Big Data Analytics: Numbers Never Lie

What is Big Data and its Origin ?

In 2013 the term Big Data was added in the Oxford dictionary with a definition “extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially relating to human behavior and interactions.” In the Cambridge Dictionary Big Data is defined as a very large set of data that is created by the people by using the internet and that data can only be handle, stored, understood and used with the help of special tools and methods that are designed for it. Big Data is such a big thing that it does not fit the architecture of a typically used database system because of its massive and gigantic and dynamic nature.

On the other hand large data processing is very difficult to perform with the help of  any conventional database management systems available in the market because they don’t have the adequate capacities by default.

IBM openly claims about the Big Data that it comes from three major primary sources:

  1. Machine Data: it gathers information from different sensors, Industrial equipment, GPS devices, Cameras on the road, medical devices and even from satellites.
  2. Social Media: it gathers information and data through the likes, shares and comments that are generated through social media. It also fetches data in the form of videos and images that we upload on the internet.
  3. Transactional Data: information and data is collected through online and offline transections as well as invoices, payments that we make Delivery receipts and different storage records.

Big Data Analytics?

The process of Big Data analytics is a series of steps of examining, filtering, aggregating and modeling of large and complex chunks of data in order to discover hidden patterns, different marketing trends, conclusions and meaningful correlations between different variables. One of the famous techniques of Big Data Analytics is Data Mining. Data Mining is used to derive different patterns and sequences that help in predictive activities. Unfiltered, unstructured and raw data is itself a chaotic and unclear data and it always gives minimum values. These different analytical techniques are used to retrieve intelligent and structured insights from the raw data and to make decision based on these results in many areas of business.

In general data is gathered from many different sources that include digital and traditional, online and offline or by any other means and this data is then stored in different platforms in various format. For example a company’s Human resource department may have different database than sales and transection department. This process generates a new concept called ‘’Data Silo” which is came into existence due to the data isolation inside the same organization. To solve the problem of data silo companies invest in data integration because Data Analysis is only applicable on that data that is retrieved and combined with one data centric architecture that can be accessed by other applications quite easily and also on the place where data is continuously updated in real time.

Processes of Data Analytics:

In the process of Data Analytics we break something big into separate small elements in order to run a detailed examination. There are multiple stages in the data analysis process. It starts from obtaining raw and unstructured data and after processing it the process will end with a desired structured and useful output.

The Seven “V”s of Big Data

Challenges and opportunities both comes along with Big Data Analytics. In order to do a successful and useful analysis it is highly recommended to know the different characteristics of big data that is defined by seven ”v”s

  • Volume
  • Velocity
  • Variety
  • Value
  • Variability
  • Visualization
  • Veracity
  1. Volume: it is the actual amount of the data or it can be defined as how much the data is weighted.
  2. Velocity: it is the rate of speed at which the data is being generated and the speed at which it must be processed.
  3. Variety: it is defined as the data types. The data is classified as structured data, semi-structured data and unstructured data. Structured data can be presented in the form of numerical information in a tabular formats, simple text, email or transections etc. in other hand unstructured data needs structural organization in order to be presented.
  4. Value: it is defined as the study to check whether the data is can easily be accessed to deliver quality analytics. If the data is valuable it will deliver actionable insights and useful details.
  5. Variability: it checks constant changing of data‘s meaning. What the data means today could mean a different thing tomorrow in the future,
  6. Visualization: it is the way to present data. After the data is processed and structured it must be demonstrated in a presentable manner. You may use graphs with different variables so that it can be readable to the audience.
  7. Veracity: it is defined as the legitimacy of data which means to check the data is trustworthy and consistent. In order to get processed data should be of quality. Ambiguous, duplicate or data with errors cannot be processed.

It’s time for you to uncover the secrets of big data go and discover it !

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