Information is the oil of the twenty-first century, and analytics is its combustion engine. Pursuing this strategically will create an unprecedented pool of information of enormous variety and complexity. This is leading to a change in data management strategies which is known as big data. This creates what we call as a pattern-based strategy architecture. An architecture that seeks signals models them for their impact and then adapts it to the business process of the organisation.’

-W Edwards Deming

According to Wikipedia, data represents various values attributed to different parameters; thus, information is nothing, but the data in context with some attached meaning. As per a report published by Social Media Today, currently, 2.5 quintillion bytes of data are being produced by humans daily with faster computing speeds and rapidly evolving technologies with every passing day. The large amount of data produced, stored and analysed today has the potential to eventually change the way we live and carry out our day-to-day activities.


What is analytics?


Analytics is a technique that turns raw and unprocessed data into insight for making better choices and meaningful decision. It is the scientific process of discovering meaningful models / patterns that can be hidden in the data. Basically, it is the application of mathematics, statistical techniques and computer programming to data for uncovering hidden patterns and unknown correlations, thereby enabling fact-based planning and decision making within an organisation.


In current times, data analytics is categorised as a great revolution across the globe. As the technological innovations around data science are constantly exploring new dimensions, organisations need to give strategic importance to their data sources so that they could have an idea about customer’s perception and his behaviour so that they can offer the best of their products/ services according to the preferences and needs of the existing as well as prospective customers. Analytics enables the business organisation to deliver their customers in the context of the recommendation, right offer, service, tailored and personalised products to provide unequalled value. Data analytics addresses different types of data coming from various data sources, such as Enterprise Resource Planning (ERP) systems, Supply Chain Management Systems, Customer Relationship Management (CRM), E-Commerce Transactions, Human Resources (HR) software etc. It also ascribes semantic data that comprise Web Logs, Call Details Records (CDRs) from service / call centres, E-commerce footprints, trading systems data generated by machine and computer systems etc.

Some definitions

1. Big data analytics: In the year 2005, Roger Magoulas had coined the term ‘big data’ for the very first time. Big data are extremely huge data sets that are so large or complex that traditional data processing systems are inadequate to deal with them. It is a set of either structured ie the traditional form of data and unstructured data collected from emails, audio-video clips and other textual data etc. Big data analytics is the process of examining, handling and managing large data sets to discover unknown correlations, concealed patterns, preference of the customers, trends in the market and other useful business information for the benefit of both the banks and the customers.

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