Big Data

Big Data is the new reality of everyday living and stands for an immensely huge pool of data which cannot be de-jargoned with ordinary and archaic methods of data interpretation. Such huge data-sets form the basis of all reasonable aspects of society. Data-driven decision-making is gaining leverage and has lent more authenticity to modern ways of life.  Thus, it is not just big data but the various correlations or linkages which can be drawn between two completely different data-sets that has led to explosion in value of the unstructured and unsorted datasets. Such analysis is highly complex and cumbersome as most data comes in unorganised format  which takes painstaking efforts to analyse, organize, retrieve and model the mine of data. Another major hurdle after all this is crossed is accurate interpretation and presentation of the results. This step is critical to drawing relevant conclusions and actionable knowledge.

Data management and analysis poses novel challenges to the experts. This creates a need for appropriate investment of time and resources to streamline the processes further, to create more economic value for the nations. One has to come up with revolutionary approaches to completely turnaround traditional modes of data analysis tools and systems.

Applications of Big Data

Data is being generated at unprecedented scale  as it has come to support many human endeavours.

  • Scientific analysis: All scientific disciplines work on facts which are now supported by numbers. These are both the basis for scientific data repositories which come handy to draw new conclusions and interpretations. The discipline of bioinformatics is a pure combination of biology and information technology which involves curation and analysis of huge data-sets to draw meaningful insights. Likewise, other disciplines like physics, astronomical data, chemical analysis etc. is intensively data-based.
  • Education: In this field, data is usually compiled to cater to two specific needs-
    • the student performance and analysis
    • teaching aids and techniques

Both the approaches have proven successful under different scenarios. The collection of various forms of student data to track the performance is highly appreciated by both the parents and teachers. Likewise, web-driven education methods are another buzzword which has led to a measurable enhancement of  teaching and academic effectiveness.

  • Healthcare: Use of IT in healthcare cannot be undermined. Rising costs of sophisticated healthcare stem from the fact that IT-based machinery and equipment has made diagnosis more accurate and costly too. This has reduced risk to life by improving quality and timeliness in the industry. The patient data is recorded and stored in databases which can be easily extracted in future to browse through the disease history.
  • Likewise many other fields and disciplines rely on data and its uses to erect sound systems for effective delivery like financial matters viz. Stocks, investment patterns and analysis, risk analysis; environmental modelling, census and population studies or databases, computational algorithms, inventory management etc.

Challenges faced to monitor big data

There are many challenges one faces to realise the full potential of big data. These can be broadly categorised into three types:

  • Volume: The huge amount of data the biggest challenge which is posed to the experts. This data is highly disorganised and needs to be properly refined and structured to draw valuable conclusions.
  • Variety: It stands for the diversity in types of data, representation modes and interpretation modes.
  • Velocity: It means the rate at which data is generated and arrives with experts for further analysis.

These three peripherals pose grave challenges for experts working with big data in different fields.


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