What is Big Data?
You must have heard the term “Big Data” a lot. It is indeed gaining a lot of importance these days. Some are of the opinion that it is the modern age oil! So then, what is Big Data all about?
Big data refers to the large amount of data generated by web-logs, text, videos, content & images — mainly created by online activity that demands modern and sophisticated systems for storage.
Here are 4 Big Data Essentials that you probably didn’t know!
Big Data Essentials #1- Characteristics
When we talk about Big Data we don’t necessarily mean the size of the data. Dough Laney defines Big Data on the basis of 3Vs, viz., Volume, Variety, and Velocity.
The volume of data is increasing daily. As per IBM, 2.5 Exabyte of data is generated every day. By 2020, the total data will add up to 40,000 Exabyte! A single storage server cannot store such vast amounts of data. Hence, a need for network of storage devices called SANs (i.e. Storage Area Networks) arises. Companies find it harder to afford the cost of these storage servers.
Velocity is the speed at which data is generated and the promptness at which it needs to be processed. Some researchers believe that 90% of the world’s data was generated in the last two years alone. Big Data poses a huge challenge for social networking sites. For example, Facebook needs to store petabytes of data generated by its 1.65 billion active monthly users. Such streaming data needs to be stored and queries need to be processed in real-time.
Traditional data types only include structured data, which perfectly fits in the case of an RDBMS (i.e. Relational Database Management). But most of the data we generate is unstructured. The digital world has opened up its doors to unstructured data making RDBMS no longer viable. In fact, Facebook alone generates 30+ petabytes of unstructured data in the form of web logs, pictures & messages. Almost 80% of the data today is unstructured and cannot be classified into tables. With the aid of Big Data technologies, it is now possible to consolidate this data and make sense of it.
However, Big Data has been further classified to include two more Vs i.e. Veracity and Value.
Veracity means the biases, noises and abnormality in data. In other words, how dependable, reliable or certain the data is. Uncertainty may exist due to incompleteness and inconsistency, ambiguity, latency, deception, model approximation, etc. With the amount of data surging, the accuracy of the data surely takes a toll. This then leads to the big question, “Can I trust this data and the insights it provides? A lot of “dirty data” may exist in the system. Therefore, it is recommended to use clean data while formulating the Big Data Strategy.
Tip- Assign a Data Veracity score by ranking specific data sets. Do this to avoid making decisions based on analysis of uncertain and imprecise data.
From the business perspective, this V is the most vital element in Big Data. The main aim of delving into an ocean of data is to bring some value out of it.
This requires financial and hardware investments in infrastructure & resources to handle and analyze Big Data. As a result, it’s crucial to do a cost-benefit analysis before investing in Big Data projects.
Big Data Essentials #2- Sources
Big Data Essentials #3- Careers
Big Data Essentials #4- Importance
So, here’s why Big Data is a significant addition in every sector-
Some of the companies using Big Data
Now you finally know about the 4 Big Data Essentials!
Want to know how Adobe, Twitter, Google, Netflix, Facebook, etc. make smarter decisions every day based on Big Data? Then read our article on Retail, Sports & Media. Big Data is everywhere! and learn about the different use cases of Big Data in modern industries.
Surely, the amount of data generated and stored every day across the global level is incredible. More so, this phenomenon is only expected to continue. At the same time, organizations having futuristic thinking are quickly evolving to include Big Data. They are thus hiring skilled professionals to interpret data and aggressively build their organization’s Big Data capabilities.