Author Archives: CloudTweaks

Problem In Customer Support – What Mobile App Developers Can Learn From AmEx

Problem In Customer Support – What Mobile App Developers Can Learn From AmEx

Mobile App Developers

Peanut butter and jelly, salt and pepper, marketing and… customer support?

We don’t tend to consider customer support as a complement to marketing, but when an organization experiences success in retaining customers and securing customer loyalty, it’s likely they owe it to the two segments of business working together. Marketing, with all its bells and whistles, gets all the glory for attracting new customers, but customer service – good customer service — is the secret sauce that keeps the customers coming back – and bringing their friends and family with them.

If you’re reading this, you’re probably a marketer or involved with marketing in some respect, so it’s likely you already know about the concept of “customer segmentation” – the process of separating a customer base into groups based on specific demographics or company engagement. However, customer segmentation can also provide a big payoff through customer support. Segmentation enables customer support reps to deliver differentiated experiences to users, allowing organizations to adjust their approach and service level based on:

  • Customer lifetime value (actual or potential)
  • Recent purchase/transaction
  • App engagement and usage history
  • Customer support team size
  • Fluctuating ticket volumes

By segmenting customers by these factors, it enables companies to define distinct service-level agreement per segment and optimize resource allocation accordingly. For example, a company may have a 48-hour wait time for a lower-tier customer, but a two-hour response time for a V.I.P. Additionally, being able to access customer segmentation data in real-time enables businesses to appropriately route their users’ tickets. A lower-tier customer may get routed to a general hotline, while V.I.P.s get a dedicated concierge.

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Most businesses deliver customer support based on the problem, routing customers to the right team or agent to address a given issue. But businesses that have taken a tiered customer group approach by focusing on the customer’s segment type have experienced happier customers and longer customer retention. Take American Express, for example. The financial institution aligned its help desk workflows with an eye for people, not problems. Over the last few years, the organization has created a tier of customers with a series of customer support services available to each. Its V.I.P. members expect V.I.P. service, whether they’re being notified of potential fraudulent activity or forget their password to their online account. By focusing on the customer first and the transaction second, American Express is able to deliver differentiated, higher-touch customer service to its highest value customers. The results speak for themselves: since shifting to a customer-centric model, the organization has been able to triple customer satisfaction, increase cardmember spend by 10 percent, and decreased its card member attrition by 400 percent.

When considering how to improve your customer retention and overall customer satisfaction, a natural first step is to start with changing the model for customer support to be customer-centric rather than problem-centric.

Here are some other things to consider:

  • Who are your most valuable customers?
  • Are you treating them differently than the rest of the pack?
  • Once you secure a customer, what’s the retention plan? Do you have one?

By aligning these elements with your overall marketing and customer support strategies, you’ll be well on your way to ensuring customer retention and loyalty.

By Barry Coleman, CTO, UserCare

barry_colemanBarry Coleman is CTO at UserCare, an in-app customer service solution that uses Big Data to help companies grow lifetime value by blending real-time support with relationship management.  Prior to UserCare, Coleman served as CTO and vice president of support and customer optimization products at ATG, which was acquired by Oracle for $1 billion. Coleman is the author on several patents and applications in the areas of online customer support, including cross-channel data passing, dynamic customer invitation, and customer privacy. He holds a B.A. in Artificial Intelligence from the University of Sussex.

Infographic: 12 Interesting Big Data Careers To Explore

Infographic: 12 Interesting Big Data Careers To Explore

Big Data Careers

A Career in Big Data isn’t just a dream job anymore nor is the terminology associated just another buzzword. It is now operational in almost every business vertical possible. Strategic decisions employ a variety of applications for Big Data in various industries and continue to create value for businesses across the board.

Everyone wants a piece of Big Data and the demand for jobs in the sector continues to outrank the supply. In order to carve out a career in this field, a course in Big Data and Analytics can provide an aspirant with a ladder to scale quickly.

This Infographic by simplilearn (below) takes you through 12 Interesting Career options in Big Data which opens the door for those seeking a career in this vertical.

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Digital Twin And The End Of The Dreaded Product Recall

Digital Twin And The End Of The Dreaded Product Recall

The Digital Twin 

How smart factories and connected assets in the emerging Industrial IoT era along with the automation of machine learning and advancement of artificial intelligence can dramatically change the manufacturing process and put an end to the dreaded product recalls in the future.

In recent news, Samsung Electronics Co. has initiated a global recall of 2.5 millions of their Galaxy Note 7 smartphones, after finding that the batteries of some of their phones exploded while charging. This recall would cost the company close to $1 Billion.

This is not a one-off incident.

Product recalls have plagued the manufacturing world for decades, right from food and drug to automotive industries, causing huge losses and risk to human life. In 1982, Johnson & Johnson recalled 31 million bottles of Tylenol which retailed at $100 million after 7 people died in Chicago-area. In 2000, Ford recalled 20 million Firestone tires losing around $3 billion, after 174 people died in road accidents due to faulty tires. In 2009, Toyota issued a recall of 10 million vehicles due to numerous issues including gas pedals and faulty airbags that resulted in $2 billion loss consisting of repair expenses and lost sales in addition to the stock prices dropping more than 20% or $35 billion.

Most manufacturers have very stringent quality control processes for their products before they are shipped. Then how and why do these faulty products make it to the market which poses serious life risks and business risks?

Koh Dong-jin, president of Samsung’s mobile business, said that the cause of the battery issue in Samsung Galaxy Note 7 device was “a tiny problem in the manufacturing process and so it was very difficult to find out“. This is true for most of the recalls that happens. It is not possible to manually detect these seemingly “tiny” problems early enough before they result in catastrophic outcomes.

But this won’t be the case in the future.

The manufacturing world has seen 4 transformative revolutions:

  • 1st Industrial Revolution brought in mechanization powered by water and stream.
  • 2nd Industrial Revolution saw the advent of the assembly line powered by gas and electricity
  • 3rd Industrial Revolution introduced robotic automation powered by computing networks
  • The 4th Industrial Revolution has taken it to a completely different level with smart and connected assets powered by machine learning and artificial intelligence.

It is this 4th Industrial Revolution that we are just embarking on that has the potential to transform the face of the manufacturing world and create new economic value to the tune of tens of trillions of dollars, globally, from costs savings and new revenue generation. But why is this the most transformative of all revolutions? Because it is this revolution that has transformed mechanical lifeless machines into digital life-forms with the birth of the Digital Twin.

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Digital Twin refers to the computerized companions (or models) of the physical assets that use multiple internet-connected sensors on these assets to represent their near real-time status, working condition, position, and other key metrics that help understand the health and functioning of these assets at granular levels. This helps us understand asset and asset health like we understand humans and human health, with the ability to do diagnosis and prognosis like never before.

How can this solve the recall problem?

Sensor enabling the assembly line and creating Digital Twin of all the individual assets and workflows provides timely insights into tiniest of the issues that can otherwise be easily missed in the manual inspection process. This can detect causes and predict potential product quality issues right in the assembly line as early as possible so that the manufacturers can take proactive action to resolve them before they start snowballing.  This can not only prevent recalls but also reduce scraps in the assembly line taking operational efficiency to unprecedented heights.

What is so deterrent? Why is this problem not solved most organizations that have smart-enabled their factories?

The traditional approach of doing data science and machine learning to analyze data doesn’t scale for this problem. Traditionally, predictive models are created by taking a sample of data from a sample of assets and then these models are generalized for predicting issues on all assets. While this can detect common known issues, which otherwise get caught in the quality control process itself, but it fails to detect the rare events that cause the massive recalls. Rare events have failure patterns that don’t commonly occur in the assets or the assembly line. Although, highly sensitive generalized models can be created to detect any and all deviations but that would generate a lot of false positive alerts which cause a different series of problems altogether. The only way to ensure that we get accurate models that detect only the true issues is to model each asset and the workflow channels individually, understand their respective normal operating conditions and detect their respective deviations. But this is what makes this challenge beyond human-scale. When there are hundreds, thousands or millions of assets and components it is impossible to keep generating and updating models for each one of them manually. It requires automation of the predictive modeling and the machine learning process itself, as putting human data scientists in the loop doesn’t scale.

But aren’t there standard approaches or scripts to automate predictive modeling?

Yes, there are. However, these plain vanilla automation of modeling process which just runs all permutations of algorithms and hyper-parameters again doesn’t work. The number of assets and as such the number of individual models, the frequency at which models need to be updated to capture newer real-world events, the volume of the data and the wide variety of sensor attributes all create prohibitive computational complexity (think millions or billions of permutations), even if someone has infinite infrastructure to process them. The only solution is Cognitive Automation, which is an intelligent process that mimics how a human data scientists leverage prior experience to run fewer experiments to get to an optimal ensemble of models in the fastest possible way. In short, this is about teaching machines to do machine learning and data science like an A.I. Data Scientist.

This is the technology that is required to give Digital Twin a true life-form that delivers the end business value – in this case to prevent recalls.

Does it sound like sci-fi?

It isn’t and it is already happening with the advancement in the world of machine learning and artificial intelligence. Companies like Google are using algorithms to create self-driving cars or beat world champions in complex games. At the same time, we at DataRPM are using algorithms to teach machines to do data analysis and detect asset failures and quality issues on the assembly line. This dramatically improves operational efficiency and prevents the product recalls.

The future, where the dreaded product recalls will be a thing of the past, is almost here!

By Ruban Phukan, Co-Founder and Chief Product & Analytics Officer, DataRPM 

www.datarpm.com

Write Once, Run Anywhere: The IoT Machine Learning Shift From Proprietary Technology To Data

Write Once, Run Anywhere: The IoT Machine Learning Shift From Proprietary Technology To Data

The IoT Machine Learning Shift

While early artificial intelligence (AI) programs were a one-trick pony, typically only able to excel at one task, today it’s about becoming a jack of all trades. Or at least, that’s the intention. The goal is to write one program that can solve multi-variant problems without the need to be rewritten when conditions change—write once, run anywhere. Digital heavyweights—notably Amazon, Google, IBM, and Microsoft—are now open sourcing their machine learning (ML) libraries in pursuit of that goal as competitive pressures shift focus from proprietary technologies to proprietary data for differentiation.

Machine learning is the study of algorithms that learn from examples and experience, rather than relying on hard-coded rules that do not always adapt well to real-world environments. ABI Research forecasts ML-based IoT analytics revenues will grow from $2 billion in 2016 to more than $19 billion in 2021, with more than 90% of 2021 revenue to be attributed to more advanced analytics phases. Yet while ML is an intuitive and organic approach to what was once a very rudimentary and primal way of analyzing data, it is worth noting that the ML/AI model creation process itself can be a very complex.

Data

The techniques used to develop machine learning algorithms fall under two umbrellas:

  • How they learn: based on the type of input data provided to the algorithm (supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning)

  • How they work: based on type of operation, task, or problem performed on I/O data (classification, regression, clustering, anomaly detection, and recommendation engines)

Once the basic principles are established, a classifier can be trained to automate the creation of rules for a model. The challenge lies in learning and implementing the complex algorithms required to build these ML models, which can be costly, difficult, and time-consuming.

Engaging the open-source community introduces an order of magnitude to the development and integration of machine learning technologies without the need to expose proprietary data, a trend which Amazon, Google, IBM, and Microsoft swiftly pioneered.

At more than $1 trillion, these four companies have a combined market cap that dwarfs the annual gross domestic product of more than 90% of countries in the world. Each also open sourced its own deep learning library in the past 12 to 18 months: Amazon’s Deep Scalable Sparse Tensor Network Engine (DSSTNE; pronounced “destiny”), Google’s TensorFlow, IBM’s SystemML, and Microsoft’s Computational Network Toolkit (CNTK). And others are quickly following suit, including Baidu, Facebook, and OpenAI.

But this is just the beginning. To take the most advanced ML models used in IoT to the next level (artificial intelligence), modeling, and neural network toolsets (e.g., syntactic parsers) must improve. Open sourcing such toolsets is again a viable option, and Google is taking the lead by open sourcing its neural network framework, Google’s SyntaxNet, driving the next evolution in IoT from advanced analytics to smart, autonomous machines.

But should others continue to jump on this bandwagon and attempt to shift away from proprietary technology and toward proprietary data? Not all companies own the kind of data that Google collects through Android or Search, or that IBM picked up with its acquisition of The Weather Company’s B2B, mobile, and cloud-based web-properties. Fortunately, a proprietary data strategy is not the panacea for competitive advantage in data and analytics. As more devices get connected, technology will play an increasingly important role for balancing insight generation from previously untapped datasets, and the capacity to derive value from the highly variable, high-volume data that comes with these new endpoints—at a cloud scale, with zero manual tuning.

Collaboration 

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Collaborative economics is an important component in the analytics product and service strategies of these four leading digital companies all seeking to build a greater presence in IoT and more broadly the convergence of the digital and the physical. But “collaboration” should be placed in context. Once one company open-sourced its ML libraries, other companies were forced to release theirs as well. Millions of developers are far more powerful than a few thousand in-house employees. As well, open sourcing offers these companies tremendous benefits because they can use the new tools to enhance their own operations. For example, Baidu’s Paddle ML software is being used in 30 different online and offline Baidu businesses ranging from health to financial services.

And there are other areas for these companies to invest resources that go beyond the analytics toolsets. Identity management services, data exchange services and data chain of custody are three key areas that will be critical in the growth of IoT and the digital/physical convergence. Pursuing ownership or proprietary access to important data has its appeal. But the new opportunities in the IoT landscape will rely on great technology and the scale these companies possess for a connected world that will in the decades to come reach hundreds of billions of endpoints.

martin-ryan-hi-rezBy  Ryan Martin and Dan Shey

Ryan Martin, Senior Analyst at ABI Research, covers new and emerging mobile technologies, including wearable tech, connected cars, big data analytics, and the Internet of Things (IoT) / Internet of Everything (IoE). 

Ryan holds degrees in economics and political science, with an additional concentration in global studies, from the University of Vermont and an M.B.A. from the University of New Hampshire.

Is Automation The Future Of Radiology?

Is Automation The Future Of Radiology?

Future of Radiology

For those of you who don’t already know, radiology is a subset of medicine that specializes in the diagnosis and treatment of diseases, illnesses and injuries based on imaging techniques. X-rays, MRI’s, CT scans, ultrasounds and PET scans all fall under the umbrella of radiology. Even within this medical niche you will find doctors that are highly specialized in treating certain parts of the body. Once you go down this rabbit hole, you will be shocked to see how deep it can go.

But how close are we to having the entirety of radiation automated by all-knowing robots that can do the job equally well, if not better than our well-trained doctors? The idea of automation in medicine is nothing new, as our exponential progress in technology has brought up the valid concern that robots are the future of medicine. We are already in the process of designing nano-sized robots to solve certain medical problems. We invest millions of dollars in building the best equipment that doctors and healthcare workers can get their hands on. What stops us from taking it one step further and having robots perform our jobs without having to lift a finger?

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Take IBM, for example. Their radiologist software Avicenna is already showcasing the future of automation in action. It was specifically programmed to make diagnoses and suggest treatments based on the patient’s medical images and data within their record. Early demos are already showing that its accuracy is on par with independent diagnoses made by trained radiologists. With more data fed to this software in the form of millions of anonymized patient data, it will gradually escape from demo testing and become a seriously useful tool in hospitals all around the world.

Another recent case study of robot-guided radiology in action is Entilic, a deep-learning machine system that is engineered for medical image recognition. According to a test that involved analyzing a CT scan of a patient’s lungs done against three expert human radiologists, “Enlitic’s system was 50% better at classifying malignant tumours and had a false-negative rate (where a cancer is missed) of zero, compared with 7% for the humans”. If this is the kind of result that we are seeing from a startup, just imagine what the implications will be when this technology is fully developed and integrated with the IT systems in healthcare facilities worldwide.

Many people are divided on the implications of automatizing the radiology-guided diagnosis and treatment of patients. The common argument against automation is that it will put a lot of radiologists out of work. Several decades of intense study and hard work will be thrown down the drain because a machine will be able to do their job with greater accuracy and success. Thanks to advances in artificial intelligence and deep learning within machines, this possibility cannot be disregarded any longer. We are already seeing several jobs in the transportation and manufacturing industries being lost to robots and well-programmed machines.

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On the other hand, those in favour of automation are arguing that radiology robots are going to help radiologists do their jobs instead of taking them away. Indeed, we still have a long way to go when it comes to rigorous testing and optimizing the ability of intelligent programs to accurately diagnose complex medical programs. One might go as far as to argue that radiology software will act as a checking system in which we can compare independent diagnoses against a machine-produced result. In the end, problems would be found far sooner and fixed far faster. It could even lead to reduced patient wait times!

We are fortunate enough that medical automation is still in its early developmental stages. There is still time left in the future for us to debate over the pros and cons of automation in radiology. No matter the outcome, it is blatantly clear that jobs needs to be at the forefront of this discussion. Either we provide hard-working radiologists with a new career path, or we find a way for automation to work alongside their work instead of against it.

What are your thoughts about automation in radiology? Are you for it or against it? Leave your thoughts in the comments below!

By Tom Zakharov

Tom is a Master’s student at McGill University, currently specializing in the field of Experimental Medicine. After graduating from the University of Ottawa as a Summa Cum Laude undergraduate, he is currently investigating novel indicators of chemotherapy toxicity in stage IV lung cancer patients. Tom also has 4+ years of scientific research in academia, government, and the pharmaceutical industry. Tom’s first co-authored paper investigated a novel analytical chemistry method for detecting hydrazine in nuclear power plants at parts-per-billion (ppb) concentrations, which can be viewed here.

3 Groundbreaking Wearables In The Travel Space

3 Groundbreaking Wearables In The Travel Space

3 Groundbreaking Wearables

The advent of wearable technologies had many expecting a utopia free of 20th-century pains such as paper maps, customer loyalty cards, lost luggage, and sluggish airport security.

Unfortunately, technological limitations have prevented wearables from revolutionizing the world. A number of devices struggle with voice recognition: Travel technology company Sabre found that about 16 percent of voice commands were ineffective with Google Glass during tests at an airport. To top it off, GPS in smartwatches and smartphones sometimes misses the mark, and battery life in most wearables is dismal.

If wearable use were as prolific as smartphone use, the potential applications while traveling might be nearly limitless. For now, initial excitement over wearables has not translated to long-term use. While a 2016 survey by PricewaterhouseCoopers found about 49 percent of respondents owned at least one wearable, the same study found that daily use of those devices decreased over time.

With data networks everywhere upgrading to 5G, connectivity woes might soon be a thing of the past. Rising interest in virtual reality and augmented reality technologies and Internet of Things applications is fueling curiosity in the devices, and advances in batteries and charging capabilities have the technology poised to break into the mainstream.

Wearables on the Rise

Nearly every tech company has a full line of wearables, and even fashion juggernauts such as Under Armour are moving toward connected clothing. Three main types of wearables are reaching mainstream success, and their applications will revolutionize the way we travel.

wearable-gps

1. Smartwatches.

Smartwatches most often connect to mobile phones, although Samsung’s Gear S2 and several pending releases also support separate data plans. This wristwear has a screen and an operating system that makes it ideal for notifications. Activity-tracking bands often sport similar features.

Smartwatches can be used to pay for meals, book hotels or cars, and check the status of flights. Most major airlines already have an Apple Watch app that allows travelers to board by scanning their wrists rather than tickets. Hotel chains are investigating ways to use smartwatches for room keys. And vibrational GPS while traversing an unfamiliar city is invaluable.

2. Smart glasses.

The high-tech eyewear connects to your phone, and headsets such as Samsung’s Gear VR and Mattel’s View-Master VR use smartphone cameras to deliver AR. Several generations of consumers are being introduced to untethered AR experiences, while Google negotiates with retailers and manufacturers to embed its Glass technology into eyewear across the globe.

Travelers will soon be able to use AR to provide interactive maps, travel guides, notifications, and flight updates while they interact with the real world. The technology is still in its infancy, but the smart glass industry will change travel when it reaches full maturity.

3. Wearable cameras.

Wearable cameras are often mentioned in relation to police officers, but tourists could also benefit from this technology. With the small cameras now readily available for a modest price, travelers can get in on the action to document their adventures in innovative ways and share them with friends and family.

In fact, the action camera market already has moved to spherical cameras, with Kodak’s Pixpro SP360 4K camera offering the most compact solution. Using two GoPro-sized SP360s, anyone can capture immersive, 360-degree views of exotic locales from around the world. With social networks pushing for more visual content, capturing and sharing vacation photos will only become easier.

Signs of a Wearable Revolution

Passenger IT Trends Survey found 77 percent of respondents were comfortable with airport staff using wearable technology to help them. That same year, World Travel Market named wearable tech as one of its top trends.

The benefits are clear for travelers: Wearable tech can replace sagging fanny packs and wallets bulging with paperwork. Rather than carrying around credit cards, tickets, receipts, and identification documents, travelers can store and access everything from a watch to glasses to eventually even their own solar- and motion-powered clothing. The technology can help simplify the entire customs process for both passengers and agents.

The devices also should help travel agents respond to increased demand for personalized services. By using the technology to customize holiday packages and enhance communications with clients, wearables could be a boon for the travel industry as a whole.

Smartphones and tablets have fully saturated the market, and interest in technology such as AR and gesture commands is reaching a fever pitch. These technologies are converging for both consumers and enterprises out in the wild as people untether from their desktops and make data-driven decisions on the go.

While the shift likely will have wide-reaching effects throughout society, the travel industry in particular is in line for momentous changes.

By Tony Tie,

tonytie.expediaTony is a numbers-obsessed marketer, life hacker, and public speaker who has helped various Fortune 500 companies grow their online presence.

Located in Toronto, he is currently the senior search marketer at Expedia Canada, the leading travel booking platform for flights, hotels, car rentals, cruises, and local activities.

Trading Routine: How To Track Suspicious Events In Different Locations

Trading Routine: How To Track Suspicious Events In Different Locations

Tracking Suspicious Events

Financial security can be compared to a constant arms race between cyber criminals and businessmen who try to magnify their assets. Trading and financial organizations bear the brunt of the losses occurred due to fraud because their active assets are more liquid and it attracts criminals in all shapes and forms. Security expenditures also turn to be forced losses.

In late 2013 for example, United States entered the age of the mega breach when Target Corp. lost 40 million credit-card numbers to Russian-hackers. And it didn’t stop there; other companies such as Adobe Systems Inc., Home Depot Inc., J.P. Morgan Chase & Co., Anthem Inc. and eBay Inc., fell victim to hackers.

Tense situations like these call for efficient tools for tracking suspicious events. An opportunity to detect and analyze these threats will produce an amplified outcome, i.e. significant revenues for businessmen.

In fact, trading companies generate huge amounts of information. And the main purpose of any corporate security system is to analyze the data and define suspicious events.

How to create an effective system to analyze and monitor corporate information?

Every day, companies are entrusted with the personal and highly confidential information of their customers, therefore creating an effective security policy, which is executed as planned, is extremely important. Experts in custom trading and brokerage solutions emphasize the following security issues that should be taken into account during the elaboration and integration of a security system:

1) Flexible scenarios

It is very well known that swindlers are continuously searching for sophisticated and innovative ways to commit fraud. Since hackers will scan for susceptibilities the minute they are discovered, an organization must have a routine in place for checking its own networks regularly. To address the challenge, we can’t employ universal scenarios; the only thing left is to use some specific methods. A ‘Threats and Alerts’ system should support a flexible parametric structure with individual indicators adjustment, giving the operator a possibility to regulate basic security scenarios and take into consideration all the factors.

2) Analysis algorithms plugged on demand

Trading routine - How to track suspicious events in different locations

Using the same information security tools and analysis algorithms demonstrates different levels of efficiency throughout the course of time. Some of them are up-to-date, others become obsolete. That is why the operator needs an analytic tool base that could be implemented within the context. At the same time, the solution provider should refresh and update the analytic tools base.

3) Online Geoscreening

Upon analyzing hacker attacks and fraudulent operations, specialists in custom e-commerce apps agree that the visualization of information on transactions and financial tools usage is of great importance during the initial stage of detecting suspicious events. Sometimes experts’ intuition and analytic skills prevail over automatic monitoring systems. That is why it’s crucial to provide the operator with well-organized and visualized information.

4) Machine learning algorithms

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Many specialists recommend another double system to track suspicious events. It is based on machine learning algorithms. The efficiency of such a system can be noticed only after a certain period of time when the algorithms already analyzed the needed amount of information. That’s why it is vital to launch this system as an independent sub-program as early as possible to obtain another security tool to address financial frauds.

Conclusion

As no one can predict the nature of a future threat (internal or external) it’s a must for a company to have an individual dynamic platform for analyzing information streams within and outside the institution.

By Yana Yelina

Top 5 Digital Health Trends

Top 5 Digital Health Trends

Digital Health Trends

It is very important to keep up with the changing technology. However, it is also just as important to advance the consumer experience, care delivery methods and create opportunities for career development for the healthcare workforce.

Five trends that are proven to be effective in winning in the digital age have been revealed by the Digital Health Technology Vision 2016 via Accenture.

They are:

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1. Intelligent Automation – Do different things in different ways and create new jobs, products and services in the healthcare industry. Across the health ecosystem, intelligent automation is responsible for making the job of care delivery and administration more seamless. While robots are performing housekeeping duties and the patient intake process is being streamlined by avatars – the trend is not about replacing people but it is about making people do their job more efficiently and work where they are needed the most.

2. The Liquid Workforce – A smoother workforce has been generated by digital technology. For example, when you have a sick child, you can Skype with a pediatrician and take advantage of the Digital service scan. Or during a high-risk pregnancy issue, the virtual technology will enable a doctor in New York to treat a patient in New Mexico.

3. Platform Economy – Platforms can make healthcare experiences more connected by providing underlying technology. The whole healthcare ecosystem – from patients to providers to health plans can be connected by platforms.

4. Predictable disruption – With the advent of digital technology, disruptions are bound to happen any time. Digital technology is changing the way consume everything from products to entertainment. Healthcare isn’t immune to the consumers’ demands of personalized and on-demand services. Digital manufacturers of wearables and other devices are connecting meet consumers’ demands.

5. Digital Trust – To build consumer trust, organizations must figure out a way to efficiently and ethically manage a vast consumer data. If these data can be handled properly, this treasure trove can become a valuable tool for creating customized services and build consumer trust at the same time. In 2014, Apple discovered the importance of consumer trust after the consumer outcry after its iCloud breach. There should be solid policies placed for the governance of ecosystem. Moreover, in order to ensure the right consent and access to information, those policies must be disclosed and understood.

By Glenn Blake

CloudTweaks Comics
Why Cloud Compliance Doesn’t Need To Be So Overly Complicated

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Cloud Infographic – Big Data Predictions By 2023

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Three Factors For Choosing Your Long-term Cloud Strategy

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Cloud Computing Price War Rages On

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Infographic Introduction – Benefits of Cloud Computing

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4 Different Types of Attacks – Understanding the “Insider Threat”

4 Different Types of Attacks – Understanding the “Insider Threat”

Understanding the “Insider Threat”  The revelations that last month’s Sony hack was likely caused by a disgruntled former employee have put a renewed spotlight on the insider threat. The insider threat first received attention after Edward Snowden began to release all sorts of confidential information regarding national security. While many called him a hero, what…

How To Humanize Your Data (And Why You Need To)

How To Humanize Your Data (And Why You Need To)

How To Humanize Your Data The modern enterprise is digital. It relies on accurate and timely data to support the information and process needs of its workforce and its customers. However, data suffers from a likability crisis. It’s as essential to us as oxygen, but because we don’t see it, we take it for granted.…

The Cloud Is Not Enough! Why Businesses Need Hybrid Solutions

The Cloud Is Not Enough! Why Businesses Need Hybrid Solutions

Why Businesses Need Hybrid Solutions Running a cloud server is no longer the novel trend it once was. Now, the cloud is a necessary data tier that allows employees to access vital company data and maintain productivity from anywhere in the world. But it isn’t a perfect system — security and performance issues can quickly…

Three Challenges of Network Deployment in Hyperconverged Infrastructure for Private Cloud

Three Challenges of Network Deployment in Hyperconverged Infrastructure for Private Cloud

Hyperconverged Infrastructure In this article, we’ll explore three challenges that are associated with network deployment in a hyperconverged private cloud environment, and then we’ll consider several methods to overcome those challenges. The Main Challenge: Bring Your Own (Physical) Network Some of the main challenges of deploying a hyperconverged infrastructure software solution in a data center are the diverse physical…

Lavabit, Edward Snowden and the Legal Battle For Privacy

Lavabit, Edward Snowden and the Legal Battle For Privacy

The Legal Battle For Privacy In early June 2013, Edward Snowden made headlines around the world when he leaked information about the National Security Agency (NSA) collecting the phone records of tens of millions of Americans. It was a dramatic story. Snowden flew to Hong Kong and then Russia to avoid deportation to the US,…

How The CFAA Ruling Affects Individuals And Password-Sharing

How The CFAA Ruling Affects Individuals And Password-Sharing

Individuals and Password-Sharing With the 1980s came the explosion of computing. In 1980, the Commodore ushered in the advent of home computing. Time magazine declared 1982 was “The Year of the Computer.” By 1983, there were an estimated 10 million personal computers in the United States alone. As soon as computers became popular, the federal government…

Having Your Cybersecurity And Eating It Too

Having Your Cybersecurity And Eating It Too

The Catch 22 The very same year Marc Andreessen famously said that software was eating the world, the Chief Information Officer of the United States was announcing a major Cloud First goal. That was 2011. Five years later, as both the private and public sectors continue to adopt cloud-based software services, we’re interested in this…

Do Not Rely On Passwords To Protect Your Online Information

Do Not Rely On Passwords To Protect Your Online Information

Password Challenges  Simple passwords are no longer safe to use online. John Barco, vice president of Global Product Marketing at ForgeRock, explains why it’s time the industry embraced more advanced identity-centric solutions that improve the customer experience while also providing stronger security. Since the beginning of logins, consumers have used a simple username and password to…

Adopting A Cohesive GRC Mindset For Cloud Security

Adopting A Cohesive GRC Mindset For Cloud Security

Cloud Security Mindset Businesses are becoming wise to the compelling benefits of cloud computing. When adopting cloud, they need a high level of confidence in how it will be risk-managed and controlled, to preserve the security of their information and integrity of their operations. Cloud implementation is sometimes built up over time in a business,…

Technology Influencer in Chief: 5 Steps to Success for Today’s CMOs

Technology Influencer in Chief: 5 Steps to Success for Today’s CMOs

Success for Today’s CMOs Being a CMO is an exhilarating experience – it’s a lot like running a triathlon and then following it with a base jump. Not only do you play an active role in building a company and brand, but the decisions you make have direct impact on the company’s business outcomes for…