The phrase ‘big data’ reflects how mammoth it is. It’s a vast pool from where data scientists pick out solutions to sort out business related puzzles. Collecting, extracting, cleansing and analyzing such a huge data is absolutely next to impossible for an individual. In that mean time, around 30 billion Facebook content is exchanged every day. How interesting is this fact to strike the conclusion! But this is only rosy picture of big data’s story. It has some shortcomings also.
1. Analysis: It is open fact that computer is adopted to overcome the inadequacies of the traditional manual efforts. It eliminates the possibility of losing train of thoughts and helps in retaining innovative ideas that cross in your mind. ‘Google Analytics’ is its perfect example. It scans whatever is the progression or regression in user experience. Along, keeping an eye over the website activities taken place is an empowering tool to derive new strategies to outreach the traffic.
Pros: Big data is an exclusive repository of data from where millions of innovative and creative ideas take birth. Analyzing greases the wheel of any business.
Cons: The analysis extracted through big data can be deceitful. As I foretold about Google Analytics, black hat SEO strategies can mislead and hence, the analysis becomes manipulative.
2. Data Access through Surveys: Quantitative primary research ‘survey’ aims at accessing more and more information. Whether online or offline, the survey delivers authentic databases from the end-users.
Pros: Big data is an amazing tool to get exposure of such data that can disclose customer behavior and trend patterns. Knowing all those stuffs paves ways to explosive revenue generation. For instance, if the owner of TATA motors wants to know which fuel rates first in the list of its users, a survey can disclose it. Further, it will refine its fuel and try to go ahead of the customers’ first choice.
Cons: Getting key to access of such a huge volume of data is a walkover. But is data’s processing to analysis as easy as it sounds? For sure, designating analysts, data mining experts and other overhead expenses will prove as the path full of thorns. Difficulty is endless in this process.
3. Answering Data: Answers underlie big data. Take an example of the app ‘Truecaller’. This app is carved to answer the question of ‘who called you’ on your mobile phone.
Pros: Take an example of the app ‘Truecaller’. This app is carved to answer the question of ‘who called you’ on your mobile phone. This represents that the truecaller app delivers true identity of the caller after exploring the big data. Thus, it hides answers.
Cons: Comprehending answers after analysis of big data is a like shot in the dark. However, the truecaller app is created to tailor the answer. But what about those who are not registered to Google from where it gets big data for answering? No information about the caller is an apparent example of its loophole.
4. Speedy Updates: The data is changing every day. Pace is clearly visible in its updates. And why not? It’s the need of the hour.
Pros: Alterations in data is Ok. Authentic information makes you more eligible to gain accurate insight. Take a look over Makemytrip app. Globe hoppers need not check out details of the transport and accommodation expenses of a particular place. It speeds up delivery of information to them.
Cons: Looking at the other side, revised rates of transport and accommodation can be missed from getting updated on the app. Who knows the very next minute, the rates get revised. So, big data needs speed for tuning with updates.
5. Big Data is enormous: At the single click in the search bar of the Google after entering a keyword, huge volume of related information get open in search results. It’s enough to anticipate how big the data is.
Pros: Check out the example of Wikipedia. It is a faction of enormous data on internet. It’s just an example of it. But there are likewise repositories online and offline to illustrate how enormous it is.
Cons: Creating data is good but reaching to the most relevant information is an uphill battle. If I want to know about the inception of ‘Data Mining’ on Google, an array of results will pop up on the screen. Thereafter, I have to tussle with the extensive huge volumes of data to find what I want to know.