Gone are the days when Big Data used to be problem. For those who are not acquainted with what the  term means, Big Data is a contex...

Big Data - Opportunity or Obstacle?

April 28, 2017 Libsys Blog 16 Comments



Gone are the days when Big Data used to be problem. For those who are not acquainted with what the term means, Big Data is a contextual term coined to denote the gigantic volume of data that companies or organization amass through multiple mediums. This data was earlier collected for the sake of collection, but gradually with time this has become the treasure chest of future strategies!

This collection of data is called Big Data because sometimes its volume is not limited to gigabytes or terabytes, it rather escalates to the scale of Petabytes or Exabytes. Today’s corporate world is leaving no stone unturned to find every possible method of collecting data, from mobile devices, service logs, cameras, microphones to wireless sensor mediums. The Radio Frequency Identification Readers (RFID) have also started contributing to data collection along with aerial and local recordings etc. all across United States. 

Big Data, Bigger Potential!

With the unprecedented advancement in technology, not only have the companies started valuing what this massive dataset has to offer, but also safeguarding it from being copied or stolen, irrespective of how irrelevant the bit of data it may be.

While the companies hunt for every possible way to collect as much data as possible that includes any aspect in context of either their company or their customer, the technology is sculpting new ways to formulate new solutions to existing problems on the base of collected data. Big Data is not always a structured bulk, sometimes it is unstructured to the levels that it can only be analyzed by industry experts. Its Four V’s : Volume, Velocity, Variety and Variability make its handling a tedious job, but as the wise have said, The Bigger, The Better! 

From Analysis to Analytics! 

The heavier the datasets become, the higher is the precision needed to collate, create and confine it. Today’s edgy technology has a lot of solutions in store for managing not just the safe storage of the data, but also its sorting and screening.

The key motive of implementing a chain of processes on this data is to extract the Patterns and Anomalies. The search of patterns lead us to finding anomalies, which in turn opens the Pandora’s Box. With the assistance and support of complementing analytical expertise, a company can find the answers to their cross-sector concerns like customer information, inventory management, feedbacks, customer responses, huge piles of unaddressed data or maybe a surprising broken link of information crucial to your organization.

Since the past decades have witnessed the benefits of raking the data enough to find what lies hidden in it for us. Data is the treasure, analytics is the key!
Will you lose it or use it? 

16 comments:

  1. In any case, it is costly, sets aside a long effort to create, and needs adaptability because of the trouble in accomplishing consistency and accord for a typical information model for the whole association.Data Analytics Course in Bangalore

    ReplyDelete
  2. project management course by 360DigiTMG is the best one in Hyderabad and is a Registered Education Provider (R.E.P.) by PMI to conduct training for this globally recognized certification.
    project management course
    pmi acp certification

    ReplyDelete
  3. Positive site, where did u come up with the information on this posting?I have read a few of the articles on your website now, and I really like your style. Thanks a million and please keep up the effective work. Tableau Data Blending

    ReplyDelete
  4. This comment has been removed by the author.

    ReplyDelete
  5. After reading your article I was amazed. I know that you explain it very well. And I hope that other readers will also experience how I feel after reading your article.

    data science training in gurgaon

    ReplyDelete
  6. I have to search sites with relevant information ,This is a
    wonderful blog,These type of blog keeps the users interest in
    the website, i am impressed. thank you.
    Data Science Course in Bangalore

    ReplyDelete
  7. I am looking for and I love to post a comment that "The content of your post is awesome" Great work!


    Data Science Course

    ReplyDelete
  8. This is a wonderful article, Given so much info in it, These type of articles keeps the users interest in the website, and keep on sharing more ... good luck.

    Data Science Training

    ReplyDelete
  9. I was very pleased to find this site.I wanted to thank you for this great read!! I definitely enjoying every little bit of it and I have you bookmarked to check out new stuff you post.
    data science course in malaysia

    ReplyDelete
  10. I like viewing web sites which comprehend the price of delivering the excellent useful resource free of charge. I truly adored reading your posting. Thank you!
    data science training in hyderabad
    data analytics course in hyderabad
    business analytics course in hyderabad

    ReplyDelete
  11. Awesome blog. I enjoyed reading your articles. This is truly a great read for me. I have bookmarked it and I am looking forward to reading new articles. Keep up the good work!
    Data Science Course in Pune
    Data Science Training in Pune

    ReplyDelete
  12. I just got to this amazing site not long ago. I was actually captured with the piece of resources you have got here. Big thumbs up for making such wonderful blog page!

    Simple Linear Regression

    Correlation vs Covariance

    ReplyDelete
  13. Such a very useful article. Very interesting to read this article.I would like to thank you for the efforts you had made for writing this awesome article.
    data science training in guwahati

    ReplyDelete
  14. I just got to this amazing site not long ago. I was actually captured with the piece of resources you have got here. Big thumbs up for making such wonderful blog page!

    Simple Linear Regression

    Correlation vs Covariance

    ReplyDelete