There is a lot of hype around big data, and this, to a certain extent is harmful for businesses. Sounds shocking? Well, since there are so many articles, research reports and studies about big data, people are provided with more than enough information, and this, somewhere down the line, makes big data look too easy to understand. This is where all the problems begin since even before knowing the original features and characteristics of this new phenomenon, people start assuming a lot. As a result, a lot of misconceptions get generated.
No doubt, big data is a revolutionary concept and it’s also going to change the approach to marketing to a great extent. There are many businesses that have vast amount of data to store, from different sources. Transactional data, mobile data, financial data, behavioural data, customer research data and social media data are only to name a few of those. Simply put, this huge amount of data is referred to as big data. But then, sometimes the analysis of this type of data is too simplified. Consequently, a lot of myths get created. It’s essential to not only know about these misconceptions, but also clarify these. This is so because people often take wrong decisions based on these misconceptions. Here are some of the most common misconceptions about big data –
- Big data is good – There is certainly a distinction between plenty of data and plenty of good data. There might be huge amount of data that has lots of errors. At times, a chunk of data might get misplaced and this can further lead to wrong conceptions. This is why it’s wrong to think that big data is always good. It’s advisable to use some smart tool to figure out whether or not the data is actually good. Sometimes, it’s also essential to eliminate a certain portion of the data in order to analyse the nature of the data correctly.
- Big data offers concrete solutions – Well, this is one of the biggest misconceptions about big data. Quite contrary to this popular belief, ambiguity is one of the major characteristics of Big Data. Since most often the data comes from multiple sources, it becomes difficult to analyse the original significance of the evidences. If most of the data are interpreted incorrectly, this may lead to conflicting evidences. Moreover, if the volume of the data is bigger, the ambiguities and contradictions keep on increasing. Although you will get more witnesses with more data, it nowhere helps you find out the truth behind the data. This is why human judgment is required to reconcile all the conflicting evidences.
While these are the top two misconceptions about big data that most people believe in, there are many other as well. It’s a common belief that big data helps create self-learning algorithms. Quite contrary to this belief, it requires human intervention to create powerful algorithms. It’s essential to find out the truth behind all these misconceptions as it helps one make informed decisions, to know about big data and create most effective strategies.
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