“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…” (Dan Ariely, Duke University)
Big Data is like that bloke you met at a local bar, after having your usual two drinks and then decided to have one more. You leaned in, listening more intently than usual. “Digital footprint.” “Information Age.” You nodded and smiled, even though you didn’t understand. “Change the world.” “The future.” You were impressed—and even if you weren’t, you faked it well.
Then came the “morning after”. You only have fuzzy recollection of the conversation. Big Data is now just another cloud in your morning coffee.
The newest term that is used and abused is Big Data. So what exactly is Big Data?
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
Why Big Data?
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
- Determining root causes of failures, issues and defects in near-real time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behavior before it affects your organization.
How does it work?
This category includes data that reaches your IT systems from a web of connected devices. You can analyze this data as it arrives and make decisions on what data to keep, what not to keep and what requires further analysis.
The final step in making big data work for your business is to research the technologies that help you make the most of big data and big data analytics. Consider:
- Cheap, abundant storage.
- Faster processors.
- Affordable open source, distributed big data platforms, such as Hadoop.
- Parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity and high throughputs.
- Cloud computing and other flexible resource allocation arrangements.
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