Data: The New Oil

Years long,oil an immense,untapped valuable asset of the 18th century ruled the economy of the country and became the world's most valuable resource. It was the key functionality of everything from government to local companies. Students then would have strived to create a career path around this industry or sector (Lolz). Fast forward...

The 21st century a new particular wonder that amazes the world, which isn't limited to a sector,profession, and career path emerges which is DATA.

The emergence of data in every field has changed the course of work activities, data just like oil when refined gives you detailed information about the results and decisions to take.

DATA Compared with Oil

•Information can be extracted from data just as energy can be extracted from oil.

•Traditional oil is finite, data availability seems infinite.

•Data flows like oil but we must “drill down” into data to extract value from it. Data promises a plethora of new uses — diagnosis of diseases, direction of traffic patterns, etc. — just as oil has produced useful plastics, petrochemicals, lubricants, gasoline, and home heating.

•As a tangible product, Oil faces high friction, transportation and storage costs. As an intangible product, data has much lower friction, transportation and storage costs.

•Oil is a scarce resource. Data isn’t just abundant, it is a cumulative resource.

Standardizing data is essential, but selecting the correct input is also important because the algorithm is created based on the data. And, choosing that data is not easy. One of the problems that can occur when selecting data is that it can be biased in some way, creating a problem known as selection bias. That means that the data used to train the algorithm does not necessarily represent the entire space of possibilities. The saying in the industry is, “Garbage in, garbage out.” That means that if the data entered into the system is not correct, then the model will not be accurate. This is best illustrated by the parable in
 “Artificial Intelligence as a Negative and Positive Factor in Global Risk,” Eliezer Yudkowsky:

Let's check out data application in some companies mentioned below,

Pinterest – Improved Content Discovery

Whether you’re a hardcore pinner or have never used the site before, Pinterest occupies a curious place in the social media ecosystem. Since Pinterest’s primary function is to curate existing content, it makes sense that investing in technologies that can make this process more effective would be a priority – and that’s definitely the case at Pinterest.

In 2015,Pinterest acquired Kosei a machine learning company that specialized in the commercial applications of machine learning tech (specifically, content discovery and recommendation algorithms).
Today, machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers. Amazing right!

Baidu – The Future of Voice Search

Google isn’t the only search giant that’s branching out into machine learning. Chinese search engine Baidu is also investing heavily in the applications of AI.

A simplified five-step diagram illustrating the key.    stages of a natural language processing system
One of the most interesting (and disconcerting) developments at Baidu’s R&D lab is what the company calls Deep voice,a deep neural network that can generate entirely synthetic human voices that are very difficult to distinguish from genuine human speech. The network can “learn” the unique subtleties in the cadence, accent, pronunciation and pitch to create eerily accurate recreations of speakers’ voices.
Far from an idle experiment, Deep Voice 2 – the latest iteration of the Deep Voice technology – promises to have a lasting impact on natural language processing, the underlying technology behind voice search  and voice pattern recognition systems. This could have major implications for voice search application, as well as dozens of other potential uses, such as real-time translation and biometric security.

IBM – Better Healthcare

The inclusion of IBM might seem a little strange, given that IBM is one of the largest and oldest of the legacy technology companies, but IBM has managed to transition from older business models to newer revenue streams remarkably well. None of IBM’s products demonstrate this better than its renowned AI, Watson.

An example of how IBM’s Watson can be used
to test and validate self-learning behavioral models

Watson may be a Jeopardy! champion, but it boasts a considerably more impressive track record than besting human contestants in televised game shows. Watson has been deployed in several hospitals and medical centers in recent years, where it demonstrated its aptitude for making highly accurate recommendations in the treatment of certain types of cancers.
Watson also shows significant potential in the retail sector, where it could be used as an assistant to help shoppers, as well as the hospitality industry. As such, IBM is now offering its Watson machine learning technology, on a license basis – one of the first examples of an AI application being packaged in such a manner.

DISNEY- USES BIG DATA to Boost Customers Experience 
DISNEY is getting even better thanks to big data.Every visitor gets their own Magic Band Wristband that serves as ID, hotel room key,tickets,FastPasses and payment system.

While guest enjoys the convenience, Disney gets a lot of data that helps them anticipate guest needs and deliver an amazing,personalised experience.They can resolve traffic jams,give extra services to guest who may have been inconvenience by a close attraction and data allows the company schedule staff more efficiently.

“Around a decade ago, we first heard the expression ‘Data is the new oil’. It was coined by Clive Humby, the man that built Clubcard, the world’s first supermarket loyalty scheme. He was using the metaphor to explain how data is a resource that is useless if left ‘unrefined’: only once it’s mined and analysed, does it create (potentially extraordinary) value.

Finally, the reality about oil is that its supply, as well as its use cases, are finite. The reality with data is the opposite: as long as there are humans around, we will always create more data.

Thanks for reading,give your comments and share if found insightful.

Thanks Once again!