Big Data Analytics Tools is making the buzz nowadays. Every single enterprise, organization etc. seems to be gathering it, evaluating it, making money from it and tapping its powers. Whether we’re talking about interpreting millions of data collected for a product launch, or an uncountable number of hotel packages stats to find the best time to go on vacation, Big Data Analytics is on the cards. The enormous data coupled with highly skilled information technology, it is a promised solution for any problem by just crunching the numbers.
Is Big Data really that Big or is it the bubble waiting to burst. There is no doubt that Big Data Analytics Tools has already made a critical impact in certain areas but we need to be level-headed about what big data can do and can’t.
First, big data can work as an aide to scientific inquiry but rarely succeeds as a complete replacement. Molecular biologists, for example, would like to infer the maize smut fungus structure of DNA sequence, and scientists working on the problem use big data as one tool among many. But no scientist thinks you can give a solution by only crunching data. No matter how strong the statistical analysis is, you will have to start with an analysis that relies on an understanding of molecular biology.
Second, beware of Big Errors in Big data. As the data grows so does that number of variables to be analyzed resulting in more information meaning more noise or false information. Which also means big data will also result in making false statistical relationships. It’s not that big data will always yield false information, but main challenge lies in removing noise from big data.
Third, if the results of the Big Data Analytics Tools aren’t intentionally gamed, they often turn out to be less robust than they initially seem. We cannot solely depend on big data analysis always. It can be risky to draw conclusions from huge sets of data.
The Fourth drawback is called the echo-chamber effect. In this scenario, the source of information for a big data analysis is itself a product of big data, opportunities for error are aplenty here. Let us look at Healthcare programs. One medical company is exchanging data across all medical facilities and promoting the use of electronic health records. These records can be further programmed to deliver evidence-based care. This is a good strategy, except for the fact that with some of the uncommon diseases data, many of the findings could have been made. Any initial errors in initial trends infect the analysis, which is fed back into the next research, reinforcing the error.
In a nutshell, Big Data Analytics Tools is good when analyzing things that are very common but often falls short when analyzing things that are less common. Last but not the least is the hype created by big data. Champions of big data promote it as a revolutionary concept. Big data is certainly not revolutionary like electricity or any other important inventions which revolutionized the world. So let’s not create hype because it’s just like any other technology that we use.
Big Data Analytics Tools is here to stay and make an impact. It’s a great tool for analyzing data, but definitely not a silver spoon.
