Most people are still lurking in the dark about certain facts about big data. Daily, our world is evolving full of data. As a beginner, or if you are interested in big data analytics, this is for you to get exposed to common misconceptions about big data in its entirety. Our big data guru would also agree with these misconceptions as many may have these notions and those may have been the stumbling blocks in advancing their interest in the field.
MYTH 1: EVERYONE IS DOING BIG DATA.
The fact here is that the number of people or institutions making use of big data is few. Even research often reports that the number of organizations or companies maximizing the technology of big data is few. This can serve as a new lead for beginners in the field of data analytics to gain confidence in developing careers in the field. Big data is large and a single organization cannot fully utilize it. It is ever-increasing and un-exhaustive
MYTH 2: BIG DATA IS ALL ABOUT THE SIZE OF DATA
Well, big data can also mean large data that are analyzed by computation to show trends, relations, and association. So, factually, big data involves velocity, variety and veracity. All these are what make up the big data. By looking at the components, none can stand alone without having to encompass others – velocity, variety, veracity and value. Put in another way, these are factors to consider when treating the concept of big data. So, when you think of big data, don’t just conceive the volume alone.
MYTH 3: PREDICTION OF OUR FUTURE
Necessarily, we cannot hope that data would explicitly tell us about the future. Whatever data we get today can only be used to forecast the future and that does not guarantee certainty. Big data is not certain, so it cannot give a certain guess of what comes in the future.
MYTH 4: BIG DATA IS FOR IT PERSONNEL ALONE
This is common to many data science aspirers. The fact is that, data is not analyzed by the IT personnel but in the hands of individuals who understand its uses. Don’t forget the quote that says “things are easy to deal with when you are good (have sound knowledge) at dealing them”. Big data works well in the hands data analysts. Although a data scientist can also serve as the IT manager as well as a company analyst, but distinctively, big data is for the data scientists.
MYTH 5: BIG DATA IS EXPENSIVE
It is often said that if knowledge is expensive, try ignorance. Top companies and businesses in the world are running and investing so much on big data analytics, and they are reaping more profit than expected. So, technology and big data resources cost is not so high to push away or discourage interested companies from investing in the technology. If you are novice in the field, this is an opportunity to give into the technology and acquire knowledge because there is a room for you in the analytics world.
MYTH 6: BIG DATA IS USUALLY CLEAN
Don’t forget that big data involves variety, velocity and veracious computational values. Hence, it mostly always rough (about 98% – data comes from many sources and forms). Although a clean data is good, but clean data is uncommon among most of the big data software – most software don’t need your data to be clean before task is done to get result.
MYTH 7: YOU MUST WAIT TO MAKE BIG DATA PERFECT
With the advent of software, you necessarily don’t need complex algorithms to clean your mined data. After all, waiting for data to be organized perfectly is time wasting and that will eventually make the data useless and archaic or old. While you think you need to wait, big data already has sophisticated programs specific to carry out certain tasks so that you can do your analysis in limited time.
This is an opportunity for to advance your skill if you have begun a data science career already. And if you haven’t, but are interested, this is a perfect time for you as well. Drop all myths and misconceptions and buckle up and reach your goal in becoming a data scientist. It’s definitely your time.
