IN 2016, according to Cisco, an American technology group, the volume of data flowing through the internet each month passed a zettabyte, enough to fill some 16bn 64GB iPhones. By 2025 it will be many times greater. Immeasurably more data sit outside the public internet on company servers. Most of these data are valuable information, which means that people are keen to trade it. Typically, data deals are at present worked out between someone holding the information and those who want to extract insights from it. For instance, Uber has deals allowing many cities to access data generated by its fleet of drivers. This helps city planners understand traffic flows. ... These new data markets face stiff challenges. Maintaining individual privacy and monitoring questions to prevent corporate leaks will be difficult. The cryptography securing the network needs to be airtight. Perhaps the biggest challenge will be convincing people to use them.
Non-Functional Attributes (NFAs) always exist though their signficance and priority differs when considered with certain other functional or non-functional attribute. It’s particularly important to pay attention and consider them in the inital phase of the EA framework development, as these attributes may have direct or indirect impact on some of the functional attribute of the framework. Considering Non Functional attributes early in the lifecycle is important because NFAs tend to be cross-cutting, and because they tend to drive important aspects of your architecture, they do cause considerable impact on certain important aspects of your test strategy. For example, security requirements will drive the need to support security testing, performance requirements will drive the need for stress and load testing, and so on. These testing needs in turn may drive aspects of your test environments and your testing tool choices.
In 2018, discussions about big data infrastructure no longer revolve around methods to reduce network traffic through the use of clever data placement algorithms; instead, there are now more discussions about how to reduce the cost of reliable, distributed storage. The Hadoop open-source community has brought this discussion to the forefront with the recent introduction of Apache Hadoop version 3.0. One of the key features of Hadoop 3 is Erasure Coding for the Hadoop Distributed File System (HDFS), as an alternative to the venerable HDFS 3x data replication. Under typical configurations, Erasure Coding reduces HDFS storage cost by ~50% compared with the traditional 3x data replication. Over the past few years, the Hadoop community has discussed the potential storage cost savings that Erasure Coding will bring to HDFS; and many have questioned whether 3x data replication still makes sense, given the advancements in hardware and networks over the last ten years.
New technology creates a shift. The inventions of the industrial era led to the decline of blacksmiths, but it did give rise to the steelworker. AI, similarly, has the potential to create such opportunities. The CRO of Business Insider Peter Spande was reported by CNBC as saying that, this year nearly US$ 2 billion has been spent on AI advertising alone. Technology research and analysis company Gartner estimates that by the year 2020 AI field will have created 2.3 million jobs. About this estimate, Svetlana Sicular of Gartner says, “unfortunately, most calamitous warnings of job losses confuse AI with automation — that overshadows the greatest AI benefit — AI augmentation — a combination of human and artificial intelligence, where both complement each other”. There will, in fact, be job losses to the tune of around 1.8 million — mainly mid and low-level positions — but new ones will be created in the highly skilled, management and even entry level. Gartner estimates that by 2022, one in five workers who conduct non-routine tasks will depend on AI to do their job.
A report from Deloitte titled Dark Analytics: Illuminating Opportunities Hidden Within Unstructured Data Tech Trends 2017 discusses how some medical facilities could use dark data to take more all-encompassing approaches to patient care. For example, during consultations, doctors may take handwritten notes and capture voice recordings, plus make notes in emails or cloud-based applications. Collecting it all and making it accessible could improve treatments and insights, reducing the instances of incorrect diagnoses or interventions that don’t work as well as more appropriate options. People in the health care field are also hopeful that dark data could make it possible to analyze population groups. The previously unused data could potentially make predictions about future needs and illness trends that could ultimately affect individuals’ interactions with health professionals and help local health departments understand the situations their staff members will most likely encounter.
While tech giants tend to hog the limelight on the cutting-edge of technology, AI in banking and other financial sectors is showing signs of interest and adoption – even among the stodgy banking incumbents. Discussions in the media around the emergence of AI in the banking industry range from the topic of automation and its potential to cut countless jobs to startup acquisitions. ... Through facts and quotes from company executives, this article serves to present a concise look at the implementation of AI by the seven leading commercial banks in the U.S. as ranked by the Federal Reserve. Changes in the banking industry directly impact businesses and commerce, and we sought to provide relevant insights for business leaders and professionals interested in the convergence of AI and financial technology. We’ll explore the applications of each bank one-by-one. The top seven US banks below have been rank-ordered by their size, starting with JPMorgan Chase, the largest.
Financial innovation can be interpreted as common developments happening gradually in the financial services industry. This includes contemporary markets, technologies, instruments, or institutions. Fintech refers to a distinct area of financial innovation where the centre of interest is transformative technology. Fintech is short for financial technology. A great example of fintech is a P2P lending platform called Zopa, which gives people access to loans directly from connected devices. On the other hand, financial innovations may sound like the same thing, however they are different devices and institutions which enable people to use financial services. Existing examples of financial innovations include debit cards, ATMs and traditional banking services. ... Traditional banks have been around for centuries. Fintech is bringing banking into the modern age, however it now threatens to outgrow banking completely. They could become superior to banks because they are able to curate big data and offer flexibility in managing money in ways that banks would need to be redesigned from the ground up to match.
Robo-advisers started out by going after people with limited disposable income and little experience with investing. These companies are able to offer wealth management tools at low fees because they rely mostly on automated software, instead of people, to deliver advice. Customers fill out a risk profile by answering questions about their age and goals to receive a customized portfolio made up of exchange-traded funds and other passive investments. Competition has upped the urgency for these startups to add new products. Charles Schwab Corp., Morgan Stanley and Vanguard Group Inc. have all introduced robo-advisers in recent years and have billions of assets under management already. Betterment and Wealthfront have said they each have more than $10 billion in assets. “The industry has come a long way over the past several years, but success has also attracted new competition, including large and established players across the financial services industry,” said Devin Ryan, an analyst at JMP Securities LLC. “Increasing competition within the digital wealth management space appears to be accelerating the pace of innovation.”
Cerner’s EDH helps them understand the most significant risks and opportunities for improvement across a population of people. Cerner computes quality scores for managing a number of chronic conditions, and analysts can see which conditions could gain the most by improving those scores. For instance, Cerner can accurately determine the probability that a person has a bloodstream infection, such as sepsis. Sepsis is an uncontrolled inflammatory response to an infection. It is a complex condition which is difficult for a junior doctor or nurse to recognise. From the time sepsis first takes hold, healthcare professionals have only the initial 6 hours after the diagnosis to deliver a group of interventions to the patient. These interventions require close and rapid interaction between teams in the Emergency Department, in the general ward and in Critical Care. For an individual patient, getting the interventions right at the right time may mean a 20-30% better chance of surviving.
“Agile development can easily stall without agile leadership in place,” says Berthelsen. “Here at Fourth, we don’t lead from the top down – we lead from the bottom up. The leadership team inspires all members of staff to become agents for change. Everyone in the organisation has the personal responsibility, accountability and authority to deliver on our clients’ requirements. Once we’ve agreed on an action, we agree as a business. That open style of leadership makes the life of the product owner and the software engineering teams so much easier, as they’re not in the middle of a conflict of priorities.” As the CTO at Fourth, Berthelsen is also best placed to unpick what the characteristics are of a successful agile leader. “It’s all about openness and collaboration. Agile leaders set the vision and give individuals the freedom to follow that vision. They create an environment for people to push decision making authority down to those closest to the information and they remove barriers so individuals can respond in real-time to unfolding situations.” ... Every stakeholder is trained on what agile is, why we’re doing it, and they participate in it every day.
Quote for the day:
"Leadership appears to be the art of getting others to want to do something you are convinced should be done." -- Vance Packard