How to handle Big Data {Science & Tech}

Big Data

Big data reflects the changing world we live in. The more things change, the more the changes are captured and recorded as data. Take weather as an example. For a weather forecaster, the amount of data collected around the world about local conditions is substantial. Logically, it would make sense that local environments dictate regional effects and regional effects dictate global effects, but it could well be the other way around.

It is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Why Is Big Data Important?

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable

1) cost reductions,

2) time reductions,

3) new product development and optimized offerings, and

4) smart decision making.

When you combine big data with high-powered analytics, you can accomplish business-related tasks such as

  • Determining root causes of failures, issues and defects in near-real time.
  • Generating coupons at the point of sale based on the customer’s buying habits.
  • Recalculating entire risk portfolios in minutes.
  • Detecting fraudulent behavior before it affects your organization.

Activities performed on Big Data

  • Store – Big data need to be collected in a seamless repository, and it is not necessary to store in a single physical database.
  • Process – The process becomes more tedious than traditional one in terms of cleansing, enriching, calculating, transforming, and running algorithms.
  • Access – There is no business sense of it at all when the data cannot be searched, retrieved easily, and can be virtually showcased along the business lines.

Who uses big data?


With large amounts of information streaming in from countless sources, banks are faced with finding new and innovative ways to manage big data. While it’s important to understand customers and boost their satisfaction, it’s equally important to minimize risk and fraud while maintaining regulatory compliance. Big data brings big insights, but it also requires financial institutions to stay one step ahead of the game with advanced analytics.


Educators armed with data-driven insight can make a significant impact on school systems, students and curriculums. By analyzing big data, they can identify at-risk students, make sure students are making adequate progress, and can implement a better system for evaluation and support of teachers and principals.


When government agencies are able to harness and apply analytics to their big data, they gain significant ground when it comes to managing utilities, running agencies, dealing with traffic congestion or preventing crime. But while there are many advantages to big data, governments must also address issues of transparency and privacy.

Health Care

Patient records. Treatment plans. Prescription information. When it comes to health care, everything needs to be done quickly, accurately – and, in some cases, with enough transparency to satisfy stringent industry regulations. When big data is managed effectively, health care providers can uncover hidden insights that improve patient care.


Armed with insight that big data can provide, manufacturers can boost quality and output while minimizing waste – processes that are key in today’s highly competitive market. More and more manufacturers are working in an analytics-based culture, which means they can solve problems faster and make more agile business decisions.


Customer relationship building is critical to the retail industry – and the best way to manage that is to manage big data. Retailers need to know the best way to market to customers, the most effective way to handle transactions, and the most strategic way to bring back lapsed business. Big data remains at the heart of all those things.


The problem with so much information is that there is a much larger haystack now in which one has to search for the needle.

Causes of blunders

  • Low-quality data.
  • Unreliable sources.
  • Technical glitches.
  • An improper understanding of the larger picture.
  • Lack of proper statistical tools and resources to analyse large volumes of data.

How to mine Big Data?

Mining and geological engineers design mines to remove minerals safely and efficiently. The same principle should be adopted by statisticians in order to mine data efficiently.

Big Data is more complex and involves additional challenge. They might involve the use of some skills involving analytics, decision-making skills, logical thinking skills, problem-solving, advanced computational expertise and also statistical expertise.

Source: The Hindu

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1 Comment
  1. Reply
    February 14, 2018 at 10:24 am

    Brilliant sir… Excellent explanation and beautiful insight about the subject…

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