Size is Not All That Matters: the Need for Speed in Big Data
Big data is often spoken of in terms of the three V’s: Volume, Velocity and Variety. In other words, big data is not just about size, it’s also about speed. There is no clear cut off as to where data becomes big data. The fact that there are multiple parameters means that it is not possible to list a specific number of gigabytes or terabytes of data and say that anything below that is just data and anything above that is big data. Instead, how quickly the data needs to be analyzed and the speed at which outputs are required is a significant factor.
The Future of Data
In the future of big data, it is very likely that there will be two types of players: the quick and the dead. Hadoop is one of the most important terms to know in the sphere of big data. Big data can be very cumbersome to handle. The point of Hadoop is to make that easier. There is currently a rich and rapidly evolving ecosystem of Hadoop reporting tools that are making it both faster and easier to access these large data files and make use of the data in real time.
Some of these tools get around the problem of size by taking the tools to the data instead of bringing large data down from the server to your desktop. You can think of it like sending a small drone to survey the data landscape on the server and pick out what is most important rather than trying to manage a landslide of information that needs to be suddenly and rapidly moved elsewhere all at once.
Rapidly Cutting it Down to Size
Many of these tools also use the idea that a picture is worth a thousand words to good advantage. Graphs, charts and other data visualization techniques are an important part of slicing through big data like a hot knife through butter and cutting it down to easily digestible, usable bites. Without that, big data is at risk of making a mountain out of a molehill. Larger piles of data with which we can do nothing of real value is not an asset. It is a potential liability.
It takes tremendous capacity to sort and use high volumes of data streaming in at high speed in real time. Rapidly processing such data is critical to making it something valuable to your business. Otherwise, it is in danger of being mostly noise and not much signal. Turning big data into something with a high signal to noise ratio is the whole point of applying these tools.
Business Use Case: Insurance
Insurance is an industry that has historically been overwhelmed by the amount of data with which it must cope. A typical insurance company needs to deal with federal regulations that apply to financial institutions, such as the Gramm-Leach-Bliley Act, and also federal regulations that apply to the health sector, such as HIPAA. On top of that, this industry must comply with regulations at the state level that differ in all fifty states. The same policy that says the exact same thing can de facto result in different coverage from one state to another, depending upon what the state regulations say about specific things. This can be an information overload nightmare for the industry.
Counter-intuitively, the advent of big data isn’t adding to the problems of the industry. Instead, it is helping this industry cut things down to size. Among other things, big data is helping auto insurance quote comparison companies respond promptly to many claimants when widespread events, such as a hurricane, causes a sudden influx of claims. In the past, this was an overwhelming situation where the only solution available to the industry was to ask people to work overtime in order to try to catch up. This can put a company in a position of feast or famine — either they are swamped with claims and paying their employees too much to handle them, or there is not enough work to do and they are paying employees to sit around twiddling their thumbs. The use of rapid response big data solutions can make the entire year more profitable and smoother.
About the author:
Jeremy is a tech and business writer from Simi Valley, CA. He lives for success stories, and hopes to be one someday.
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