Category Archives: Big Data

Conquering Disease with Artificial Intelligence and IBM Watson

Conquering Disease with Artificial Intelligence and IBM Watson

Artificial Intelligence and IBM Watson

Artificial Intelligence, or AI, is growing increasingly pervasive in today’s modern world. Perhaps the most publicized and recognizable application of AI to date, IBM’s Jeopardy-winning computer, Watson, is now being used to help cure cancer. IBM announced the development of Watson for Genomics on Wednesday at the National Cancer Moonshot Summit. The supercomputer aims to analyze gene structures of cancer patients to determine where mutations occur and in turn figure out potential causes and treatments.

Watson differs from other supercomputers because it is able to answer questions in natural language, not just binary code or technical inputs, making it extremely practical for busy doctors on the move. Currently, coming up with cancer treatment plans for specific patients is a time-intensive process. First, the entire genome of a patient must be analyzed, then a team of doctors must convene to figure out the best treatment plan. Watson can do all of it in under three minutes by tapping into massive data sources of medical literature and the sequenced DNA of patients on file.

IBM-Watson
(Image Source: Kaesler Media / Shutterstock)

This is just the latest in a series of healthcare applications IBM is looking to use its Watson supercomputer. Watson has already worked with Alder Hey Children’s Hospital and CVS to improve patient care and predict future health issues. Healthcare is just one potential vertical for IBM, and they are looking for creative ways to use their super-computing power to help industries across the board. The software giant even developed an LED-light filled dress worn at the Met Gala that changed based on the mood of the event on social media.

Watson is even teaming up with doctors at the Veterans Affair Department (VA) to provide treatment suggestions to 10,000 US veterans over the next two years. That means 30 times more patients will receive care than by using the current doctor-by-committee approach.

It is time-intensive and it is not scalable,” says Dr. Michael Kelly, national program director of oncology at the VA. “One human couldn’t do it, it takes a panel.” Or one Watson. Watson will look at a DNA sequence and explore potential causes for the cancer, known as deidentified genetic alteration files. Watson will cross reference the DNA sequence of each patient with other patients’ DNA and contemporary medical literature to formulate what gene mutations have occurred and potential treatments to those.

Watson and IBM also aim to benefit from access to 3.5% of the country’s cancer patients and their data. But working through genome sequences is only half of the equation for Watson. Stephen Harvey, vice president of Watson Health at IBM says, “The other half of the job is filtering out things that wouldn’t be clinically valuable for doctors like Dr. Kelly.” In other words, Watson may be able to take orders in practical language, but it will need to give them to doctors as well, a challenge now at the core of all AI developers world-wide.

By Thomas Dougherty

The Fundamentals of Predictive Analysis

The Fundamentals of Predictive Analysis

Predictive Analysis

Article sponsored by SAS Software and Big Data Forum

Analytics is playing an increasingly important role in our lives thanks in large part to internet of things (IoT) developments and a greater appreciation of Big Data. With solutions that range across business productivity, health care, individual and national security, new insights are regularly generated. But just as such technology enriches our day-to-day lives, it produces considerable privacy and security vulnerabilities.

Unlimited Possibilities with Big Data

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In a recent IDG Research Services study, in partnership with SAS, it was noted that IT and business leaders are expressing excitement about Big Data’s potential impact on process optimization and customer experience, along with product and service innovation. People are quickly learning that Big Data has great value, but it’s taking a little longer to understand how. We’re faced with an explosion of stored data as cloud data-housing tools making large-scale retention possible, the popularity of IoT devices collecting information that was previously discounted, and social media churning out customer needs and wants faster than ever. The analysis of Big Data, however, promises us the possibility of understanding everything. How specific diseases are spreading; which politicians will gain public approval; why products succeed or fail; when to withdraw your pounds and reinvest in the dollar.

The Value of Predictive Analytics

The key, however, to accessing all the benefits of Big Data, is effective analytics. Merely employing analytics isn’t enough, and so analysts are advancing into the data-supported forecasting of predictive analytics. In its entirety, predictive analytics helps organizations discover, analyze, and act on their data, using historical information to uncover trends and calculate probable results. Further, predictive analytics provides a framework for periodic analysis which results in more precise insights and enhanced strategies.

Analytics, Cyber Security, and Safeguarding Information

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(Image Source: Shutterstock)

As always, what benefits the good can just as easily aid the corrupt. Data privacy and security is a weighty concern that’s gaining momentum as the collection and access of data increases. With private and sensitive information collected through financial applications, healthcare systems, social structures, and consumer databases, just about everything concerning an individual or organization is viewable to those who, legitimately or not, gain access to the data. And so, much worth is placed on cybersecurity strategies, data protection measures, and the control of both data and the tools we use to collect it. Organizations must implement robust cybersecurity plans that encourage security as a standard practice; employee education, too, is fundamental to a protected environment, as is network awareness, suitably trained security staff, and the implementation of sustainable security solutions.

Event Trails

Of course, there is another security slant to analytics which should be considered, and that is the security it can provide. Already, advanced analytics are used in the fight against money laundering, bank and insurance fraud, and evolving cybersecurity threats. It can potentially even help welfare organizations prevent child abuse. While security experts previously focused mainly on security tools that would prevent cyber attacks and keep sensitive information locked away from prying eyes, cybersecurity analytics is a new weapon in the arsenal. Because it’s almost impossible to completely prevent data infringement, it’s important to consider what can be done after an intrusion occurs. Every attack leaves a network event trail, such data forming the digital DNA of the attacker, and this data is the beginning of analytics value to cybersecurity through investigation and cybercrime detection.

Big Data and analytics may still be in the early stages, but the value we’re already experiencing suggests a future of improved solutions, practical forecasts, and advancement through mere bits and bytes.

By Jennifer Klostermann

Clouding Around With The Unicorns

Clouding Around With The Unicorns

The Social Unicorn

Early investors and technology consumers alike love the poignantly named “Unicorn” companies, or private startups that reach the $1 billion valuation. The most recent unicorn spotting is social media game-changer Snapchat, which was recently valued at $16 billion. According to a recent infographic from Alexa discovered via Adweek, Snapchat also proves to be one of the rarest unicorns of all.

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In the technology industry, 25% of all unicorn companies are in the E-commerce vertical; social media is a distant 7th producing only 5.36% of total unicorns. The infographic shows the viral takeoff of the platform over the past few years, which says the biggest growth may still lie ahead for Snapchat, especially considering the youth of their user base. Social platforms can benefit from the exponential exposure their own platforms give them, and it looks like Snapchat has done just that.

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Compared to Unicorns in other industries, Snapchat still lags behind companies like Aerospace iconoclast Space X and Deem in Big Data. This year has seen the rise and fall of unicorns like DraftKings and FanDuel, obviously competing in a different vertical, but a case in point of how a changing market or user-base can quickly make a unicorn company mythical, some never to be seen again.

By Thomas Dougherty

Curing Cancer With Big Data

Curing Cancer With Big Data

Cancer & Big Data

The fight against cancer has been going on for centuries. Many leaders have tried and failed to bring about change to cancer treatment. Richard Nixon famously declared a War on Cancer with the National Cancer Act of 1971, and while the Nixon administration certainly increased research funding, they ultimately fell short of eradicating this horrible disease.

The new Cancer Moonshot championed by Vice President Joe Biden has a much better chance of making good on its lofty premise because of the extensive collaboration promised by all stakeholders in an effort to finally and permanently cure cancer.

The Cancer Moonshot aims to develop new ways to treat cancer that can be in use by 2020 (Infographic provided by Maacenter.org). This will be achieved by bringing together each and every part of the healthcare process to ensure that information is shared by anyone who may have ideas about how to cure cancer. Never before have research institutions, government agencies, academia, big pharma, doctors, and even patients themselves banded together in hopes of ridding the world of its most recognizable and ubiquitous disease.

Moonshot

This effort is exciting for all those affected by cancer and is especially invigorating for people afflicted with rare cancers like mesothelioma. With only 3,000 people diagnosed every year, the disease is largely an afterthought when it comes to cancer funding and large-scale research projects. However, by targeting specific tumor signatures patients can be cured, not with a disease-specific treatment, but by personalized therapies that calibrate the patient’s immune system to fight off diseased cells. Cancers of all types would be permanently impacted with this new method of treatment.

For all initiative of this magnitude to realize success, all resources possible must work together to achieve a common goal. How data is securely stored, analyzed, and shared will have an incredibly significant role in the effort. A large step towards breaking down the silos in data sharing was taken with the announcement of the National Cancer Institute’s Genomic Data Commons (GDC). When completed, the GDC will be an interactive and searchable database that allows doctors to access the most recent information about treatments from around the country.

DarenThe GDC platform is the first of its kind and represents an innovative way to share and analyze information. Because of the nature of the disease, sharing results of these genomic trails will be integral to this data resulting in clinical advancements. For the moonshot to work it must have sound and significant data to tell the story of its success. As Daren Glenister puts it, “Big data and learning algorithms will enable researchers to identify patterns and anomalies that, for instance, may help to identify patients who can benefit from standard treatments, or whose tumors require a different approach.”

Landing on the moon was once thought impossible. Finding the cure for cancer can be viewed with some skepticism. However, with the advancements in genomic medicine of the past 20 years combined with a revolutionary collaboration of ideas and data, we have never been positioned better to finally beat cancer once and for all.

By Sarah Wallace

IoT & Predictive Analytics In Healthcare

IoT & Predictive Analytics In Healthcare

IoT & Predictive Analytics

In a report by Grand View Research Inc., it’s predicted that the global Internet of Things (IoT) healthcare market will reach nearly $410 billion by 2022, with mobile penetration, software automation, and innovation medical devices promising rapid testing, greater accuracy, portability, and user-friendliness. Chronic diseases such as obesity, diabetes, heart failure, and hypertension specifically can be better addressed with IoT devices, offering users greater personal independence while still having their vital signs monitored, activity and safety measured, and medication supervised.

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IoT Healthcare Solutions

We already have a host of IoT solutions available in the healthcare sector, possibly most prevalent the fitness monitors. But such personal activity trackers are really just a taste of what IoT technology can offer the healthcare industry. Infusion pumps, lighting and heating automation, and record keeping utilizing IoT tech already aid hospitals and healthcare providers, but pioneering organizations are taking a leap forward. Boston Medical Centre, for instance, uses sensors which prevent newborn babies from being removed from the hospital without being signed out, and monitors for patients in the neonatal ICU alert nurses via cell phone when significant changes to vital signs are detected.

Little known and future possibilities include Asthmapolis, an asthma inhaler with built-in GPS-sensor, which records the data of time and location of use. This provides specific environmental information that can help asthma sufferers avoid problem areas. Google and Novartis have teamed up, and a prototype digital contact lens that measures blood sugar levels through tear liquid has been developed, proposing easier management of diabetes. And Vitality’s GlowCabs addresses medication monitoring, an area in which the WHO believes 50% of patients are not correctly following doctors’ advice. Using light and sounds, the GlowCabs system signals users when it’s time to take medication, and once a week a report is sent to GlowCap users containing information about medication use to date.

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IoT Security Concerns in Healthcare

The question of security is still being answered by innovators, though, with the medical data collected being particularly sensitive. Notes Anura Fernando, who has served on the FDA Medical Device Interoperability Council, “It’s very challenging in this rapidly moving market. They have to balance safety and effectiveness and innovation. It seems clear-cut but sometimes it’s not. It’s truly a tough balance.” Inappropriate access to or sharing of such data should be addressed not only by the makers of IoT devices but by the organizations that implement them. Data privacy and security policies already in place will have to be updated to include the broader data landscape that IoT devices touch, and adequate encryption and virus protection systems for individual devices will require continuous evolution and updating to ensure new threats are obstructed.

IoT, Predictive Analytics & Healthcare

With the over-abundance of medical data available, and increasing exponentially day by day, predictive analytics tools offer a better way to quickly unearth relevant insights. Infection surveillance systems are growing in popularity, helping prevent hospital-acquired infections, monitor surgical risks, and predict outbreaks of deadly diseases. Already a $260 million market, analysts forecast a growth of 14.1% CAGR to 2021. Moreover, with healthcare providers taking on the responsibility for longer-term patient outcomes, and often trusted with a single patient’s care over many years, predictive analytics technologies are helping change and improve systems. Using predictive analytics tools, predictions can be made for particular patients, based on their individual information. This method doesn’t rely on the bell curve, and doesn’t have to group a range of patients for accuracy, and so predictions are more precisely suited to particular patients. Additionally, preventative medicine advanced with predictive analytics, impacting favorably on public health.

From wearable sensors to telemedicine applications to medical smartphone applications, the health sector has an influx of tech assistance. The talented, benevolent, and shrewd all have a role to play.

By Jennifer Klostermann

Big Data Predictions: Who Will Win The UEFA Euro 16?

Big Data Predictions: Who Will Win The UEFA Euro 16?

UEFA Euro 16

Iceland beating England on Monday 27 June 2016 and causing them drop from UEFA Euro 16 was a surprising result. This is even more unexpected in light of Microsoft Bing’s predictions for how the championship would play out.

Microsoft’s search engine, Bing, uses a complicated algorithm that draws from social media, surges in searches on its platform and team performances in the past to make predictions about the winners of certain future competitions, tournaments or championships. At first this seems completely ridiculous – every fan posting on social media or searching for their team will “predict” (read: want) their team winning. How can this predict who the actual winner will be?

UEFA Euro 16

(Image Source: IDN / Shutterstock)

The thing is, Bing has been impressively accurate in the past, correctly predicting the outcome of the FIFA World Cup, Cricket World Cup, Oscars and even the Scottish referendum. Of course when it comes to predictions of reality shows such as The Voice, predictions are easier to make as the winner is chosen by way of popular vote.

Coming to the Euro 16, Bing predicted how the entire championship would unfold. It was predicted that England would win over Portugal and Iceland would win over Russia in the playoff rounds. Iceland would not make it past the quarterfinals whereas England was expected to playoff (and lose) against Spain in the semi finals. Ultimately, Germany is (at the time of writing this article) expected to win over Spain in the final.

Bing is not the only search engine making such predictions. Yahoo was also tasked with predicting the winner of Euro 16, drawing from Yahoo Sport and the multitude of activity on social media site Tumblr. That England would lose to Spain in the semi finals was also predicted.

So, although both predictions shattered any optimism of England making it to the final and potentially winning this year, they failed to account for a situation in which Iceland knocked England out of the tournament. But considering how the algorithm works, that this wasn’t predicted should itself not be surprising – Bing and other online predictive algorithms can only take what is available from social media, searches and team history and formulate its own predictions. One wonders then how different these predictions are to our own human predictions, save for those exceptions where, let’s say, Iceland beats England.

By Jason de Klerk

Dismissal Of Class Action Lawsuit A Setback For Internet Privacy

Dismissal Of Class Action Lawsuit A Setback For Internet Privacy

A Setback For Internet Privacy

On Monday the Third U.S. Circuit Court of Appeals (a federal appeals court) unanimously dismissed a class action lawsuit by parents of children under the age of 13 who had used Nickelodeon’s websites against Google and Viacom (which owns the Nickelodeon websites). This was a result of litigation beginning in 2013.

The class action disputed the legality of planting of cookies to gather and track the browsing information from the computers and phones of children who had played games and watched videos on Nickelodeon’s websites. Collecting IP addresses, the Court held, was not against the law. The laws in question came from the Video Privacy Protection Act from 1988. This legislation prohibits the personally identifiable information about a person’s video viewing habits from being disclosed. This law was amended in 2013 but was regrettably not brought up to speed with current Internet privacy trends. The result was that the Court found Viacom and Google had only received the browsing and video viewing history – it did not disclose it.

data-privacy

While Google was held not to be liable at all, Viacom itself could still be liable on the promise it makes on its registration forms that “HEY GROWN-UPS: We don’t collect ANY personal information about your kids. Which means we couldn’t share it even if we wanted to!” Even though Viacom disclosed no personal information, collecting such information is potentially a breach of that promise to its users.

What this ruling illustrates is, first, that the laws around Internet privacy are severely outdated. Technology and its uses change and grow exponentially whereas lawmakers are slow to meet these demands. Secondly, it demonstrates that the Courts are willing to allow invasions of privacy by strictly interpreting the law. However, near the end of the ruling the Court does note the need for change in this area: “Our decision necessarily leaves some unanswered questions about what kinds of disclosures violate the Video Privacy Protection Act. Such uncertainty is ultimately a consequence of our common-law system of adjudication and the rapid evolution of contemporary technology. In the meantime, companies in the business of streaming digital video are well advised to think carefully about customer notice and consent. Whether other kinds of disclosure will trigger liability under the Act is another question for another day.” Hopefully that day is not too far in the future.

By Jason De Klerk

How You Can Improve Customer Experience With Fast Data Analytics

How You Can Improve Customer Experience With Fast Data Analytics

Fast Data Analytics

In today’s constantly connected world, customers expect more than ever before from the companies they do business with. With the emergence of big data, businesses have been able to better meet and exceed customer expectations thanks to analytics and data science. However, the role of data in your business’ success doesn’t end with big data – now you can take your data mining and analytics to the next level to improve customer service and your business’ overall customer experience faster than you ever thought possible.

Fast data is basically the next step for analysis and application of large datasets (big data). With fast data, big data analytics can be applied to smaller data sets in real time to solve a number of problems for businesses across multiple industries. The goal of fast data analytics services is to mine raw data in real time and provide actionable information that businesses can use to improve their customer experience.

Fast data analytics allows you to turn raw data into actionable insights instantly

Analyze Streaming Data with Ease

The Internet of Things (IoT) is growing at an incredible rate. People are using their phones and tablets to connect to their home thermostats, security systems, fitness trackers, and numerous other things to make their lives easier and more streamlined. Thanks to all of these connected devices, there is more raw data available to organizations about their customers, products, and their overall performance than ever before; and that data is constantly streaming.

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With big data, you could expect to take advantage of at least some of that machine data, but there was still an expected lag in analysis and visualization to give you useable information from the raw data. Basically, fast data analytics allows you to turn raw data into actionable insights instantly.

With fast data analytics services, businesses in the finance, energy, retail, government, technology, and managed services sectors may create a more streamlined process for marketing strategies, customer service implementation, and much more. If your business has an application or sells a product that connects to mobile devices through an application, you can see almost immediate improvements in how your customers see you and interact with your business, all thanks to fast data analytics.

Consider a few real-world examples of how fast data analytics have helped companies across business sectors improve their performance.

A Financial Firm Monitors Flow of Business Transactions in Real-Time

The world of finance has always been fast-paced, and today a financial firm can have many millions of transactions each day. There’s no way to spare the time or effort to constantly search for breaks and/or delays in these transactions at every hour of the business day. However, with fast data analytics, they found that they could consistently monitor the flow of business throughout the day, including monitoring of specific flow segments, as well as complete transactions.

With the right fast data analytics service, the firm was able to come to a proactive solution in which they could monitor the production environment using their monitoring software’s automated algorithms to keep a constant eye on transaction times. The software’s algorithms determined whether transaction flows were within acceptable parameters or if something abnormal had occurred, giving the firm the ability to respond immediately to any problems or abnormalities to improve their customer experience and satisfaction.

A Large Insurance Firm Ensures Faster Claim Processing

In another case, a large health insurance provider with over three million clients was in the process of a massive expansion. As the firm expanded, though, they noticed a disturbing trend. Over the span of a single month, the average processing time for claim payments had increased by a dramatic 10%, but only for a single type of transaction. While they had the tools necessary to analyze the operating system problems, the servers’ hardware, application servers, and other areas where the problem could be originating, they were dealing with monitoring tools that were half-a-decade old.

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(Image Source: Shutterstock)

Thanks to these outdated monitoring tools, the insurance provider had a very expensive problem on their hands, as finding the solution was taking up over 90% of their tier-three personnel’s time and energy. Not only that, but customers were actually finding the majority of their application problems before the provider’s IT support could detect them.

To immediately diagnose the problem and get ahead of it, the insurance firm deployed a fast data monitoring service that immediately diagnosed what was causing the delays in claims processing transactions. The solution was found promptly – one claim type was sitting in a queue long enough that it would time out – and that the addition of new branch locations was causing over-saturation of their architecture design. By reconfiguring their middleware, they were able to accommodate the load increase and solve the problem without taking up valuable employee time and resources.

Just a few of the benefits of deploying this service were:

  • A 40% decrease in the mean-time-to-repair for the software problem.
  • A 60% decrease in the time spent by third-tier personnel to solve the problem.
  • A 35% decrease in the number of open tickets at help desk.
  • More than 30% improvement in the average processing time for claims.

A Securities Firm Ensures Dodd-Frank Compliance

Enacted in 2010, the Dodd-Frank Wall Street Reform and Consumer Protection Act is a US federal law that was enacted to regulate the financial industry and prevent serious financial crises from occurring in the future. Securities firms and other financial institutions must ensure that they are Dodd-Frank compliant in order to stay in business and avoid the risk of serious litigation. One such securities firm implemented fast data monitoring and analysis for Dodd-Frank compliance for all of their SWAP trades.

To be Dodd-Frank compliant, firms must report all SWAP trades “as soon as technologically possible”. Within a few minutes of the execution of a trade, a real-time message and a confirmation message must be reported, as well as primary economic terms (PET). If a message is rejected for any reason, it must be resubmitted and received within minutes of execution.

Without monitoring of their reporting systems, the securities firm found that they were in danger of being found non-compliant should anything go wrong within their internal processes. Fast data analytics solution gave them the real-time monitoring they needed to stay compliant.

How Can Fast Data Analytics Help Your Business?

As you can see from these examples, fast data analytics makes it possible for businesses to quickly turn raw machine data into actionable insights by tracking transactions, identifying issues with hardware and software, and reducing customer complaints. With the ability to identify and solve these issues faster and more efficiently, fast data analytics services can significantly improve any business’ customer experience.

These processes can all be monitored in real-time, giving you access useful analytics and insights for time-sensitive activities. Fast data analytics can help you stay compliant with government and/or industry regulations, avoid preventable losses and it improve your personnel’s efficiency by pinpointing errors and problems without taking up a lot of employees’ time and energy.

By Ronald van Loon

CloudTweaks Comics
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Five Signs The Internet of Things Is About To Explode

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