What Big Data Really Meansposted by John Spacey, June 27, 2016
The term Big Data seems to be on every technology pundit's lips these days. It has reached the level of hype that breeds misperception and myth.
Luckily, there are only 3 things you need to know to help you cut through the hype:
1. Big Data is About Technology LimitsAccording to Moore's Law processing speeds double every 18 months.
It's well known that corporate data doubles roughly every 12 months (the current annual growth rate is 97%).
These two growth rates have been running against each other for decades.
As data grows faster than processing capabilities, the IT industry needs to find new ways to process that data.
To put it another way, conventional tools and architectural approaches choke on big data volumes and velocity.
2. Big Data Isn't A ProductBig Data is a general term for architectural approaches and tools designed to handle large data volumes at high speed. It's as simple as that.
There's currently no silver bullet for big data and there's not likely to be. Big data is about processing and analyzing data. That's too large a problem to solve with a single product.
The current state of big data is focused on decision support (e.g. social analytics) and research (e.g. scientific research). However, big data also makes appearances in transaction processing and business processes.
3. Big Data is a Fundamental Problem That's Only Going to Increase in ImportanceBig data is driven by the growth of corporate (and organizational) data.
Dozens of business and technology trends are pushing data volumes and velocity. These include social software, gamification, crowdsourcing, data integration, machine learning, internet of things, analytics and visualization.
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