“Understanding is the first step to acceptance, and only with acceptance their will be recovery”, this beautiful line which was a generous opinion of a famous writer changed my perspective towards understanding things. The above line expressed by non other than Ms. J.K Rowling clearly elucidated that if you want to learn something or even want to get through something such as a bad phase of life , your first and foremost task is to understand exactly the root cause of the problem or basics of that thing which you want to learn. Without understanding the true meaning of any element, aspect, situation etc. you can’t face it and cannot solve it either which may lead to dissatisfaction. But how it is related to big data analytics?

Semantics play a key role making us understand the meaning of a sentence. Proper implementation of Semantics means less use of technical words, proper reference of words depending upon the situation especially in cases when a word can be interpreted in multiple ways. Does big data have semantics? The answer is yes. Semantics in big data simply signify a proper way to understand the meaning and make meaningful use of that data. It sounds quite simple but actually is very complicated. The reason why it is becoming more and more complicated is because of generation of more and more unstructured data which is leading to germination of new problems in big data management.

Big data management is not merely a term but it is a full procedure or process which is quintessential to keep in mind while working in the field of big data. It comprises of familiar steps which sound very simple but play a key role in the process of big data management. They include data acquisition, extraction, cleaning, analyzing and interpretation. Semantics play key role in each of these steps.

During data acquisition , Semantics Ensure that only necessary data is acquired and unnecessary or private data is not used for further extraction. Since data acquired is always a combination of both structured and unstructured, semantics ensure that only readable and usable data is extracted. After this , comes the part of data cleaning in which data analysts spend around 80% of their time. Here, semantics play a key role in ensuring that while data cleaning process is being carried on especially with context to unstructured data, proper names and references are given to the metadata so that it becomes easy for analysis. After the cleaning part , the next step comprise of data analysis which is a quintessential step in gaining insightful details out of data which can be used to make strategies and long term decisions. Here semantics play an important role in ensuring that data is available for all the analysis tools in machine readable formats. For ex for solving of complex problems, various algorithms are used together such as different kinds of machine learning models are used together for predicting certain behavior and so on. Semantics ensure that all the data which has different formats for different kinds of analysis tools can be integrated if they are being used together just like in the case of machine learning. The final part is related to big data reporting and interpretation. Semantics is such a thing in reporting and interpretation which can either make or break the entire deal of work. Here Semantics ensure that simple but meaningful words are used to describe the data. Vague and difficult words must be ignored.

In the end I would like to conclude that even if an organisation use top notch software’s for analysis and have highly skilled employees, if they don’t keep in mind the proper implementation of semantics , then the whole data management process is itself a mess.