The term Big Data has been around a while and is still evolving rapidly, taking multiple digital transformations into its folds. Artificial intelligence, data science, and Internet of Things are some of them.
The concept of Big Data takes birth in the fact that the amount of information that has been produced since the beginning of the Digital Age is enormous. Not to say we didn’t have any data before digitalization, but the concept of Big Data focuses purely on data produced digitally on an extremely large scale. This is analyzed using computers to reveal patterns that further help us in understanding human interactions and behaviors.
With the constant changes and revolutions happening in the digital world, some truths that were once sworn by, can lose their legitimacy very quickly and be replaced with a different more updated truth. The Big Data world is no different.
Here are some of the myths:
1) The data has to be “Big” to fall under the Big Data category
Large volumes are the key to Big Data has been overstated as a fact. The term large or big is misleading. The fact is that Big data is actually diverse data. Things that are also important are Variety, Velocity, Veracity, and Value of the said data. High-speed processing and most updated data during analysis, instead of huge quantities, helps to get the best results.
2) All companies now work on the lines of Big Data
When something new comes in the market, there is an anxiety to not be on the sidelines and miss out the main stuff. This myth is purely on those lines. Big data is a much talked about concept, but it is still in evolution and much like all sustainable things in the digital world. Companies that spend time on envisioning a data-centric model will surely benefit. Although, currently an analysis shows very few companies actually following their models on a full scale. The challenges faced currently include how to derive value from the many terabytes of data analyzed and the technology needed to process them.
3) Big Data is only for “big players”
Smaller organizations create smaller amounts of data; this does not, however, imply that Big Data cannot be utilized by them. The concepts can still be used in analyzing smaller packets of data to develop insights into the business no matter how big or small.
4) Clean data only
Arijit Sengupta, CEO of BeyondCore, calls this the biggest of all myths. It’s not possible to always have clean data. What can be done is a “good enough” analysis, where the data however unclean is analyzed. This helps in understanding the quality of the data. A data analysis application illuminates the strong points and weaknesses and then a cleanup strategy can be drawn.
5) It’s expensive
Considering the huge costs in technology and manpower, certain companies are afraid to wet their feet in the pool of Big Data. The truth is that there a number of free tools available online that allow anyone to do a Big Data analysis. The cost of cloud computing has also considerably come down.