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How Big Data Will Change Every Business

If you’re still saying, “Big data isn’t relevant to my company,” you’re missing the boat.

Big data and its implications will affect every single business — from Fortune 500 enterprises to mom and pop companies — and change how we do business, inside and out.

It doesn’t matter what field you operate in or the size of your business; as data collection, analysis, and interpretation become more readily accessible, they will have an impact on every business.

Big Data Introduction

Why WaferWire

At waferWire we are trying to implement services which helps businesses understand and maintain there data more efficiently. Harnessing the value and power of data and cloud can give your company a competitive advantage, spark new innovations, and increase revenues. As cloud computing and big data technologies converge, they offer a cost-effective delivery model for cloud-based analytics.

As a delivery model, cloud computing has the potential to make your business more agile and productive, enabling greater efficiency’s and reducing costs.

In addition

  • Discover business reasons for organizations to adopt cloud for their analytics needs.
  • View an architectural overview of an analytics solution in a cloud environment with a description of the capabilities offered by cloud providers.
  • Learn about the architectural components of this solution.
  • See how our products can be used to implement the architectural components.
  • Review example architectural scenarios that might be similar to your operating environment.

What is Bigdata analytics

Bigdata analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information

Big data analytics examines not only structured information like purchase histories, customer relationship management (CRM) data and intelligence from industry but unstructured data including social media feeds, blogs, tweets, videos, and other sources. This structured as well as unstructured data is the basis of competition to enhance productivity. Big Data for businesses is helping organizations in planning and taking right decisions based on information gathered from different sources.

What is Bigdata analytics
What is Bigdata analytics

3 Vs of Big Data – Volume, Velocity and Variety

3Vs (volume, variety and velocity) are three defining properties or dimensions of big data. Volume refers to the amount of data, variety refers to the number of types of data and velocity refers to the speed of data processing.

Why Bigdata is important.

  • Data will become an asset to every business.

    Even the smallest businesses generate data these days. If the business has a website, a social media presence, accepts credit cards etc., even a one-person shop has data it can collect on its customers, its user experience, web traffic, and more. This means companies of all sizes need a strategy for big data and a plan of how to collect, use, and protect it. This also means that savvy businesses will start to offer data services to even very small companies.

  • Big data will enable companies to collect better market and customer intelligence.

    Like it or not, the companies you do business with know a lot about you — and the quantity and diversity of what they know about you is increasing every year. Every company (from car manufactures who will monitor our driving to tennis racket manufacturers that know how often and how well we play) will get much better insights into what customers want, what they will use, what channels they use to buy, and so on.

  • It will improve internal efficiency and operations

    From using sensors to track machine performance, to optimizing delivery routes, to better tracking employee performance and even recruiting top talent, big data has the potential to improve internal efficiency and operations for almost any type of business and in many different departments.

    Companies can use sensors to track shipments and machine performance, but also employee performance. Companies have started using sensors to track employee movements, stress, health, and even who they converse with and the tone of voice they use.

  • And data will allow companies to improve the customer experience and build big data into their product offering.

    John Deere DE +0.00% is an excellent example of a company that is not only using data to benefit its customers. All new John Deere tractors are equipped with sensors that can help the company understand how the equipment is being used, and predict and diagnose breakdowns. But they’ve also put the sensors to work for the farmers, offering access to data about when to plant, where, the best patterns for ploughing and reaping, and more. It’s become an entirely new revenue stream for an old company.


In India, our first brush on a large scale was around the elections.

During that time, we often heard stories on how political parties were looking at information at booth level and trying to gauge the mood of the nation. Information suddenly became a potent tool within political circles and we are now seeing a lot more activism around it.

How big data is improving lives in India


The first example I would like to cite is about the massive government exercise in the form of AADHAR. That project has enabled the government to reach out to the citizens directly, thus helping them in transferring various government benefits directly to their accounts – DBT, MNREGA, bio-metric attendance, etc.

Traffic management using big data

Namma Bengaluru is another example. This center monitors the feeds from various cameras at major junctions. In technical terms, this feed is a form of big data given its volume and velocity. The center analyses feed data and has automated signals at few of the junctions, thus enabling traffic to move smoothly.

Automation in manufacturing sector

Working in a tech organization, we are seeing disruption in the manufacturing sector driven by the need of faster and cheaper roll out of products. The sector is handling this need by looking at big data gained from various manufacturing processes and improving forecast, testing and production. For price sensitive markets like India, such automation and optimization of processes by relying on big data is helping bring down the product price, thus driving consumption.

An eye into future

For starters, we have seen how the massive urbanisation is crippling lot of present day cities. To mitigate that, the government is setting up a cluster that would be futuristic, this grid will be capturing data related to civic and personal amenities – birth, death, water, sewage, electricity, etc., thus eliminating all or most of the physical touch points. The data thus generated will help the government in allocating appropriate resources for business continuity as well as personal well-being.

Consumerisation of big data

Finally, on the consumer side of business, I would like to quote Myntra app-only approach, though it didn’t succeed the way it should have. The aim was to track the app usage, gather data and understand consumer behavior. This would have helped them send customized deals and improve sales.

Smart homes

We are already looking at few ‘smart’ homes where your devices have the capability to track usage and are interconnected. So based on the data usage, you might have your coffee ready when you wake up, water warm for you to bath, a toast at an optimum temperature, the room temperature optimized by the time you’re back from work, etc.

That would be the power of big data when you wake up tomorrow, and with India investing in ‘smart’ cities much of this could be a reality before you can anticipate.

Have a look at some of the challenges in Big Data

Main Challenges in Implementing Big Data in your Company

Global Survey of Big Data Users

Security - (51%)

Budget - (47%)

Lack of talent to implement big data - (41%)

Lack of talent to run big data and analytics - (37%)

Integration with existing systems - (35%)

Procurement limitations on big data vendors - (33%)

Enterprise not redy for big data - (27%)

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