Big Data Analytics For Beginners

Big Data Analytics For Beginners

Big Data looks like streams of abstract information to many people which is gathered from internal and external sources like CRM systems, customer contact billing, market research, social media, mobile data and more together.

This data must surely be able to deliver value for both the company and the customer through a deeper understanding of customer behaviour, brand performance and market development.

What Big Data promises is at least this but in the real world, it's not that simple.

We already know that nearly Ninety per cent of the data available today was created over the last two years which makes this the age of big data. And as someone popularly put, data is the new oil.

But what does this mean exactly?

Nearly 3 trillion bytes of data and information gets generated daily through our activities on our smartphones, tablets, GPS devices sensors spread all over, Cities Bank cards. What can be done with all this data and more importantly is there something to be gained from it. This is where big data analytics combines high technology systems and Mathematics, which come together to create a capability that can analyse information. This kind of information can generate great value for both companies and countries.

Big data analytics is the study of large, stored data to extract Behaviour patterns.

These data are characterized by the high-speed, they are being generated with the huge volume they represent, the immense variety of typology they encompass and the degree of paralysis

Big data, big benefits in real life

The world generated more than 2000 exabytes of patient records and test results. All this data is generated at a very high speed which attributes to the velocity of Big Data which refers to the various types of data like structured, semi-structured and unstructured data examples that include XL records, log files and the x-ray images.The veracity of the generated data is its accuracy and Trustworthiness. Analysis of all this data will benefit the medical sector by enabling faster disease detection, better treatment at a reduced cost. This is the Value of Big Data.

So How do you go from accumulating and managing the ever-growing mountain of abstract and diverse data to gaining insights for making a substantial impact in creating value with big data analytics?

Well, there are several frameworks around this but one of the main ones comes from America. Their framework applies to any business; they do this not by focusing on the technical abstract side of big data, but by taking a more practical approach starting with the business objectives, to begin with. It takes certain capabilities to define the right questions and to apply analytics that generate the right answers to these questions.

Big data, big benefits in real life

This input is the heart of the Big Data value creation.

The first step is to Define.

The right Key Performance Indicators to measure the value to your firm and the value to your customers at three levels:

1. Market

2. Brand and

3. Customer

The second step is to define -
A Data strategy identifying the data that can be used to monitor these KPIs which can be redefined and broadened at any stage by putting all efforts together. Later the data can be retrieved, merged, and analysed as determined.

The final step is to gain
Insights and create access through an actionable dashboard and that any company may be provided easy access to valuable insights to make a significant impact on their business to learn.

Types of analytics used commonly

Broadly classified, there are four types of big data analytics.

1. Descriptive Analytics: This explains what happened in the past Based on data presented through Graphics or reports, but not why or what will happen in the future. It was observational and not causal in the analysis.

2. Diagnostic Analytics: This is closely linked to the Descriptive but seeks to understand the reasons why any given event took place in the past.

3. Predictive analytics: Most widely used form of analytics, as it is most useful for companies to go through data to predict what could happen?

4. Prescriptive analytics: where the evolution of the preceding approach based on automation processing or AV testing.


Latest analytics systems advice/ recommend on the future course of action as per their requirements besides analysing and predicting data. For example, advising the best location on a website to place a banner for the most convenient gas station on the route and to avoid traffic jams in any area.

Big data analytics help companies or public administrations to understand their users better and find unnoticed past opportunities. This helps them provide a better service and even mitigate fraud.

Given all the above, we can safely say that the revolution of ‘ratification’ is just beginning.
Types of analytics used commonly

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