Big Data – Decoding the big picture

Dream Big; but make sure you have it under control to stay on top of the game. To stay on top of the game it is necessary to give attention to small information that your company’s Big Data is giving. That’s exactly why multinational corporations are hiring Big Data analysts. Little about Big Data and Big Data scientists are discussed in this write up.

To put it short, Big Data is a catchphrase that is used to describe large volumes of structured or unstructured data. In most cases the data structures are unstructured or incompatible to the needs of a business user.  A ‘Big Data Scientist’ is someone who knows the tools and tricks to statistically evaluate this Big Data to churn out meaningful information. Such meaningful information can be used by the business in devising further strategies to expand market share, penetrate new markets or to sustain the existing ones.

The agreed notion in the business world is that at least 91% of the data held today has been created in the past 3 years. The volume of data stored and accessed is only going to increase with arithmetical progression which makes Big Data management a necessity for large sized businesses; that is if they want to stay at the top of their game.

The examples listed below hint how much data is being transferred to and fro between users on a single day:

• 500 million is the number of tweets that are estimated to be exchanged every single day. That is approximately 6,000 tweets every single second. (Internetlivestats)

• 172,800,000 is the number of debit/credit card transactions that VISA alone handles on a single day around the world.

• 936 million is the count of users who log into Facebook daily. Of that 83% constitutes outside USA users. (Socialbakers)

• $107 Billion was theturnover achieved by Amazon during 2015 from 193 million unique visitors. (Statistia)

• More than 50% of Google searches originate from mobile devices. Google still commands a 64% market share of US desktop search market.

The above mentioned Big Data examples can be further explained using 3 V’s:

1. Variety refers the diverse forms in which data is being created or exchanged, like spreadsheets, images, videos, audio messages, documents, text messages, etc.

1. Velocity refers to the speed at which such data is being exchanged. In certain industries, this could also indicate the speed at which data keeps changing or replaced with new versions. (Eg: Number of tweets).

1. Volume indicates the size and quantity of information that keeps exchanging. In Big Data it is often describedin terms of giga bytes, peta bytes and progressing space volumes.

A Big Data scientist can be concluded to be a professional who can identify what volume and in what variety should be analyzed with reference to its velocity for business decisions. Needless to say, he or she is someone who can carve the future growth of the company with their statistical study of the corporation’s Big Data. That said, there are some essential skills that an individual must have in order to become a Big Data scientist.

Essential skills required to become a Big Data scientist

Broadly, there are three major skills that a Big Data scientist must have essentially for a successful career. They are described in detail as below:

1.Technical skills
Expertise in Big Data technology like Hadoop, Grid Computing, etc.
Expertise in programming and scripting using common system languages like Java, Phython, C++, etc.
Knowledge of Database management, data modeling, SQL, etc.

2.Visualization skills
A Big Data scientist should be capable of presenting and visualizing his findings in a manner that is understandable to users who are not experts in Big Data terminology. For this, he or she must have hands-on experience in using tools like Tableau, MS PowerPoint, MS Excel, Google Visualization API, infographics, etc.

3.Business Skills
A Big Data analyst must have a brief understanding of how the particular domain industry functions, what are its peculiarities and what makes it unique from other related industries. It is also necessary to have an understanding of what kind of information will help the business understand its customer in a better manner.

Career prospects of a Big Data scientist

 A Big Data professional can fit into any of the following roles:

•          Big Data Scientist,

•          Big Data Visualizer,

•          Big Data Analyst,

•          Big Data Architect,

•          Big Data Manager,

•          Big Data Consultant,

•          Big Data Researcher, etc.

Final Words

These nomenclatures are but different titles for the same roles and responsibilities. A Big Data scientist can aspire to find lucrative career options in Big Data Analysis in multinational corporations with business spanning the entire globe.