Big Data Analytics Helping to Predict and Prevent Cyber Attacks

Big Data Analytics Helping to Predict and Prevent Cyber AttacksExpanding mobile/online banking channels, the rise of algorithmic trading, and escalating regulatory requirements have dramatically increased the amount of customer, business, and transactional data that financial organizations must store and protect.

The unrelenting growth of Big Data can be a double-edged sword for financial institutions; while it presents valuable opportunities to streamline operations and improve service delivery, it also increases vulnerability from a cybersecurity standpoint.

The threat of a cyberattack has become omnipresent in the financial services industry. According to a recent Websense report, businesses in this sector encounter security incidents 300 percent more frequently than other industries. The report also revealed the high degree of sophistication in the methods used by criminals targeting financial organizations, whose attacks are specifically designed to fade into the “background noise” and be very difficult for financial chief information security officers (CISOs) to detect.

While Big Data has served to amplify data security concerns in the financial sector, when advanced analytics is applied to that data it can also provide organizations the opportunity to better protect their data, identify suspicious activity or behavior, and take steps to prevent cyberattacks before they occur.

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How HR Departments Can Obtain and Use Big Data

How HR Departments Can Obtain and Use Big DataEnterprises are constantly seeking new ways to use analytics across their organizations, and the human resources department is no exception. According to a Towers Watson survey, companies are spending a good portion of their HR technology budgets on big data and analytics to improve the hiring process, retain employees, and make better business decisions. While big data can give HR a seat at the overall decision-making table when used correctly, many companies struggle with obtaining the data. Additionally, those in possession of hiring and employee analytics struggle with how to best use this data in a way that matters.

The following is an exploration of how HR departments can leverage big data analytics to become strategic business partners in their organizations.

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2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud Apps

2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud AppsKey takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months.

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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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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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Analytics in Government: Picking Up Speed or Caught in an Eddy? (Industry Perspective)

Analytics in Government: Picking Up Speed or Caught in an Eddy? (Industry Perspective)Whether U.S. voters are advocates for “smaller government,” “better government” or both, analytics has great potential to make federal, state and local government agencies more efficient, faster and able to operate with fewer staff members and make their staffs more effective.

In the private sector, applying analytics and using big data is a major trend that’s gaining momentum — 73 percent of private-sector enterprises said they are now using analytics or plan to start in the next two years, according to a leading research firm’s latest report. Likewise, applying analytics to big data is “one of the hottest trends in the public sector,” according to Tod Newcombe, a senior editor with Government Technology.

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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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