it’s veracity that keeps people up at night.

I’ve also discussed how new technologies like cloud computing and open source software can process great volumes and varieties of data at great speeds. Except you still have concerns. You’re getting all this great data, but how do you know if it’s accurate?

Four Ways to Ensure the Accuracy of Big Data
One in three business leaders don’t trust the data they’re given. And for good reason: flawed data costs the US economy approximately $3.1 billion annually. Keeping data clean, unbiased and free of noise and abnormalities can be a full-time undertaking.

Fortunately, there are a handful of ways to ensure your data is valid:

1. Create separate processes for gathering data and analyzing it
2. Find a technology with built-in operational assistance: algorithms for data validity, checks and balances, and the ability to quickly scour multiple sources for correct information
3. Appoint someone to familiarize themselves with the data so they can spot anomalies and outliers (technology is incredibly useful, but it isn’t flawless)
4. In extreme cases, it may be worth investing in a secondary data processing source so you can check by hand if something seems “off”

With the scrutiny that financial institutions are under (from account holders and regulators), accuracy is a paramount concern. Their data needs to be verified for validity and context. There’s no point paying for big data architecture if problems with veracity lead to poor business decisions or compliance failures.

> Learn more in the Research & Analytics section of our website

I’ll be back next week with the final post in our series about the five “V’s” of big data. I’ll take a stab at the $64,000 question: What value does big data provide? Plus, how can financial institutions ensure they’re receiving that value?