A Beginner’s Guide to Big Data

It is no longer unusual to see data pools mushrooming and blooming at an alarming rate. Social Media and the Digital Revolution have accelerated this process. We’re now generating more data every year than all the data generated in previous years of humanity, combined. And these numbers are only headed in one direction – up. Not surprisingly – the world calls it Big Data.

So what’s all the hype about? Why is data popularly considered the next oil? Let’s dive in.

Defining the buzzword

Surprisingly however, the term was first used back in the late 90s. It defines the large volume of data, structured and unstructured, that inundates a business on a daily basis. However, the volume alone does not form the core of the concept. It brings in a myriad of technologies, algorithms, and dependent concepts. The massive amount of data calls for a holistic information management strategy as the volume makes it difficult to process using a conventional database and software tools and techniques. Big Data has found application in business areas and industries across all paradigms: Healthcare, Hospitality, Manufacturing, and even Agriculture, to name a few.

The 4 Vs of Big Data

Big Data is normally defined using the 4 Vs.

#1 – Volume

The amount of data covered by this umbrella term might be intimidating and huge. However, another interesting aspect of the Big Data is its granularity. Data is poured in from billions of sensors, electronic device usage, social media content and even menial daily chores like banking and shopping trips. It takes an immense level of processing to extract information from the noise contained in the large amounts of data. This information can then be used to draw behavioral insights and hence, drive businesses better.

#2 – Velocity

The rapid rate of data procurement and processing gives Big Data its unique characteristics. Owing to the high velocity of data, it is often streamed directly into memory, instead of saving it on disk. Platforms are now developing and promoting ways for processing data real-time for better applications. IoT (Internet of Things) applications like healthcare and safety services need real-time data-processing and recommendation-designs.

#3 – Variety

Big Data entails all kinds of data: unstructured text documents, video and audio streams, financial transactions or collected results of scientific experiments. Due to this, unstructured data needs to be processed into structured data before analyzing it. There are several pre-processing and data extraction techniques like summarization, data cleaning, outlier removal or normalization methods. These tools help us extract usable information from a large noisy data-pool.

#4 – Value

It is fairly obvious that raw data will need to be tuned and analyzed through several rounds to derive any meaningful information. This information is of utmost value to various stakeholders in the chain. A series of investigative and quantitative techniques help one drive unimaginable conclusions from Big Data- from a customer’s sentiment towards a particular service or a product to the prediction of a functional failure in a machine on the manufacturing floor. On the flipside, low-quality data questions the veracity of the same. Hence, it becomes very important to filter the desired information from the low-entropy noisy signals.

Using Big Data

Having defined Big Data, we need to cover grounds on the multiple technological tools businesses use to play around with it. Since data comes in different formats – structured, semi-structured and unstructured, the databases vary too. While structured data is collated in relational databases, examples of semi-structured data can be seen in XML formats. Unstructured data, however, can be found in various formats like PDF files, Text files or ever audio streams.

To deal with each of these data formats, a large variety of technical platforms have been designed. Many big organizations use special Big Data clusters, instead of conventional data warehouses. Some examples are NoSQL databases, Tableau, Hadoop and its companion tools (for instance: Spark, Hive, and Kafka). These tools also assist in processing and visualizing Big Data. This is further used by data analysts and scientists to draw insights from the data and hence recommend actionable steps to drive the businesses.

Big Data ushers us to another era of Digital Revolution

A large network of sensors are pouring data into the digital world. Coupled with the large processors and computers crunching the data, an intricate mesh of tech-nodes – Internet of Things (IoT) finds itself on the global scene. With large volumes, faster velocities, and a staggering variety of data being generated every second, algorithms designed to train machines to learn and operate like human beings are the need of the hour. Machine Learning (both supervised and unsupervised) forms an integral part of Big Data Analytics (does Funnel Analytics sound familiar? Your business likely uses it).

Newer algorithms and technological platforms prop up every day. Big Data stands at the entrance of (and perhaps, holds the doors to) the next age Digital Revolution.

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