Big Data Versus Small Data Analytics

In a nutshell, text analytics makes use of the traditional way of data mining and identifies keywords, phrases, or words that can be linked to each other. It is a tool useful in looking for sentiments, relevance, or emotions. It sifts through emails, blogs and more taking unstructured data and linking it with structured ones.


In small data analytics, the focus or range of the searches are naturally small. Most of the data in small data analytics is structured. It allows the researcher to find data in an information pool. For many companies, this is the first step to take. It’s like walking – to do it, you have to learn to take one step at a time. Learning the ropes in small data analytics can be a great preparation before moving on to big data analytics.

Big data analytics is no longer a pool of information – it’s an ocean. In small data analytics, searching for patterns is easier since the pool can be easily explored with values that target information which a researcher already knows is there. In big data, you have to deal with huge databases of information in many forms and more often than not, a researcher has no idea what’s in it or what’s not. In this way, the chances of finding a new trend or pattern is exponentially higher since big data analytics was built in order to do just that. Make connections or links using all the coded algorithms and see what comes out.

In comparison managing small data is easier than big data. Information in small data can be relatively limited while in big data it is virtually unlimited. Both deliver information in short amounts of time making research faster but of course both have its differences.

For companies, taking advantage of small data opens doors to what you already have and finds ways you can potentially profit from it. Big data opens doors to a whole new arena of what is out there. On one hand, big data allows you to be ahead of the game in what could be the next big thing in medicine, fashion, education, engineering, etc. On the other hand, it can help in searching for fraud, security discrepancies and more.

The bottom line is that both techniques are equally useful. Going from small data to big data is like going from the basic level to an advanced level in gathering information that can influence your company’s future business decisions.


Why Big Data is Important

Information has always been a lifeline. The info that you have could open or close doors. It could build or destroy. In qualitative research, everything that one knows could be tomorrow’s next indispensable product, new trend, or service.

Why Big Data is Important

Nowadays, the speed of how people connect with each other influences how quickly trends or fads change. How on earth would companies ever keep up? For some companies who conduct their market research on their own, keeping a database seems to be the primary solution to the need of compiling and studying data. Most companies use on-hand database management tools and require the interpretation of researchers to sum up and come up with answers to the most recent queries.

This is where Big Data comes in handy. Text analytics is a method that identifies similar keywords or phrases, puts them together for certain queries made for different purposes. Text analytics downloads all the ‘pages’ it needs to search through. It’s not just any single search but gigabytes to terabytes of information.

Sentiment Analysis is a part of the big data text analytics that digs deeper into the data gathered by reaching out to what makes potential markets tick. This aims to not only determine the subjective aspect of an author but also of its readers.

Big Data Gives:

  1. Accuracy of Data – loads and loads of information that are related are found in shorter time than any traditional method of gathering then searching through data. In the global industries today, everyone is racing to be able to supply for new demands first, or find needs that need better solutions, too. Nobody wants old news.
  2. More – Big data gives more information. Much more. Data gathered can be focused in one locale or spread out overseas due to the mobility of everyone’s information. It makes ‘knowing’ much easier to achieve.

It might sound simple and it is, but the impact this has in the corporate world is phenomenal. Companies with this platform can have the upper hand against its competitors with proper use. Combining the information gathered by this tool, and the ingenuity of putting it to good use definitely adds to the appeal of big data analytics. Text analytics has already changed the league in catching fraudulent activities, sentiments of movie reviews, medical, and media applications, and big data only opens the door wider for text analytics making the business world more competitive than it already is.