Big Data And A Shocking Waste Problem

Big Data And A Shocking Waste ProblemIt’s a shocking fact that in the 21st century more than three quarters of a billion people do not have access to enough food to keep themselves healthy, while 30% of the food produced around the world goes to waste.

It’s partly a problem of logistics – it’s expensive to keep food fresh and get it to the people who need it most. This of course means its ripe for tackling with technology and Big Data.

Recently I had the chance to speak to one of the founders of Food Cloud – a social enterprise which harnesses the power of crowd sourcing in a bid to cut down on food wasted by retailers.

Food Cloud made headlines recently thanks to a partnership with Tesco , the UK’s biggest grocery seller. The simple premise is that it matches retailers that have surplus food stocks with charities that have the facilities to deliver it to those who need it.

Of course even a concept this elegantly straightforward requires some fairly advanced technology if it’s going to be rolled out at the sort of scale necessary to bring about social change. And plans are underway to operate the scheme in 1,000 Tesco stores by the end of the year.

Co-founder Iseult Ward told me “Our data driven approach is what differentiates us from a lot of traditional food surplus redistribution organizations.

“We’re able to collect all sorts of data on all of the food which is donated, and the organizations it is donated to, which allows us to do things like provide full sourcing and measure the performance of the stores and charities.”

 

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What every CEO/MD needs to know about Big Data & Data Analytics

What every CEO/MD needs to know about Big Data & Data AnalyticsIn order to remain competitive, and viable, businesses now have to deal with a vast and rapidly growing sea of what has been termed ‘Big Data’.  They need to be able to transform this raw data, often in real-time, into more meaningful insights about their markets, customers, competitors, and to measure and manage their performance more accurately using using techniques such as ‘Data Analytics’. In many cases this represents a paradigm shift from their comfort zone of approaches based more on experience, guesswork, or painstakingly constructed models of reality.

It used to be that Big Data and Data Analytics were the preserve of large global corporations but consider this definition for Big Data: When volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big Data is a relative term. Every organization will rapidly reach a point where the volume, variety and velocity of their data will be something that they have to address.

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Surge in real-time big data and IoT analytics is changing corporate thinking

Surge in real-time big data and IoT analytics is changing corporate thinkingOrganizations want real-time big data and analytics capability because of an emerging need for big data that can be immediately actionable in business decisions. An example is the use of big data in online advertising, which immediately personalizes ads for viewers when they visit websites based on their customer profiles that big data analytics have captured.

“Customers now expect personalization when they visit websites,” said Jeff Kelley, a big data analytics analyst from Wikibon, a big data research and analytics company. “There are also other real-time big data needs in specific industry verticals that want real-time analytics capabilities.”

The financial services industry is a prime example. “Financial institutions want to cut down on fraud, and they also want to provide excellent service to their customers,” said Kelley. “Several years ago, if a customer tried to use his debit card in another country, he was often denied because of fears of fraud in the system processing the transaction. Now these systems better understand each customer’s habits and the places that he is likely to travel to, so they do a better job at preventing fraud, but also at enabling customers to use their debit cards without these cards being locked down for use when they travel abroad.”

 

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The Big Data Difference: Predictive Analytics

The Big Data Difference: Predictive AnalyticsWhat if you could know in advance which patients would benefit from certain therapies? Or identify patients approaching a medical crisis and intervene before it’s too late? While doctors have traditionally had to rely on instinct to make these calls, predictive analytics could be a game changer for hospitals, healthcare providers and patients.

The Power of Prediction

Medical sensors and data analytics can be used to power medical devices that can predict adverse outcomes before they occur. By analyzing very large data sets, researchers can identify subtle markers, such as small changes in vital signs or patient behaviors that can be correlated to development of serious conditions like heart failure or kidney failure. If we can learn to look for the right signs, we can develop an early warning system for imminent medical crises.

Combining data analytics with body-worn or implantable medical sensors will allow us to better monitor patient health. These sensors can pick up subtle changes in biometrics, biomarkers and other patient data over time. Using predictive analytics, smart sensors could use these readings to detect early warning signs of kidney failure, stroke, heart failure and other medical crises, alerting healthcare providers before adverse events occur. Data analytics could also be used to power smart apps or devices that provide ongoing guidance to patients in response to sensor data in order to help them better manage chronic conditions.

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Big data car pool a traffic jam saver

Big data car pool a traffic jam saverThe rise of big data has given new hope that car pooling could be the solution to opening up Australia’s gridlocked city roads.

From disrupters like Uber to government officials, the potential of massive, detailed trip-mapping data is generating hope that commuters making the same trip each day might share the journey rather than each making their own way in their own road-hogging car.

From Uber’s new car pooling service to giving freight trucks a green light run through cities the effective use of data and technology could free up billions in wasted productivity, transport experts and operators believe.

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The Difference Between Big Data and Smart Data in Healthcare

The Difference Between Big Data and Smart Data in Healthcare“Big data” is one of those terms that gets thrown about the healthcare industry – and plenty of other industries – without much of a consensus as to what it means. Technically, big data is any pool of information that is compiled from more than a single source.

For healthcare organizations, this could mean creating a database that takes patient names and addresses from one system and matching it up with scheduled appointments from another, or integrating claims data with clinical notes from the EHR.

Stitching multiple sources of information together into a centralized databank accessed by reporting or a query system can provide a more in-depth and actionable snapshot of a patient’s history, diagnoses, treatments, socioeconomic challenges, and risk profiles.

But leveraging these disparate data sources requires the right tools and competencies, which aren’t always easy to develop.

Electronic health records are starting to take big data analytics seriously by offering healthcare organizations new population health management and risk stratification options, but many providers still turn to specialized analytics packages to find, aggregate, standardize, analyze, and deliver data to the point of care in an intuitive and meaningful format.

These tools may include quality benchmarking and performance measurement systems, clinical analytics algorithms that monitor patients in real-time, revenue cycle and claims analytics, and population health management packages that foster engagement, deliver alerts and reminders, stratify beneficiaries, or gauge risk of a certain disease.

In addition to the right technologies, providers must invest time and manpower into acquiring the competencies to make analytics work for them.  This includes crafting a dedicated team of experts to oversee big data projects, implement and optimize software, and convince clinicians that these new strategies are worth their while.

 

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Five Key Big Data Challenges Companies Need To Overcome When Developing A Big Data Strategy

Five Key Big Data Challenges Companies Need To Overcome When Developing A Big Data StrategyBig Data will Increase IT Dependency

In the past years, IT has become a lot more important for many organisations and in the coming years we will see, with the appearance of the Internet of Things and the Industrial internet, that many currently unconnected devices will become datafied and start generating vast amounts of data. For companies that develop offline products and only use IT for their website, this will mean a rigorous change. In the coming years, IT will become a central part of all business units. Big Data will infiltrate and affect all departments within your organisation and that requires a different way of working.

So apart from being able to access the data, Big Data will become an essential part of the different departments and consequently require IT staff of their own. For many organisations, IT will form a much more important aspect of their company and for currently ‘offline companies’ this is a major shift that needs to overcome.

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How companies are using big data and analytics

How companies are using big data and analyticsFew dispute that organizations have more data than ever at their disposal. But actually deriving meaningful insights from that data—and converting knowledge into action—is easier said than done. We spoke with six senior leaders from major organizations and asked them about the challenges and opportunities involved in adopting advanced analytics: Murli Buluswar, chief science officer at AIG; Vince Campisi, chief information officer at GE Software; Ash Gupta, chief risk officer at American Express; Zoher Karu, vice president of global customer optimization and data at eBay; Victor Nilson, senior vice president of big data at AT&T; and Ruben Sigala, chief analytics officer at Caesars Entertainment. An edited transcript of their comments follows.

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Planning Makes for a Successful Big Data Implementation

Planning Makes for a Successful Big Data Implementation CIOs have mastered the ability to capture and store data but how that data is used depends on how it is analyzed. The structures and processes that CIOs put in place to extract value from their big data efforts can determine their eventual success. We asked Rocana CEO Omer Trajman to elaborate on his experience implementing big data initiatives.

Trajman explains, “In our experience, the most successful analytics projects are ones backed by a solid business case that clearly identifies the business need for the project, how success will be measured, and the people, skills, and technology required to actually make the project a success. A business case enables the CIO to get the input and support of all key IT and LOB stakeholders in advance. It is this support that maximizes the likelihood of success of the analytics project.

“Often IT teams without a business case will attempt an analytics project on their own as an “under the radar” side project. These projects are almost always doomed to fail because they aren’t sufficiently staffed, and the technology used (typically free, unsupported open source components) takes much more effort to build into a production ready solution than anybody realizes. In contrast, an approved business case enables a CIO to get the resources he or she needs to properly staff the project. Often this includes acquiring an out-of-the box, fully supported data capture and analytics solution in order to dramatically accelerate the project.

“Therefore, what we would recommend to CIOs is that while any analytics project will require operational changes, the most important operational change they should focus on (if they aren’t doing so already) is to build and solicit support for a solid business case for their analytics projects, and have a plan to integrate and run the project as part of day to day operations.

“The primary rule with analytics is that the more data you have, the better your analytic output will be. And while we agree that CIOs have mastered the capture and store of structured, transactional data, the same cannot be said of the semi-structured and unstructured machine-generated data (e.g. metrics, logs, NetFlow). Every Fortune 500 organization is collecting only a fraction of the operational event data they really want and need, due to a combination of scalability and cost issues that prevent doing so. Our ABCs of starting and managing a data analytics effort based on our experiences with Fortune 500 customers is:

A: Acquire purpose-built analytics (marketing analytics, sales analytics, log analytics), don’t try to build a team of data scientists for each area

B: Be ready for the operational changes to people, processes, and technology required to make the analytics program a success

C: Collect as much data as you can and plan for explosive data growth– as much as 100x the amount of data ingested just over the next few years

Predicting and Preventing Health Issues with Big Data

Predicting and Preventing Health Issues with Big DataFor eons, healthcare, in whatever form it came in, was fairly straightforward. Someone would get sick or injured, and a physician would prescribe a cure or treatment using the prevailing medical knowledge of the day. This has held true even in modern times, where many people only think about their health during those times when it fails them.

While this approach is a practical one, especially when many have other concerns on their mind, it doesn’t make for long term healthy outcomes. That’s why many medical institutions are trying to use the latest technology to improve patient care and help people live healthier lives by trying to predict health problems before they happen.

Some of this advanced technology is obvious and visible, while much of it happens behind the scenes. This is where big data is being used. On the surface, big data may seem like an odd choice for the healthcare industry, but it’s actually a perfect fit that can help health professionals predict and prevent health issues.

When broken down into its most basic parts, healthcare is really all about data. Doctors are often seen carrying clipboards filled out with patient data such as age, height, weight, blood pressure, blood type, and more. Even such rudimentary data can be challenging to collect and analyze.

Now factor in more advanced information like CAT scans, X-rays, and the biggest goldmine of health data — genetic data. It quickly becomes obvious that the amount of health data that is currently available or will one day become available is absolutely massive. All of this information needs to be taken into account by big data analytics in order to come up with the best, most accurate predictions. Better data sets essentially equal better results.

That’s really what the move to institute predictive analytics is all about — determining the biggest at-risk factors in patients and preventing health problems from developing. In a sense, it’s similar to how companies use business analytics to predict who is most likely to buy their products; instead, in the case of healthcare, it’s an analysis of factors taken from many different sources over the course of one person’s life. A checkup as an eight-year-old would be considered, much like an emergency room visit would twenty years late

 

 

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