5 “Oscar-Winning” Big Data Articles of the Week

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And the Oscar goes to… data scientists….

Predicting Oscar winners at the Annual Academy awards show is a well-established tradition among movie experts, entertainment press and bet placers of all kinds. Recent years, have brought new comers to the guessing game with data scientists demonstrating the power of prediction models. While this year’s results might be argued as clear foresight, some companies came actually close to the numbers. David Rothschild an economist with Microsoft New York research lab, was behind the team that successfully guessed six of the biggest Oscar winners, including the awards for best film, director, actor and actress. In fact, across all 24 categories Rothschild’s team only slipped up on four — original screenplay, original score, animated feature and film editing.

This week’s curation is about big data’s potential, challenges and surprisingly human side.

Happy reading!

1. Six Predictions About Big Data and Marketing in 2015

by David Steinberg, published on Marketing Profs

The primacy of Big Data in marketing and advertising has occurred so quickly that it’s hard to remember that just a few years ago, Big Data was initially met with resistance.

2014 has seen a new order emerge: the marketing and ad tech sectors have embraced Big Data—thoroughly and completely. In a short time, harnessing the power of Big Data (gathering, synthesizing, and acting on data on a large scale) has moved from an innovation to a critical success factor. Yet, there are many questions about how the evolution will play out in 2015. Which new advances will have an impact? Will profits from Big Data marketing be significantly higher? Is there a ceiling regarding how far Big Data can go or is the potential limitless, as global data creation and information collection grows exponentially and the cost of storing data continues to plummet? Read on to review predictions for the year to come.

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2. Big Data: Why Facebook Knows Us Better Than Our Therapist

by Daniel Newman, published on Forbes

A recent study by University of Cambridge and Stanford University researchers found out that Facebook could predict our personality more accurately than most of our close family, friends, and maybe even our therapist. The idea that our persons may not know us as well as Facebook employees is almost astounding. The good news is that the actual people at Facebook probably don’t know us that well as their systems do. Deep inside their data they have the insights to know us better than we even know ourselves and big data is what makes it possible.

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3. Gartner predicts 3 Big data trends for business intelligence

by Doug Laney, published on First Post

Big data has given businesses a window into valuable streams of information from customer purchasing habits to inventory status. However, internal data streams give only a limited picture, especially with the growth of digital business.
Gartner identified three trends that describe information’s ability to transform business processes over the next few years.

1. By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.
2. By 2017, more than 30% of enterprise access to broadly based big data will be via intermediary data broker services, serving context to business decisions.
3. By 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT.

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4. Big Data: The 4 Layers Everyone Must Know

by Bernard Marr, published on Data Science Central

It’s no secret that Big Data still causes a lot of confusion in people’s heads: What really is it? What is new and what is old wine in new bottles? To help clarify the concept, Mar describes the 4 key layers of a big data system – i.e. the different stages the data itself has to pass through on its journey from raw statistic or snippet of unstructured data (for example, social media post) to actionable insight. In his opinion, the whole point of a big data strategy is to develop a system which moves data along this path. His post defines the basic layers needed to get any big data project off the ground.

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5. A Brief History of Big Data Everyone Should Read

by Bernard Marr, published on Data Science Central

According to Marr, the history of Big Data as a term may be brief – but many of the foundations it is built on were laid long ago. Although it might be easy to forget, our increasing ability to store and analyze information has been a gradual evolution – although things certainly sped up at the end of the last century, with the invention of digital storage and the internet. Marr takes a look at the long history of thought and innovation which has led humanity since the dawn of the data age.

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