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The Possibilities of Big Data

It is no secret that Big Data has some wonderful applications that may change the way we interact with businesses, and even more how they interacts with us through other facets of this rapidly growing field. But, what can it do concretely? This blog post shares insights of this question.
The Possibilities of Big Data

Endless Possibilities of Big Data

 It can tell you what may most probably happen

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Big Data- Down to the Tidbits

Any data difficult to process or store on conventional systems of computational power and storage ability in real time is better known as Big Data. In our times the growth of data to be stored is exponential and so are its sources in terms of numbers.

Big Data has some other distinguishing features which are also popularly known as the six V’s of Big Data and they are in no particular order:

  • Variable: In order o illustrate the variable nature of Big Data we may illustrate the same through an analogy. A single item ordered from a restaurant may taste differently at different times. Variability of Big Data refers to the context as similar text may have different meanings depending on the context. This remains a long-standing challenge for algorithms to figure out and to differentiate between meanings according to context.
  • Volume: The volume of data as it grows exponentially in today’s times presents the biggest hurdle faced by traditional means of systems for processing as well as storage. This growth remains very high and is usually measured in petabytes or thousands of terabytes.
  • Velocity: The data generated in real time by logs, sensors is sensitive towards time and is being generated at high rates. These need to be worked upon in real time so that decisions may be made as and when necessary. In order to illustrate we may cite instances where particular credit card transactions are assessed in real time and decided accordingly. The banking industry is able to better understand consumer patterns and make safer more informed choices on transactions with the help of Big Data.

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  • Volatile: Another factor to keep in mind while dealing with Big Data is how long the particular data remains valid and is useful enough to be stored. This is borne out by necessity of data importance. A practical example might be like a bank might feel that particular data is not useful on the credibility of a particular holder of credit cards. It is imperative that business is not lost while trying to avoid poor business propositions.
  • Variety: The variety of data makes reference to the varied sources of data and whether it is structured or not. Data might come from a variety of formats such as Videos, Images, XML files or Logs. It is difficult to analyze as well as store unstructured data in traditional systems of computing.

Most of the major organizations that are found in the various parts of the world are now on the lookout to manage, store and process their Big Data in more economical and feasible platforms so that effective analysis and decision-making may be made.

Big Data Hadoop from Apache is the current market leader and allows for a smooth transition. However with the rise of Big Data, there has been a marked increase in the demand for trained professionals in this area who have the ability to develop applications on Big Data Hadoop or create new data architectures. The distributed model of storage and processing as pursued by Hadoop gives it a greater advantage over conventional database management systems.


One fine day people realized that it is raining gems and diamonds from the sky and they start looking for a huge container to collect and store it all, but even the biggest physical container is not enough since it is raining everywhere and every time, no one can have all of it alone, so they decide to just collect it in their regular containers and then share and use it.

Since the last few years, and more with the introduction of hand-held devices, valuable data is being generated all around us. Right from health care companies, weather information of the entire world, data from GPS, telecommunication, stock exchange, financial data, data from the satellites, aircrafts to the social networking sites which are a rage these days we are almost generating 1.35 million GB of data every minute. This huge amount of valuable, variety data being generated at a very high speed is termed as “Big Data”.



This data is of interest to many companies, as it provides statistical advantage in predicting the sales, health epidemic predictions, climatic changes, economic forecasts etc. With the help of Big Data, the health care providers, are able to detect an outbreak of flu, just by number of people in the geography writing on the social media sites “not feeling well.. down with cold !”.

Big data was used to locate the missing Malaysian flight “MH370”. It was Big Data that helped analyze the million responses and the impact of the very famous TV show “Satyamev Jayate”. Big data techniques are being used in neonatal units, to analyze and record the breathing pattern and heartbeats of babies to predict infections even before the symptoms appear.

As they say, when you have a really big hammer, everything becomes a nail. There is not a single field where big data does not give you the edge, however processing of this massive amount of data is a challenge and hence the need of a framework that could store and process data in a distributed manner (the shared regular containers).

Apache Hadoop is an open source framework, developed by Doug Cutting and Mike Cafarella in 2005, written in java for distributed processing and storage of very large data sets on clusters of normal commodity hardware.

It uses data replication for reliability, high speed indexing for faster retrieval of data and is centrally managed by a search server for locating data. Hadoop has HDFS (Hadoop Distributed File System) for the storage of data and MapReduce for parallel processing of this distributed data. To top it all, it is cost effective since it uses commodity hardware only, and is scalable to the extent you require. Hadoop framework is in huge demand by all big companies. It is the handle for the Big hammer!!

Approach – Actionable Analytics

Approach – Actionable Analytics


In this blog post, we will discuss on the approach we can follow to provide an actionable analytics. Doing actionable analytics is not easier said than done. It requires a focused analytical process. Here we will outline the three important phase or levers that can improve the process of delivering actionable analytics. The three phases can help you to improve the financial aspects of the business by doing actionable analytics.


  • Discover
  • Explore 
  • Engage


For example, if we are delivering actionable analytics for the marketing function. In each phase we will identify some critical characteristics or parameters that are going to influence the financial value directly or indirectly.

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