Data Analytics is today emerging as a popular trend today and is being adopted by a number of companies, to streamline businesses. There is also a notion that the future of businesses depends more on ‘big data’ and companies that adopt big data as a platform are seen challenging their competitors with a better performance.
There are more questions on the term ‘big data’ than answers mainly because it has become the buzz word of the year. There is a serious upsurge in volumes of data in enterprises today, and it is time to examine that rather than generalizing it as big data. The word ‘big data’ is reaching a stage where it the term is becoming completely meaningless.
The big errors
Big data poses a number of ways to find fake statistical relationships, and this is primarily due to large data sets that surfaces in the industry. It is also to be noted that complete reliance on ‘big data’ for enterprise processes is creating overconfidence among enterprises. The concurrent developments in the field of data analytics include predictive analytics, smart data, data science and new SQL.
- Predictive analytics – the field of predictive analysis is currently behind everything and it includes statistical techniques, and other fields.
- Smart data – ‘Smart Data’ is a term that is all set to replace ‘big data’ in a short time, and this is a sure indication that the word itself will become overused in less time. It is more about monetizing data through predictive analytics.
- New SQL – it is used to describe highly-scalable and distributed SQL systems.
- Data Science – the field of data science employs advanced statistical techniques, language processing, and machine learning and computer science to enable extracting more data.
Beware of the big errors
The advent of ‘big data’ has brought ‘cherry-picking’ to an industrial level. Big data by all means more information, but it may also mean more false information. Researchers may have a convenient option of picking the data that confirms their beliefs and leave the rest.
Distinction between libraries and real life is another problem with big data. The excess data available compared to real life signals may force someone looking at history from the vantage point to find more bogus relationships that is really in the making. There are also higher chances of experiments being marred and biased. Researchers may hide their failed attempts and go on compiling a thesis with false results. Hence it is necessary to organize big data to avoid big errors.