After a half decade of investment, and periods of trial and error, a near majority of business executives now report successful results from their Big Data investments. According to NewVantage Partners 5th annual Big Data Executive Survey, 48.4% of corporate executives that were surveyed indicated that their firm has achieved “measurable results” from their Big Data investments. Further, a remarkable 80.7% of executives now characterize their Big Data efforts to have been successful. This marks a sharp contrast with previous years, where investment levels had grown, but results had yet to be realized for most corporations.
NewVantage Partners survey, which was released on January 9th, reflects the viewpoints of corporate business leaders (CEO/President), data leaders (Chief Data Officer), technology leaders (Chief Information Officer), Analytics leaders (Chief Analytics Officer), and marketing leaders (Chief Marketing Officer). 50 major corporations were represented, including American Express, Bank of America, Bloomberg, Capital One, Charles Schwab, Disney, Ford Motors, General Electric (GE), MetLife, United Parcel Service (UPS), and USAA, among other leaders in financial services, the life sciences, media, and selected industry groups.
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Has a 70 year old idea come of age? – by Jackie Down

The quest for artificial intelligence (AI) began over 70 years ago, with the idea that computers would one day be able to think like us. Ambitious predictions attracted funding, but after a few decades, there was little to show for it.
 
But in the last 25 years new approaches to AI, coupled with advances in technology, have meant that we may now be on the brink of realising those dreams. The term AI can be confusing.  We all know there's never any shortage of buzzwords in the IT world, but when it comes to Artificial Intelligence, machine intelligence, machine learning they can be hard to tell apart.  Artificial Intelligence is basically an umbrella term for them all! Artificial intelligence refers to "a broad set of methods, algorithms and technologies that make software 'smart' in a way that may seem human-like to an outside observer,"
 
Artificial Intelligence is today replying to our emails on Gmail, learning how to drive our cars, and sorting our holiday photos. The problem with the concept of "artificial intelligence" is that people conjure images of supercomputers that operate spaceships like the Star Ship Enterprise or robots policing our cities, rather than particularly clever spam filters, or digital assistants like Cortana, Google Now and Siri.
 
Let’s take a look at a couple of AI applications that are here today...

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Is Machine Learning coming out of the laboratory at last?  – by Jackie Down

Today’s businesses are dealing with an onslaught of challenges, but undoubtedly a major hurdle is the use of data. Every single company and every single industry is struggling with this problem. Cutting-edge companies have realised that data is actually a strategic asset that needs to be harvested for rich insights that can help fuel business growth. They have also recognised that there has to be a culture conducive to turning data into something that’s actionable from the top down.
 
Until now, most of today’s data creation has been from increasingly powerful mobile devices and the various social media channels. The second wave of data growth, which will significantly increase the data growth is the Internet of Things (IoT). This data will continue to grow as more and more data sources come online over the coming years.
 
With all this data comes the challenge of integrating all these new and complex data sources across businesses.  Here is where the application of machine learning can help.
 
What is Machine Learning?
 
Machine Learning (ML) is a class of algorithms that can learn from and make predictions on data. Generally speaking, the more data the better the outcome for machine learning techniques. Machine Learning doesn’t require explicit rules to guide decision-making. It does not require manual construction of “if this, then that.” It will make that determination on its own, based on the data.
Machine Learning is...

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The “Cinderella of corporate assets”, but potentially the most valuable – by Jackie Down 
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Data may be a company's most valuable asset, but few treat it as they would other company assets - and hence are not maximizing the potential economic benefits. Internal data is one of the most heavily invested and yet most underutilized assets that companies have available to them.  It can help you to compete and operate in new ways — if only you can connect the dots and turn that data into value.
 
In a sense, data is the new currency. Armed with it, new companies are disrupting established industries, and traditional businesses are transforming the way they operate. "Where knowledge is power, data is wealth. It's not intrinsic to the data, it's what you do with it," said Bruce Daley, an analyst at market intelligence firm Tractica.  Companies like Google and Uber are most progressive in thinking about data differently, and it is these types of companies that are changing the economy. Most businesses lag way behind in imagining how data could be a differentiator for them.
 
Businesses, such as Experian, Thomson Reuters or Dun & Bradstreet have always been focused on deriving value from data – it’s their core business. New companies that have emerged from the social media revolution use the data collected on their platform to sell targeted online advertising.  Walmart gives suppliers its entire sell-through data — almost in real time, and by store – something of immense value to its suppliers. We are seeing that the ability to use and monetise data is now impacting almost every type of business.

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