Showing posts with label business analysis. Show all posts
Showing posts with label business analysis. Show all posts

Daily Tech Digest - August 21, 2021

Can AGI take the next step toward genuine intelligence?

To take the next step on the road to genuine intelligence, AGI needs to create its underpinnings by emulating the capabilities of a three-year-old. Take a look at how a three-year-old playing with blocks learns. Using multiple senses and interaction with objects over time, the child learns that blocks are solid and can’t move through each other, that if the blocks are stacked too high they will fall over, that round blocks roll and square blocks don’t, and so on. A three-year-old, of course, has an advantage over AI in that he or she learns everything in the context of everything else. Today’s AI has no context. Images of blocks are just different arrangements of pixels. Neither image-based AI (think facial recognition) nor word-based AI (like Alexa) has the context of a “thing” like the child’s block which exists in reality, is more-or-less permanent, and is susceptible to basic laws of physics. This kind of low-level logic and common sense in the human brain is not completely understood but human intelligence develops within the context of human goals, emotions, and instincts. Humanlike goals and instincts would not form the best basis for AGI.


How to take advantage of Android 12’s new privacy options

First and foremost in the Android 12 privacy lineup is Google’s shiny new Privacy Dashboard. It’s essentially a streamlined command center that lets you see how different apps are accessing data on your device so you can clamp down on that access as needed. ... Next on the Android 12 privacy list is a feature you’ll occasionally see on your screen but whose message might not always be obvious. Whenever an app is accessing your phone’s camera or microphone — even if only in the background — Android 12 will place an indicator in the upper-right corner of your screen to alert you. When the indicator first appears, it shows an icon that corresponds with the exact manner of access. But that icon remains visible only for a second or so, after which point the indicator changes to a tiny green dot. So how can you know what’s being accessed and which app is responsible? The secret is in the swipe down: Anytime you see a green dot in the corner of your screen, swipe down once from the top of the display. The dot will expand back to that full icon, and you can then tap it to see exactly what’s involved.


Achieving Harmonious Orchestration with Microservices

The interdependency of your microservices-based architecture also complicates logging and makes log aggregation a vital part of a successful approach. Sarah Wells, the technical director at the Financial Times, has overseen her team’s migration of more than 150 microservices to Kubernetes. Ahead of this project, while creating an effective log aggregation system, Wells cited the need for selectively choosing metrics and named attributes that identify the event, along with all the surrounding occurrences happening as part of it. Correlating related services ensures that a system is designed to flag genuinely meaningful issues as they happen. In her recent talk at QCon, she also notes the importance of understanding rate limits when constructing your log aggregation. As she pointed out, when it comes to logs, you often don’t know if you’ve lost a record of something important until it’s too late. A great approach is to implement a process that turns any situation into a request. For instance, the next time your team finds itself looking for a piece of information it deems useful, don’t just fulfill the request, log it with your next team’s process review to see whether you can expand your reporting metrics.


How Ready Are You for a Ransomware Attack?

Setting the bar high enough to protect against initial entry is a laudable goal, but also adheres to the law of diminishing returns. This means the focus must shift towards improving how difficult it is for an attacker to move around your environment once they have gotten inside. This phase of the attack often requires some manual control, so identifying and disrupting command and control (C2) channels can pay significant dividends – but realize that only the least sophisticated attacker will reuse the same domains and IPs of a previous attack. So rather than looking for C2 communications via threat intel feeds, your approach needs to be to look for patterns of behavior which look like remote-access trojans (RATs) or hidden tunnels (suspicious forms of beaconing). Barriers to privilege escalation and lateral movement come down to cyber-hygiene related to patching (are there easily accessible exploits for local privilege escalation?), rights management (are accounts granted overly generous privileges?) and network segmentation (is it easy to traverse the network?). Most of the current raft of ransomware attacks have utilized the serial compromise of credentials to move from the initial point-of-entry to more useful parts of the network.


The rise and fall of merit

Wooldridge identifies Plato’s Republic as the origin of the concept of meritocracy, in which the Athenian philosopher imagined a society run by an intellectual elite, “who have the ability to think more deeply, see more clearly and rule more justly than anyone else.” Crucially, Plato’s ruling class was remade each generation—aristocrats were not assumed to pass on their talents—and it prized women as highly as men. Wooldridge finds meritocratic leanings in other pre-modern societies, including China, which began in the fifth century to use exams to recruit civil servants. But it was the expansion of the state in Europe in the early modern period that saw meritocracy first take root, albeit in a paradoxical way. As states expanded, demand for capable bureaucrats outgrew the ability of the aristocracy to produce them. The solution was to look downward and offer patronage to talented lowborns. Men such as French dramatist Jean Racine; London diarist Samuel Pepys; economist Adam Smith; and Henry VIII’s right-hand man, Thomas Cromwell, were all plucked from obscurity by favoritism. 


Intel Advances Architecture for Data Center, HPC-AI and Client Computing

This x86 core is not only the highest performing CPU core Intel has ever built, but it also delivers a step function in CPU architecture performance that will drive the next decade of compute. It was designed as a wider, deeper and smarter architecture to expose more parallelism, increase execution parallelism, reduce latency and increase general purpose performance. It also helps support large data and large code footprint applications. Performance-core provides a Geomean improvement of about 19%, across a wide range of workloads over our current 11th Gen Intel® Core™ architecture (Cypress Cove core) at the same frequency. Targeted for data center processors and for the evolving trends in machine learning, Performance-core brings dedicated hardware, including Intel's new Advanced Matrix Extensions (AMX), to perform matrix multiplication operations for an order of magnitude performance – a nearly 8x increase in artificial intelligence acceleration.1 This is architected for software ease of use, leveraging the x86 programing model.


A Soft, Wearable Brain–Machine Interface

Being both flexible and soft, the EEG scalp can be worn over hair and requires no gels or pastes to keep in place. The improved signal recording is largely down to the micro-needle electrodes, invisible to the naked eye, which penetrate the outermost layer of the skin. "You won't feel anything because [they are] too small to be detected by nerves," says Woon-Hong Yeo of the Georgia Institute of Technology. In conventional EEG set-ups, he adds, any motion like blinking or teeth grinding by the wearer causes signal degradation. "But once you make it ultra-light, thin, like our device, then you can minimize all of those motion issues." The team used machine learning to analyze and classify the neural signals received by the system and identify when the wearer was imagining motor activity. That, says Yeo, is the essential component of a BMI, to distinguish between different types of inputs. "Typically, people use machine learning or deep learning… We used convolutional neural networks." This type of deep learning is typically used in computer vision tasks such as pattern recognition or facial recognition, and "not exclusively for brain signals," Yeo adds. 


How to proactively defend against Mozi IoT botnet

While the botnet itself is not new, Microsoft’s IoT security researchers recently discovered that Mozi has evolved to achieve persistence on network gateways manufactured by Netgear, Huawei, and ZTE. It does this using clever persistence techniques that are specifically adapted to each gateway’s particular architecture. Network gateways are a particularly juicy target for adversaries because they are ideal as initial access points to corporate networks. Adversaries can search the internet for vulnerable devices via scanning tools like Shodan, infect them, perform reconnaissance, and then move laterally to compromise higher value targets—including information systems and critical industrial control system (ICS) devices in the operational technology (OT) networks. By infecting routers, they can perform man-in-the-middle (MITM) attacks—via HTTP hijacking and DNS spoofing—to compromise endpoints and deploy ransomware or cause safety incidents in OT facilities. In the diagram below we show just one example of how the vulnerabilities and newly discovered persistence techniques could be used together.


CBAP certification: A high-profile credential for business analysts

CBAP is the most advanced of IIBA’s core sequence of credentials for business analysts. It follows the Entry Certificate in Business Analysis (ECBA) and the Certification for Competency in Business Analysis (CCBA). As you might expect, the requirements get more extensive as you climb the ladder: CBAP requires more training, work experience, and knowledge area expertise. AdaptiveUS, a company that offers training for all of IIBA’s certs, breaks down the various requirements, but the important thing to know is that CBAP holders are at the top of the heap; while you don’t need to have the lower-level certs to get your CBAP certification, you should be fairly well established in your career as a BA before you consider it. Like IIBA’s other certs, the CBAP draws from A Guide to the Business Analysis Body of Knowledge, also known as the BABOK Guide. The BABOK Guide is a publication from IIBA that aims to serve as a bible for the business analysis industry, collecting best practices from real-world practitioners. It was first published in 2005 and is continuously updated. 


A Short Introduction to Apache Iceberg

Partitioning reduces the query response time in Apache Hive as data is stored in horizontal slices. In Hive partitioning, partitions are explicit and appear as a column and must be given partition values. Due to this approach, Hive having several issues like not being able to validate partition values is so fully dependent on the writer to produce the correct value, 100% dependent on the user to write queries correctly, Working queries are tightly coupled with the table’s partitioning scheme, so partitioning configuration cannot be changed without breaking queries, etc. Apache Iceberg introduces the concept of hidden partitioning where the reading of unnecessary partitions can be avoided automatically. Data consumers that fire the queries don’t need to know how the table is partitioned and add extra filters to their queries. Iceberg partition layouts can evolve as needed. Iceberg can hide partitioning because it does not require user-maintained partition columns. Iceberg produces partition values by taking a column value and optionally transforming it.



Quote for the day:

"Be willing to make decisions. That's the most important quality in a good leader." -- General George S. Patton, Jr.

Daily Tech Digest - November 14, 2020

Data Scientist vs Business Analyst. Here’s the Difference.

Perhaps the biggest similarity of Business Analyst to Data Scientist is the words itself to describe the role. A Data Scientist is expected to perform business analytics in their role as it is essentially what dictates their Data Science goals. A Business Analyst can expect to focus not on Machine Learning algorithms to solve business problems, but instead on surfacing anomalies, shifts and trends, and key points of interest for a business. ... Of course, there are some key differences between these two roles. One of the biggest differences is the use of Machine Learning for Data Scientists only. Another difference is that a Business Analyst can expect to communicate more to stakeholders than a Data Scientist would (sometimes Data Scientist work can be more heads down and not involve as many meetings). Here is a summary of the differences you can expect to find between these positions. ... These two roles share goals with one another. Each requires a deep dive into data with similar tools as well. The process of communication is similar, too — working with stakeholders from the company to go over the business problem, solution, results, and impact. Here is a summary of the key similarities between a Data Scientist and a Business Analyst.


CISA Director Expects to Be Fired Following Secure Election

US officials delivered a statement emphasizing the security of this year's election as news of these firings began to unfold. Members of the Election Infrastructure Government Coordinating Council (GCC) Executive Committee and the Election Infrastructure Sector Coordinating Council (SCC) say this election "was the most secure in American history." Across the country, they add, officials are reviewing the election process, and states with close calls will recount ballots. "This is an added benefit for security and resilience," they wrote. "This process allows for the identification and correction of any mistakes or errors. There is no evidence that any voting system deleted or lost votes, changed votes, or was in any way compromised." Security measures included pre-election testing, state certification of voting equipment, and the US Election Assistance Commission's (EAC) certification of voting equipment contribute to confidence in voting systems used in 2020, they said. Officials acknowledged the "many unfounded claims and opportunities for misinformation" about the election process and emphasize they have the "utmost confidence" in the election's security and integrity.


Security Awareness: Preventing Another Dark Web Horror Story

Our research from last year has already revealed that 1 in 4 people would be willing to pay to get their private information taken down from the dark web – and this number jumps to 50% for those who have experienced a hack. While only 13% have been able to confirm whether a company with which they’ve interacted has been involved in a breach, the reality is it’s much more likely than you’d think – since 2013, over 9.7 billion data records have been lost or stolen, and this number is only rising. Most of us would have no way of knowing whether our information is up for sale online. However, solutions now exist which proactively check for email addresses, usernames and other exposed credentials against third-party databases, alerting users should any leaked information be found. ...  Detection is undoubtedly pivotal in keeping ahead of fraudsters, but the foundations begin with awareness. The majority of breaches take place as a result of simple mistakes which can be easily addressed – using your Facebook password at work or failing to change the default settings of connected devices. But at the same time, businesses must stress the importance of being cyber-aware and foster a culture of security awareness throughout the organisation.


14 Finance Specialists Share Their Largest Fintech Predictions For 2021

There can be extra “bank in a box” tech layers between fintech and banks to allow spinning up partnerships on a sooner timeline. I additionally see extra back-end firms to automate important compliance capabilities akin to Know Your Buyer and regulatory change administration. I additionally assume we are going to see much more “regular” firms providing monetary providers in addition to growing consolidation amongst fintech firms. – Jeanette Fast... An enormous development that might be seen is a renewed want for monetary literacy. Covid-19 compelled everybody to consider each their long- and short-term monetary outlooks. What now we have seen within the auto refinancing sector is that individuals don’t even know you possibly can refinance a car. You’ll discover customers who need to sharpen their funds and firms that can be making an attempt to achieve and educate them. – Tom Holgate, ... The rise of insurance coverage tech will revolutionize the medical insurance trade, with improvements starting from digital well being information to monitoring health. The rise of good contracts offers insurance coverage firms a solution to replace their infrastructure and minimize long-term prices whereas offering shoppers with superior service. – Joseph Safina


How to Keep Up With Big Tech's Hiring Spree

If you’re realizing you need more tech skills to handle the new digital demands of your industry, look first at your existing workforce. Instead of spending time and money on hiring, look for ways to upskill employees interested in a more technical career path and have demonstrated an aptitude for learning. For example, someone in an administrative role who has quickly adapted to remote work might be a good candidate for a scrum master or project management role. If you don’t have the ability to train employees in-house, consider a partnership. ... Hiring, in general, is starting to pick up again. When the pandemic finally subsides and companies begin hiring in full force, most will be looking for talent in the same places. Instead of sourcing recent college grads, look for graduates from coding boot camps and other alternative skilling programs, or target self-taught learners. This crisis has demonstrated that online learning isn’t just possible; it’s a critical part of today’s young people’s development. The talent acquisition team at IBM has made a point to target so-called “new collar” workers to bolster its 360,000-employee workforce. The company has developed a robust learning program for people both inside and outside of the company interested in learning new technical skills.


Digital Robber Barons and Digital Vertical Integration

These Robber Barons leveraged vertical integration to create “economic moats” that locked out and blocked potential competitors. The term “economic moat”, popularized by Warren Buffett, refers to a business' ability to maintain competitive advantages in order to protect its long-term profits and market share from competing firms while charging monopoly-like prices to its customers and onerous terms to its suppliers. Just like a medieval castle, the moat serves to protect the riches of those inside the castle from outsiders. Andrew Carnegie is an example of a Robber Baron who used vertical integration to create economic moats for Carnegie Steel. Carnegie Steel (later U.S. Steel) became the dominant steel supplier in the U.S. through the vertical integration of the steel value chain process. Carnegie owned not only the steel mills that produced the different grades and types of steel, but Carnegie also owned the iron ore mines that was the main ingredient in steel production, coke/coal mines that powered the blast furnaces from which steel was produced, and the railroads and shipping that transported the iron ore and coke to the steel mills and the finished steel products to its customers


Building a secure hybrid cloud

If all your computing assets are stored in a single location which then experiences an extended power outage, phone service or internet outage, natural disaster, or terrorist attack, your business essentially grinds to a halt. Many larger organizations invest in constructing and maintaining multiple data centers for just that reason. For most small businesses, this added cost is beyond their capabilities. Cloud technology removes this challenge by placing the business continuity requirement entirely on the provider. Along the same lines of business continuity, is that because of its ubiquity, cloud provides businesses with a competitive advantage over companies that still rely on legacy on-premises hardware-based solutions. Case in point: I recently worked with a company who had one of their location’s phone lines go down. It took 3 days for 2 different phone companies to figure out whose fault it was and then finally fix the problem. During those 3 days, a busy office was completely down with no phone service whatsoever. This kind of service level might have been acceptable in 1992. However, in the 2020s that’s beyond unacceptable. A cloud communications provider with a guaranteed service-level agreement would have ensured that such a serious outage would never happen.


Testing in Production 101

To start, deploy your first feature to production with the default rule off for safety. This ensures that only the targeted users will have access to the feature. Next, run your automation scripts in production with targeted test users, as well as the regression suite to guarantee previously released features are not affected by your changes. With the feature flag off and only your targeted team members having access to the feature, you will officially be testing in production. This is the time to resolve any bugs and validate all proper functionality. It’s important to remember that because end users do not yet have access to your feature, they will not be impacted if anything does go wrong. After you’ve resolved the issues that appeared in your first test and you’re confident the feature will work properly, it’s time to use a canary release to open up the feature to 1% of your user base. The next days will be spent monitoring error logs and growing your confidence in the feature until you feel it’s appropriate to increase the percentage of users that can access your feature. Once you reach 100% of users and you know without a doubt that the feature works, it’s time to turn on the default rule for the feature.


Digital Twins: Bridging the Physical and Digital World

In short, a digital twin is the precise replica of the physical world preserved through updates on a real-time basis. It is used in virtual reality and 3D data and graphics to create virtual buildings and other models of product, service, system, process, and so on. According to the SAP Senior Vice President of IoT Thomas Kaiser, he says that this is “becoming a business imperative, covering the entire lifecycle of an asset or process and forming the foundation for connected products and services.” ... The concept of a digital twin has been around since 2002 but was shadowed by IoT. However, it has made a resurgence and, in 2017, it was part of Gartner’s Top 10 Strategic Technology Trends. It has made the system cost-effective to implement and become imperative in today’s business, combining virtual and physical worlds to enable analyses of data and monitoring systems. It also helps forestall a problem before it occurs, avoid interruption, advance new opportunities, and plan for the future with simulations. Digital twins enable real-world data for creating simulations for predicting the production process. It incorporates IoT Industry 4.0, Artificial Intelligence (AI), and software analytics to augment a better result.


Self-Service Security for Developers Is the DevSecOps Brass Ring

The ability for organizations to fold self-service security functionality into these internal platforms tends to be highly correlated to the degree to which security integration has been achieved across the software delivery life cycle. The survey asked respondents to pick which of the five phases of the life cycle where security is integrated: requirements, design, building, testing, and deployment. It found the ratio of organizations with two or more phases integrated has gone up from 63% last year to 70% this year. The ratio of organizations with complete integration now stands at 12%. As the report explains, the self-service offering of security and compliance validation is intertwined with the push for greater integration. Meanwhile, among those with three to four phases of development integrated with security, 42% offer self-service security and compliance validation. And 58% those that have achieved full security integration across all five phases say they provide self-service security. Companies that have fully integrated security are more than twice as likely to offer self-service security as firms with no security integration.



Quote for the day:

"When I finally got a management position, I found out how hard it is to lead and manage people." -- Guy Kawasaki

Daily Tech Digest - December 23, 2018

Blockchain Data Network
Some critics have been quick to disparage real efforts to create digital voting with strictly theoretical worries. In reality, the rollout in West Virginia is a very focused solution to a specific issue: low overseas voter participation. The current system is broken. A blockchain-driven digital voting app is a clear solution. Anyone but critics of progress should eagerly support West Virginia’s efforts until there is an actual reason to worry. Once any blockchain application is embraced in sufficient numbers by both the using and accepting sides, the impressive software will become an invaluable and ubiquitous tool. More widespread adoption of blockchain’s most beneficial use cases will trigger network effects that will multiply the benefits. Let’s remember that we are in the early days of blockchain. Many industry observers seem to be in a rush to declare blockchain a mainstream technology. As enthusiastic as I am in my support of blockchain, I would not yet call it mainstream. The interconnectedness of the world means its adoption will probably take root and bloom quickly.


Data Analyst and Business Analyst- A contrast

A business analyst is required to have expertise in the industry in which they function. A business analyst working for a finance company must be good with numbers and understand calculations for a payback period and internal rate of return as both are needed for the calculation of ROI( return on investment). They use various tools to analyse and manipulate data. They should also possess excellent communication skills so that they can easily convey the technical data messages to the clients in a way that is understandable to even those who might lack technical knowledge. ... Data analysts are required to possess sharp technical knowledge coupled with excellent industry knowledge. They act like security guards of the company keeping the data safe and also possess a strong and thorough understanding of the relationships that the organisation’s databases hold. They use complex query statements and technologically advanced database tools to extract information from these databases.


Banking with APIs 101


Communication over the phone is no longer necessary thanks to open banking and APIs (Application Programming Interfaces), pieces of software allowing seamless interaction between clients and banks. Not only retail and corporate clients, but an entire ecosystem of internal stakeholders, software suppliers, brokers, asset managers, fintechs, etc. may now benefit from business models shaped around open banking and alternative ways of generating revenues. But what are APIs essentially for? APIs enable communication and data exchange between clients (data requesters) and servers (data holders) in a secure and consistent manner. Applications and data being unbundled in modern architectures, the bank is now requested to share data under open banking regulations. In other words, the most valuable asset the bank possesses, has to be openly and securely shared. APIs can fulfil these needs in the most effective manner. Banks do not of course need to expose all sorts of data, only to provide access to the specific information needed or required. 


Deep automation in machine learning

Automation doesn’t stop when the model is “finished”; in any real-world application, the model can never be considered “finished.” Any model’s performance will degrade over time: situations change, people change, products change, and the model may even play a role in driving that change. We expect to see new tools for automating model testing, either alerting developers when a model needs to be re-trained or starting the training process automatically. And we need to go even further: beyond simple issues of model accuracy, we need to test for fairness and ethics. Those tests can’t be automated completely, but tools can be developed to help domain experts and data scientists detect problems of fairness. For example, such a tool might generate an alert when it detects a potential problem, like a significantly higher loan rejection rate from a protected group; it might also provide tools to help a human expert analyze the problem and make a correction.


Artificial Intelligence - Leading The Silent Revolution in HealthCare


The AI on the CherryHome device can monitor whether an elderly goes into the bathroom and does not return, if they fall, or if their gait is abnormal. To protect the patient’s privacy, CherryHome turns them into a virtual skeleton and sends caregivers and family members real-time notifications of such anomalies. Also, all video footage is processed on-device—not sent to the cloud, as is the case with most home assistants. Already in place is a pilot partnership between CherryHome, TheraCare, in-home caregiving service and TriCura, a tech ecosystem for care agencies. This represents another differentiator for AI, according to Goncharov. A lot of scientists in the AI space are working on fundamental problems—elderly care being just one of them. Looking forward, Goncharov says that AI will be further propelled as machine learning can be done with less and less data. The biggest hurdle to broader applications right now, he says, is the immense amount of data required to teach machines anything—another way that CherryHome is leading the way.


Transforming a Traditional Bank into an Agile Market Leader

In order to fix the environment, you basically boil it down to two big things. You’ve got to create an environment where you teach people and you give people the ability to get their hands dirty, learning by doing. Experimenting. And the second big thing is the fear of risk. In the professional environment, risk is extraordinarily high. At home, worst case is we get frustrated because some app didn’t work. At the bank, people could lose their jobs, they could lose their bonus. So if you figure out a way to learn by doing and make it OK to fail, then it’s OK to take risks. So how do you get this culture change and become like a startup? You have a central team that creates a culture of experimentation, which gives people an opportunity to work with other people [in a risk-free environment]. I was really surprised that in the first couple of years [of our change in mind-set] we started getting really huge traction. And we made it happen in every part of the company, including human resources, marketing and communications, everywhere.


Not all clouds are the same

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There are different architectures on the cloud security market, some more readily equipped than others to ease the transition away from hardware. An advantage of containerised cloud architecture is streamlined migration to the cloud without sacrificing your network architecture or security posture. Some less sophisticated solutions may compromise on critical capabilities provided by legacy appliances. Consider, for instance, your company’s IP presence and how important it is to operations: an IP address associated with your organisation is used to identify your users to third-party vendors for whitelisting, and for preventing non-authorised users from accessing SAML authentication. Your traffic’s all-important IP identity is lost, however, when traversing typical shared-proxy security architectures. Think too of GDPR - cloud solutions that don’t offer a strong data centre presence, or the controls to keep data in the right place, can be little more than a liability.


Building a VPC with CloudFormation

This article describes how you can use AWS CloudFormation to create and manage a Virtual Private Cloud (VPC), complete with subnets, NATting, route tables, etc. The emphasis is use of CloudFormation and Infrastructure as Code to build and manage resources in AWS, less about the issues of VPC design. You may be wondering why we would use CloudFormation to build our VPC when we can create one via the VPC wizard in the management console.  CloudFormation allows us to create a "stack" of "resources" in one step. Resources are the things we create (EC2 Instances, VPCs, subnets, etc.), a set of these is called a stack. We can write a template that can easily stand up a network stack exactly as we like it in one step. This is faster, repeatable, and more consistent than manually creating our network via the management console or CLI. We can check our template into source control and use it any time we like for any purpose we want.


European Banks Are Pushing the Adoption of Blockchain Technology

European Banks Are Pushing the Adoption of Blockchain Technology
Led by Italy-based Associazione Bancaria Italiana, 14 banks, including BNP Paribas, contributed two months of data to a Corda-based blockchain network. The original press release, delivered in Italian, mentions the establishment of the first phase as a "basis for subsequent synergistic implementations of DLT technologies," which also includes a form of smart contracts that will regulate the transfer of data. With ABI Labs at the helm overseeing a million test transactions between the banks involved, reports show that the performances were satisfactory, which will allow the process to move forward to the next phase. This cooperation between European banks comes on the heels of a project led by the Polish bank PKO Bank Polski, in partnership with the tech company Coinfirm, that will see blockchain technology utilized to notify customers about changes to product terms. The project, titled Trudatum, was described as a "breakthrough on a global scale" by Pawel Kuskowski, President of Coinfirm. All those success stories inevitably attracted the attention of the European Union.


Machine Learning Explainability vs Interpretability

In the context of machine learning and artificial intelligence, explainability and interpretability are often used interchangeably. While they are very closely related, it’s worth unpicking the differences, if only to see how complicated things can get once you start digging deeper into machine learning systems. Interpretability is about the extent to which a cause and effect can be observed within a system. Or, to put it another way, it is the extent to which you are able to predict what is going to happen, given a change in input or algorithmic parameters. It’s being able to look at an algorithm and go yep, I can see what’s happening here. Explainability, meanwhile, is the extent to which the internal mechanics of a machine or deep learning system can be explained in human terms. It’s easy to miss the subtle difference with interpretability, but consider it like this: interpretability is about being able to discern the mechanics without necessarily knowing why. Explainability is being able to quite literally explain what is happening.



Quote for the day:


"Don't focus so much on who is following you, that you forget to lead." -- E'yen A. Gardner


Daily Tech Digest - May 18, 2018

businessman bridges gap
“Elicitation of requirements and using those requirements to get IT onboard and understand what the client really wants, that’s one of the biggest responsibilities for BAs. They have to work as a product owner, even though the business is the product owner,” Gregory says. “[They need to ask:] What do the systems need to do, how do they do it, who do we need to get input from, and how do we get everyone to agree on what we need to do before we go and do it? The BA’s life revolves around defining requirements and prioritizing requirements and getting feedback and approval on requirements,” says Jeffrey Hammond, vice president and principal analyst at Forrester Research. The role of a business analyst is constantly evolving and changing – especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction or siloed large organizations.



Why AI is the perfect software testing assistant

Software testers are highly analytical, creative problem solvers. To identify hidden defects and areas where users might get frustrated, they must ask what others haven't asked and see what others don't see. But the analytical process takes time, and it isn't always as efficient as today's businesses and the users of their software demand. Artificial intelligence (AI), and its ability to search data sets for golden nuggets, could really come in handy here. An AI tool could quickly locate tests that have already been written to cover a particular scenario or new line of code. The system could even tell testers which test cases are most appropriate for the requirement. Over time, an AI tool could even pinpoint what might be causing the bugs that those tests find, based on past data. When combined with testers' wealth of knowledge about the product and its users, AI has the potential to significantly increase testing efficiency. ... We are beginning to see a few AI-enhanced testing tools hit the market now; initial capabilities include highlighting areas of risk that need further testing or that weren't covered at all. There will be many more advanced tools released in the coming months and years.


Blockchain technology lacks enough use cases to be disruptive, says Worldpay


A lack of strong use cases for blockchain is preventing the technology from disrupting the financial services industry, according to Worldpay. The payment company’s head of technology operations, Jason Scott-Taggart, said the organisation had not ruled out using blockchain in future, but the technology still has some way to go. “You’d be surprised, but in payments blockchain is not as disruptive as people assume it is. There’s not a lot of demand for cryptocurrencies, and blockchain as a technology is not something we have seen a good application for in what we do yet,” he told Computer Weekly in an interview at the ServiceNow Knowledge 18 conference. His view echoes research from Gartner, which found just 1% of CIOs are currently undertaking blockchain projects and 8% plan to start one in the short term. The analyst firm’s vice-president, David Furlonger, said the technology was “massively hyped” and warned “rushing into blockchain deployments could lead organisations to significant problems of failed innovation, wasted investment [and] rash decisions”.


Improve the rapid application development model for deployment readiness

An increasing number of enterprises adapt rapid application development tools rather than reworking their DevOps toolchain. Kubernetes, Marathon and other container orchestration platforms easily combine with continuous integration tools such as Jenkins to make every stage of rapid development, from unit testing through production, part of an explicit flow. The move from idea to prototype is defined in rapid development terms, using rapid development tools. Jenkins, Buildbot, CruiseControl and similar tools frame production as a stage of rapid or continuous development. At each stage, they link to container orchestration for deployment. Simply hosting application code in containers does not guarantee that the orchestration practices for each stage will be comparable, but it does organize the process overall. Containers, and a single orchestration tool, provide commonality across all stages of rapid application development to ensure that every stage is tested, including the transition to production.. The rapid application development model, in both setups, is a string of testing and integration phases linked together.


Adware bundle makes Chrome invisible to launch cryptojacking attacks

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Known as cryptojacking, this practice involves the use of often-legitimate mining scripts which are deployed on browsers without user consent, before funneling the proceeds to mining pools controlled by threat actors. According to the publication, the bundle creates a Windows autorun which launches the Google Chrome browser -- in a way which is invisible. By using specific code to launch the browser, the software forces Chrome to launch in an invisible, headless state. The browser then connects to a mining page whenever the user logs into Windows. This page launches the CoinCube mining script that steals processing power to mine Monero. CPU usage may spike to up to 80 percent, and while victims may notice their PCs are slow, it could be a very long time before the software is uncovered and removed -- or users may simply blame Chrome as the oddity. The researcher opened the website page responsible for the script in a standard browser window and came across an interesting element of the script; the page masquerades as a Cloudflare anti-DDoS page.


Telegrab: Russian malware hijacks Telegram sessions

Cisco Talos researchers Vitor Ventura and Azim Khodjibaev dubbed the malware Telegrab. They analyzed two versions of it. The first one, discovered on April 4, 2018, only stole browser credentials, cookies, and all text files it can find on the system. The second one, spotted less than a week later, is also capable of collecting Telegram’s desktop cache and key files and login information for the Steam website. To steal Telegram cache and key files, the malware is not taking advantage of software flaws. The malware is capable of targeting only the desktop version of the popular messenger because it does not support Secret Chats and does not have the auto-logout feature active by default. This means that the attacker can use those stolen files to access the victim’s Telegram session (if the session is open), contacts and previous chats. Telegrab is distributed via a variety of downloaders, and it checks if the victim’s IP address is part of a list that includes Chinese and Russian IP addresses, along with those of anonymity services in other countries. If it is, it will exit.


Blockchain will be the killer app for supply chain management in 2018

blockchain maersk ibm
Private or "permissioned" blockchains can be created within a company's four walls or between trusted partners and centrally administered while retaining control over who has access to information on the network. Blockchain can also be used between business partners, such as a cloud vendor, a financial services provider and its clients. Bill Fearnley, Jr., research director for IDC's Worldwide Blockchain Strategies, recently returned from visiting company clients in China where he found "everybody wanted to talk about supply chain. "If you build a blockchain ledger within [a single company] that has a certain value," Fearnley said. "The real value for blockchain is when you use distributed electronic ledgers and data to connect with suppliers, customers and intermediaries." One major challenge with supply chain management today involves trade finance record keeping, because a lot of trade finance record keeping is still based on inefficient systems: including faxes, spreadsheets, emails, phone calls and paper.


Zara concept store greets shoppers with robots and holograms

At Zara’s new flagship store in London, shoppers can swipe garments along a floor-to-ceiling mirror to see a hologram-style image of what they’d look like as part of a full outfit. Robot arms get garments into shoppers’ hands at online-order collection points. iPad-wielding assistants also help customers in the store order their sizes online, so they can pick them up later. “Customers don’t differentiate between ordering online or in a store,” spokesman Jesus Echevarria Hernandez said. “You need to facilitate that as best as you can.” The store, which opened Thursday, shows how retailers are increasingly blending online and bricks-and-mortar shopping in a bid to keep up with the might of Amazon.com Inc. Inditex SA, the Spanish company that owns Zara, calls it an example of the technologies it will implement around the world. ... Amazon is moving the other way, building out its physical retail presence. Not only has it acquired grocer Whole Foods Market Inc., it has opened Amazon Go convenience stores, which use artificial intelligence and video cameras in lieu of checkouts, in several U.S. cities.


Icinga Enterprise-Grade Open-Source Network Monitoring That Scales

analytics network monitoring
Icinga runs on most of the popular Linux distros and the vendor provides detailed installation instructions for Ubuntu, Debian, Red Hat (including CentOS and Fedora) and SUSE/SLES. Icinga does not publish specific hardware requirements, but our installation ran well on a quad-core processor with 4 GB RAM and this is probably be a good starting point for a basic installation. ... As with most monitoring applications, storage is an important variable that largely depends on the number of hosts and services monitored and how often information is written to the log. With too little storage, the logs can easily fill up and freeze the system. We were able to quickly install Icinga on Ubuntu 16.04 LTS with just a few simple commands at the prompt. The first step was to download the necessary files to the local repository, and then install the actual Icinga application. Icinga can be used to monitor the availability of hosts and services from switches and routers as well as a variety of network services like HTTP, SMTP and SSH.


CISO soft skills in demand as position evolves into leadership role

You need to be able to understand what engineering is trying to do and what their goals are, what marketing and procurement are doing, what the customer is trying to do and what their goals are. If you can't empathize with what their goals and challenges are, you can't influence. So much flows from that: Your communication skills and communication style will flow from empathy. You also need to be understanding of what we call the data subject -- the consumer who doesn't understand what's happening to their data -- and having empathy for them, as well as empathy with all the stakeholders. It's empathizing with everybody and making the wisest decision to push for the best outcome you can. ... It's important for at least two different reasons. One, from a practical perspective, I've talked a lot about the skills gap. If we're blocking 50% of the planet from joining this career path, we're really contributing to our biggest challenge. Then the other part: Women across the globe are economically oppressed, and information security is a lucrative field. I want to get women into the information security field so they can be financially independent and make a good living.



Quote for the day:


"Leadership - leadership is about taking responsibility, not making excuses." -- Mitt Romney


Daily Tech Digest - March 10, 2018

Why You Should View Linux as a Core IT Skill

Linux as a Core IT Skill
Twenty-five years ago, some fellow students and I were sitting in a computer lab at the University of Waterloo trying to compile a new open-source UNIX operating system called Linux on a PC. Back then, installing a Linux system was about as difficult as nailing Jell-O to a tree, but we managed to get a system installed after only four days of work. Linux has come a long way since then. Today, Linux is the most diverse and aggressively developed operating system in the world, primarily due to its open-source nature. And if you work in an IT field, you’ve probably been exposed to more Linux in the last few years than before. In fact, the Gartner research company identified Linux as the fastest-growing operating system segment in the computing industry in 2017. So, what does this mean for you as an IT professional? It means that you’ll likely be working with far more Linux systems and technologies in coming years, regardless of whether you currently work with them or not.



Cisco attacks SD-WAN with software from Viptela, Meraki acquisitions

Cisco attacks SD-WAN with software from Viptela, Meraki acquisitions
The SD-WAN is typically made of diverse networks and technologies that many times are outside the control of IT. Add to that the increased use of multi-cloud services and other advances, and the traditional complexity of the WAN has been increased, Cisco stated. Cisco cited a recent IDC study that found almost three out of 10 organizations considered network outages to be a top WAN concern, with the same number stating they need better visibility and analytics to manage application and WAN performance. IDC also estimates that worldwide SD-WAN infrastructure and services revenues will hit $8.05 billion by 2021. In order to address some of these challenges, Cisco rolled out SD-WAN vAnalytics, a cloud-based SaaS application that will collect data from the SD-WAN and let customers spot and fix communications problems quicker, gauge application performance, oversee bandwidth planning, and predict how policy changes might impact the network. 


Big data analytics: The cloud-fueled shift now under way

Big data analytics: The cloud-fueled shift now under way
Cloud-based big-data silo convergence is speeding enterprise time-to-value. Users are beginning to step up the pace of consolidation of their siloed big data assets into public clouds. The growing dominance of public cloud providers is collapsing the cross-business silos that have heretofore afflicted enterprises’ private big data architectures. Just as important, big data solutions, both cloud-based and on-premises, are converging into integrated offerings designed to reduce complexity and accelerate time to value. More solution providers are providing standardized APIs for simplifying access, accelerating development, and enabling more comprehensive administration throughout their big data solution stacks. Innovative big data startups are bringing increasingly sophisticated AI-infused applications to market. Innovative application providers are starting to disrupt the big data competitive landscape with AI-based solutions.


Why Startup CEOs Still Have to Make Sales Calls

For all the obvious reasons. (1) People don't really care how much you know until they know how much you care. Showing up shows them that you actually do care. (2) Startups are notoriously scattered and in a hurry. Focus and attention to detail are scarce commodities and the customers want to know that you personally are connected, paying attention, and directly engaged with their business, their concerns and their problems. And finally, (3) they want to hear it from the horse's mouth. Not second hand. They want commitments and assurances from you (since they know that the sales guys will tell them anything and promise them the world) that you will stand up for and stand behind your product or service and make good on whatever they've been promised. The buck always stops with you. None of this is very tough. You just have to say what you're going to do and do what you said you would and everything will be hunky-dory.


What is a virtual CISO? When and how to hire one

multiple-exposure image showing virtual connections and software inside and outside a human profile
Why would you need a vCISO when you could simply hire a real one on a permanent contract? The answer is varied and not necessarily the same for everyone. For starters, well-rated, full-time CISOs can be hard to come by, often stay in their job for two years or less, and critically, especially for smaller businesses, can command six-figure salaries. In contrast, vCISOs are estimated to cost between 30 percent and 40 percent of a full-time CISO and are available on-demand. The benefits go well beyond cost. Virtual CISOs usually require no training, can hit the ground running, and don’t feel obliged to play nice with office politics. In this model, it’s purely about results, and vCISOs worth their salt will provide reasonable KPIs and reporting. While different vCISOs offer different skillsets, many should be able to cover myriad tasks, from the tactical to strategic. They could help pull together security policies, guidelines and standards. That could entail anything from coming to grips with HIPAA or PCI compliance, to staying on top of vendor risk assessment. 


Josh Bersin on the Importance of Talent Management in the Modern Workplace

Bersin reminds us that, even though the top, hot job of the moment may be technical, there are are plenty of non-technical jobs that are growing in demand, too. “Soft skills are just as in demand as hard skills. There will be an increased need for social, integrative, and hybrid skills. Empathy, communication, speaking, judgement… these renaissance skills are the jobs of the future,” said Josh. “Even the job of data scientist now requires persuasion, interpretation, not just looking at data.” Although many worry that technology will render some workers obsolete, this appears to be far from the case. Many of these workers can easily transition into new roles that leverage their skills, and these new roles are good for the workers, too. In fact, 96% of all transitions have “good-fit” options and 65% of transitions will increase wages.


Machine learning: What developers and business analysts need to know

Machine learning: What developers and business analysts need to know
In the case of supervised learning, you train a model to make predictions by passing it examples with known inputs and outputs. Once the model has seen enough examples, it can predict a probable output from similar inputs. ... The results of the prediction can’t be better than the quality of the data used for training. A data scientist will often withhold some of the data from the training and use it to test the accuracy of the predictions. With unsupervised learning, you want an algorithm to find patterns in the data and you don’t have examples to give it. In the case of clustering, the algorithm would categorize the data into groups. For example, if you are running a marketing campaign, a clustering algorithm could find groups of customers that need different marketing messages and discover specialized groups you may not have known about. In the case of association, you want the algorithm to find rules that describe the data.


Software leaders pick these three technologies as top investments

Companies that have been slower to invest in technology solutions have either prioritized changing their business model or have felt the negative, if not fatal, repercussions of not doing so. Regardless of industry, staying ahead of the technological curve in today’s software-centric world is a must for business success. However, it can be difficult for even the most experienced IT leaders to wade through the long list of technology buzzwords and solutions that promise to be the “next best things.” So how can businesses cut through the noise to determine what will actually bring business value? They can start by determining the technologies the experts are actually pursuing. To find out what these tech trends are, O’Reilly analyzed search data from more than two million users on its online learning platform, most of which are trained software and technology leaders. By taking into consideration what these professionals are focusing on, other professionals can begin to determine what their companies should be investing time and money in.


RoboTiCan is building low-cost industrial robots for the masses

​RoboTiCan products, with CEO Halgai Balshai
Balshai said, "We have moving, navigation, a manipulation of an arm, computer vision. Everything combined in one platform. Basically to be able to master all this knowledge and be able to find the algorithm for making it work is really complex. With ROS, it gives us a lot of opportunity to combine algorithms from one point to another. For example, if something was developed in a Carnegie Mellon University in the United States and we want to use this particular system, image work, or cognition of an object that was developed in Carnegie Mellon, we can extract this information and extract these ideas and implement it in our robot real easily. "By that, we don't need to have a really huge company to be able to do a lot of different tasks with one robot. This is basically the idea and the advantage of using ROS and open source architects for how we use robotics. By doing something that is generic for everybody, you can use it all over the globe. Of course, there is stuff that we extract to others. ..."


Data Mining What Why

Data mining sits at the intersection of statistics (analysis of numerical data) and artificial intelligence / machine learning (Software and systems that perceive and learn like humans based on algorithms) and databases. Translating these into technical skills leads to requiring competency in Python, R, and SQL among others. In my opinion, a successful data miner should also have a business context/knowledge and other so called soft skills (team, business acumen, communication etc.) in addition to the above mentioned technical skills. Why? Remember that data mining is a tool with the sole purpose of achieving a business objective (increase revenues / reduce costs) by accelerating the predictive capabilities. A pure technical skill will not accomplish that objective without some business context. The following article from KDNuggets proves my point that data mining job advertisements mentioned the following terms very frequently: team skills, business acumen, analytics among others.



Quote for the day:


"Vulnerability is the birthplace of innovation, creativity and change." -- Brené Brown


Daily Tech Digest - November 27, 2016

Stretching Agile in Offshore Development

Stretching Agile is not easy, especially when factors like distributed development and teams with different culture come in place. In my opinion, how you communicate with people is fundamental if there is a need to create awareness and self responsibility. Another advice is to put robust working agreements in place, created and reviewed with involved parties, not enforced or imposed, so everybody will understand their benefits. ... First I would say it is primordial to understand the culture and how other people behave, react and talk. Second, it is important to have frequent face to face meetings and trips between sites. After getting to know people we tend to work together in a more collaborative way, that’s how humans behave. 


Not That Bright: Japanese Robot Fails Top-Ranked University Exam

It turns out the robot is not good at grasping "meaning in a broad spectrum," said Noriko Arai, a professor at the National Institute of Informatics, who heads the team behind Torobo-kun. Torobu-kun, for instance, did not perform well in English, where it had to link phrases to come to logical conclusions. It received scores of 36.2 in listening and 50.5 in written exams. "As the robot scored about the same as last year, we were able to gauge the possibilities and limits of artificial intelligence," she said. Torobu-kun received scores of 45.1, 47.3 and 57.8 from 2013 to 2015, according to the Asahi Shimbun. This year, the score was lower than last year. However, the machine showed progress in some areas, such as physics and world history.


What makes an awesome business analyst?

Awesome Business Analysts must learn how to operate well in the fog of projects. There’s always going to be ambiguity at the start of projects but it’s also the business analyst’s job to assist with removing ambiguity. Often ambiguity is hidden in project assumptions. Start by capturing an exhaustive list of all of the assumptions you’ve heard, both explicit and implicit, then attack them aggressively by doing what you can to clarify, validate and remove them. Project managers will be particularly happy if these investigations help to provide more clarity on the scope of the project. From the beginning of a project, it’s important to try to gain consensus around what success might look like. That way, when lost in the fog, there is a compass bearing that everyone knows so that they can course correct throughout the project to ensure that everyone is moving in the right direction.


Artificial Intelligence (AI) and Biotechnology: Striking a “Balance of Power”

Nations must not look upon AI as a novelty or an economic asset, but also as a central component to national security. Nations depending on Facebook and Google to prop up their IT infrastructure rather than brewing their own national alternatives are akin to nations during the age of empires inviting British gunboats into their harbors. Artificial intelligence, biotechnology, and other forms of emerging technology must be viewed by each nation, state, community, and individual not as a mere novelty or potential industry, but also as a potential means to grant those who develop and monopolize it economic, political, and even military superiority history has taught us they most certainly will abuse.


Is big data really the future of marketing?

Capturing all the data you need is great but what you do with it is far more important. Making your data work hard is not restricted to delivering campaigns. You now have the ability to extract that data to feed other elements of marketing; feed behavioural information to or from your mobile app, your email service provider or your corporate CRM in real time. By bringing these different elements together you will be one step closer to a single customer view, the Holy Grail for marketers. It would seem that the future for the marketer will continue to change and at a pace too. The challenge to make sense of the data you capture becomes increasingly difficult on relational databases as volumes and variety increase.


How machine learning can make humans better managers

Machine learning can help humans become better managers by removing any biases a manager might have. With machine learning, employee performance is backed up by raw, inarguable data that shows how employees are actually performing. By taking advantage of this rich repository of data, managers can better recognize which employees are achieving important goals. In turn, they can provide appropriate feedback without relying on their personal opinions. With its ability to eliminate bias and prompt a data-driven approach to feedback and recognition from managers, machine learning can completely transform the workplace by making coming to work an engaging experience for every employee — no matter their age, race or gender. Employees shouldn’t have to worry about the personal biases of their managers.


10 predictions for the Internet of Things and big data in 2017

“Test/dev and disaster recovery will be the main components of a company’s environment that will be moved to the cloud, and production continuing to remain on premises,” says Marc Clark, director of cloud strategy and deployment at Teradata. ... Deep learning is getting massive buzz recently. Unfortunately, many people are once again making the mistake of thinking that is a magic, cure-all bullet for all things analytics, according to Bill Franks, chief analytics officer at Teradata. “The fact is that deep learning is amazingly powerful for some areas such as image recognition,” says Franks. “However, that doesn’t mean it can apply everywhere. While deep learning will be in place at a large number of companies in the coming year, the market will start to recognise where it really makes sense and where it does not.”


Visibility into the DevOps Value Chain

The culture of the DevOps community seven to ten years ago was very motivated toward open source. Open source tools are almost, by definition, point solutions. I think a lot of the automation solutions, even the commercial automation solutions have been designed to solve a very specific or narrow problem. So there are tools that solve the deployment problem. There are tools that solve the configuration problem. There are tools that solve testing problems. And so on…There is no such thing as a standard DevOps tool chain. They’re like snowflakes. So developers gravitate toward their tool of choice and the DevOps culture encourages experimentation. Enterprises haven’t bought into the giant, does everything kind of tool. Instead enterprises are choosing very specific point solutions and then weaving them altogether to generate efficiencies across the value stream.


Underpinning Enterprise Data Governance with Machine Intelligence

One of the more valuable benefits of strengthening enterprise data governance with machine intelligence capabilities is an expeditious efficiency that is otherwise difficult to match. Semantic technologies allow for machine-readable data which can accelerate most processes involving those data, decreasing time spent on data modeling and other facets of data preparation. “The ability for data to be discoverable and linkable through an adoption of identifiers in a consistent way allows that data to move and to be reached more rapidly,” Hodgson said. “Whether you get the data into a machine learning environment is another matter. But at least you’re insured of its integrity, and that’s a big issue as well.”


How Business and IT Can Find Middle Ground for a Data Governance Framework

There’s great value in establishing a liaison and mediator between business and IT team leads. This helps business teams work with IT to maintain information protection, governance, and data quality while also working with business representatives to create value from data assets faster. The governance protocol then moves down the ladder to all aspects of the business where data is involved. Each business unit needs a representative to make sure that their team is up-to-speed on the process for inputting and drawing data and trained with the technology that enables them to do so. Data governance is not just about technology. It’s about key stakeholders and employees creating processes and best practices to properly organize, validate, and derive business value from their own information.



Quote for the day:


"The task of leadership is not to put greatness into humanity, but to elicit it, for the greatness is already there." -- John Buchan