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SENSORS AND BIG DATA ARE SHOWING HOW OUR MINDS WORK

20/2/2018

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Big data and personal sensing technology are revolutionising psychology, opening new frontiers in our understanding of how our minds work and how we treat mental illness. - By Andrew Trounson, University of Melbourne
When you think about it, your smart phone knows you in a way that no one else can, even your nearest and dearest. Even your psychologist.
Your phone can track where you are, how you are moving, what you are seeing, what you are hearing and, if linked to an activity tracker, how you are sleeping. It can even monitor your emails, texts and phone calls to assess how social you are. It may sound like Big Brother, but when governed by privacy protocols this wearable sensing, combined with big data computing, is the closest we’ve yet come to cataloguing our lived experience. It promises to uncover new information on how we think, learn, use language and recall memories, and better understand and treat mental illnesses. And it is already happening.
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Is Big Data Analytics The Secret To Successful Fire Fighting?

13/1/2017

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Fire departments fight fires, but they also deal with an awful lot of other incidents. Often this is by virtue of being the only ones with the training and tools to get the job done. On route to an incident in a large city, they may have an average of around four minutes to prepare for what they are likely to encounter when they arrive.
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Data comes to them from multiple sources simultaneously – radio contact from the control room, alerts via mobile devices and tablets, a touch screen information panel mounted in the response vehicle, or a mountain of technical manuals and literature on firefighting regulations and procedure.
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Now, forward-thinking fire services are looking at how this information can be best used to make sure that firefighters arrive at the scene fully equipped not just with the right tools, but the right data to get the job done.
Bart Van Leeuwen, a senior firefighter with the City of Amsterdam Fire Department, who also runs the data consultancy Netage, says “What I’m afraid of is that something will happen to me or one of my colleagues and we will find out that we had the data to stop it happening.
“Unfortunately, I’ve come across several cases where firefighters were sent into blazes and it was later found that they had no business being there – but they didn’t know that because they didn’t have access to the data.”

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or around seven years Bart has championed the use of “linked data” in firefighting. The concept is not just that fire departments be collecting as much data as possible through the technology which is becoming available – for example Internet of Things sensors attached to firefighting tools like engines and pumps.
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Executives Report Measurable Results From Big Data, But Challenges Remain

11/1/2017

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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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FCA chief set out big data agenda

23/11/2016

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The chief executive of the Financial Conduct Authority (FCA) has said insurers' use of big data will be regulated by a clear and robust framework to ensure customers are not exploited.
Speaking at the Association of British Insurers (ABI) conference this morning, Andrew Bailey said there were benefits to big data and telematics, such as incentivising better driving, but warned this technology could also be to the detriment of consumer protection.

"Let's suppose a genetic identification revolution means you can predict life expectancy and dementia. The implications for life insurance are profound," he said. "We need to be aware of the benefits versus the potential this could lead to divisions in society."
Another more well-known example used by Bailey was the potential for insurers to use big data to identify customers that are not prone to switching policy providers and subsequently increasing their premium. "There is a danger those inert policyholders could subsidise those who shop around," he said.
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Asked more specifically how the FCA will identify misuse of big data, Bailey said the regulator does not plan to build its own black box or retro-engineer a big data algorithm.
"The role for us is not to create our own model or algorithm, but have a good line of sight of how you use them. They are your black boxes not ours."
Earlier this year, the FCA decided against launching a full market study into the use of big data in the retail insurance sector, but said it would look at pricing practices in a small number of general insurers.
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You Don’t Need Big Data — You Need the Right Data

7/11/2016

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The term “big data” is ubiquitous. With exabytes of information flowing across broadband pipes, companies compete to claim the biggest, most audacious data sets. And businesses of all varieties — old and new, industrial and digital, big and small — are getting into the game.
Masses of social, weather, and government data are being leveraged to predict supply chain outages. Enormous amounts of user data are being harnessed at scale to identify individuals among a sea of website clicks. And companies are even starting to leverage huge quantities of text exchanges to build algorithms capable of having conversations with customers.
But the reality is that our relentless focus on the importance of big data is often misleading. Yes, in some situations, deriving value from data requires having an immense amount of that data. But the key for innovators across industries is that the ​
size of the data isn’t the most critical factor — having the right data is.
It’s Not About Big or Small Uber is often referred to as a big-data success story. There is no doubt that Uber captures a wealth of information. Using the applications it has running in both its drivers’ cars and its users’ pockets, it has mapped the real-time logistics flows of human transportation.
But Uber’s success isn’t a function of the big data it collects. That big data has enabled the company to enter new markets and fulfill new jobs in the lives of its customers. Uber’s success results from something very different: the small, right data it needed to do something very simple — dispatch cars.
In an era before we could summon a vehicle with the push of a button on our smartphones, humans required a thing called taxis. Taxis, while largely unconnected to the internet or any form of formal computer infrastructure, were actually the big data players in rider identification. Why? The taxi system required a network of eyeballs moving around the city scanning for human-shaped figures with their arms outstretched. While it wasn’t Intel and Hewlett-Packard infrastructure crunching the data, the amount of information processed to get the job done was massive. The fact that the computation happened inside of human brains doesn’t change the quantity of data captured and analyzed.
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The (big) data village: Get value through fostering internal partnerships

17/10/2016

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Within most corporate organizations, many (big) data programs can and will fail to create sustained business value. To avoid this risk, and to gain maximum value from data investments, organizations must overcome a number of stumbling blocks within the analytical supply chain — notably a lack of appropriate people, process or technology. Primarily we must focus on breaking down the social silos that impede efforts to adopt agility and build internal partnerships.
Entering Big Data 3.0, where agility has taken the spotlight, the traditional linear paradigm of data management is being challenged to return value. Even with access to large computing power, analysts, DevOps and IT, we know that without full alignment of the business and IT and a focus on breaking down the data silos, outcomes are likely to be less than optimal. In the worst cases, it becomes challenging to the point where project teams may perform their work with as much duct tape as best wishes.
Oftentimes I hear that organizations complete a project only to discover there was a parallel project, sometimes even two other projects, in the pipeline by other teams. This lack of synergy almost predicts that the output will be brittle and fall short of targeted outcomes. From there, moving that process into business value incurs additional technical debt translating it to other groups.
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 Business units need to create  and own a data strategy

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One method to combat this complexity is assuring that the people closest to the data collaborate and take ownership in the project, to guarantee its refinement and utilization, just as a business would do with any precious commodity.
In a perfect world, data and the information it carries for use in the rest of the company should be integrated, protected and shared with the teams who bring a business case for access to the data. It’s imperative that the business units within an organization understand the value of the data and the goal of their program, and that the custodians of the data work with the teams who need that access to the data.


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Artificial Intelligence

28/9/2016

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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 the next fad?

28/9/2016

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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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Monetising your data

28/9/2016

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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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