Getting meaningful information out of big Data
Big data isn’t only defined by the volume; instead, there are many other variables involved such as velocity, variety of data, and its consistency. Simply put, big data is the point where all this exceeds any organization’s computing storage and capacity for timely and accurate decisions.
So how do organizations make sure that not only meaningful information is achieved from this bulk of data but also how it is stored to be utilized in the future?
Getting meaningful data
All the trillions of bits and bytes accumulated and stored every second would make no sense if one doesn’t know how to sort it all out and make sense of it to glean insights. This problem at hand was researched by the researchers of the University of Southern Denmark. The conclusions and tools derived to sort data and retrieve only meaningful and useful information out of this unsorted data jungle was presented and secured in the Journal Nature Methods. They tried to explain their research in the following manner:
Hypothetically, pretend that you are conducting a research on obesity and your trillions of data on the server related to obesity stored. The information may include what overweight people mostly eat, their sleeping patterns, and the number of times they eat in a day and how many times of course.
Your first hypothesis may conclude that the person’s lifestyle directly influences their weight, and to back your hypothesis with proof, you may enter the computer to find out the link between the two and compare the weight changes and let’s say cheese sandwiches consumed by the person. You will definitely find a link, maybe even more than one. This way, you will continue collecting data from multiple links and saving it for your research for later use.
But think about an approach that may not be as fast as the one discussed earlier but discover slinks that are more genuine and relatable? These links you hadn’t even considered might land you on perfect comparisons you were looking for between lifestyles and its influence on weight. All your earlier suspicions will be put down to rest once you find out completely contrasting proofs.
This example roughly explains the importance of meaningful data.
Look for hidden patterns
This is exactly what clustering of big data tries to achieve. To sort out those hidden patterns we hadn’t considered before and let the computer provide grouped information that shares common traits directly linking to the information we are trying to gather. These clustering techniques as a prototype were used by the same university of Southern Denmark researcher’s team led by the Assistant professor Richard Rottgar to discover dogmatic networks in pathogenic species to develop a fundamental understanding of these organisms without having to opt for dangerous and expensive web-lat studies.
But this clustering of data isn’t as simple as it seems; it can even baffle computer scientists with its inherent complexity.
Big data clustering techniques
High-performance computing to analyze subsets or samples of available data to yield accurate results.
In-database analytics directs pertinent data administration, analytics and reporting tasks to where the data resides.
In-memory analytics solves complex problems and offers solutions quicker than conventional disk-based processing.
The Hadoop framework processes and stores big data on grids.
As for today, there are a number of comparable tools and techniques for clustering but require a deep understanding of the fundamental algorithms. Although there is still no measurement tool to measure what’s out there and what is to be used, but a promising future surely opens doors to prosperous possibilities.