Daily Tech Digest - June 10, 2022

Everything You Need to Know About Enterprise Architecture vs. Project Management

Even though both have their own set of specialized skills, they still correlate in certain areas. Sometimes different teams are working on various initiatives or parts of a landscape. In the middle of the project, they find out that each team needs to work on the same bit of the software or service ... However, to execute such a situation without any mishap needs some coordination and a good system in place to foresee these dependencies. Since it is hard to keep track of all the dependencies and some might come to bite you from the back later. This is where enterprise architecture is needed. Enterprise architects are usually well aware of these relationships and with their expertise in architecture models, they can uncover these dependencies better. Such dependencies are usually unknown to the project or program managers. Therefore, this is where enterprise architect vs. Project management correlates. Enterprise architecture is about managing the coherence of your business whereas project management is responsible for planning and managing usually from the financial and resource perspective.

A Minimum Viable Product Needs a Minimum Viable Architecture

In short, as the team learns more about what the product needs to be, they only build as much of the product and make as few architectural decisions as is absolutely essential to meet the needs they know about now; the product continues to be an MVP, and the architecture continues to be an MVA supporting the MVP. The reason for both of these actions is simple: teams can spend a lot of time and effort implementing features and QARs in products, only to find that customers don’t share their opinion on their value; beliefs in what is valuable are merely assumptions until they are validated by customers. This is where hypotheses and experiments are useful. In simplified terms, a hypothesis is a proposed explanation for some observation that has not yet been proven (or disproven). In the context of requirements, it is a belief that doing something will lead to something else, such as delivering feature X will lead to outcome Y. An experiment is a test that is designed to prove or reject some hypothesis.

In Search of Coding Quality

The major difference between good- and poor-quality coding is maintainability, states Kulbir Raina, Agile and DevOps leader at enterprise advisory firm Capgemini. Therefore, the best direct measurement indicator is operational expense (OPEX). “The lower the OPEX, the better the code,” he says. Other variables that can be used to differentiate code quality are scalability, readability, reusability, extensibility, refactorability, and simplicity. Code quality can also be effectively measured by identifying technical-debt (non-functional requirements) and defects (how well the code aligns to the laid specifications and functional requirements,” Raina says. “Software documentation and continuous testing provide other ways to continuously measure and improve the quality of code using faster feedback loops,” he adds. ... The impact development speed has on quality is a question that's been hotly debated for many years. “It really depends on the context in which your software is running,” Bruhmuller says. Bruhmuller says his organization constantly deploys to production, relying on testing and monitoring to ensure quality.

A chip that can classify nearly 2 billion images per second

While current, consumer-grade image classification technology on a digital chip can perform billions of computations per second, making it fast enough for most applications, more sophisticated image classification such as identifying moving objects, 3D object identification, or classification of microscopic cells in the body, are pushing the computational limits of even the most powerful technology. The current speed limit of these technologies is set by the clock-based schedule of computation steps in a computer processor, where computations occur one after another on a linear schedule. To address this limitation, Penn Engineers have created the first scalable chip that classifies and recognizes images almost instantaneously. Firooz Aflatouni, Associate Professor in Electrical and Systems Engineering, along with postdoctoral fellow Farshid Ashtiani and graduate student Alexander J. Geers, have removed the four main time-consuming culprits in the traditional computer chip: the conversion of optical to electrical signals, the need for converting the input data to binary format, a large memory module, and clock-based computations.

Scrum, Remote Teams, & Success: Five Ways to Have All Three

Agile teams have long made use of team agreements (or team working agreements). These set ground rules for the team, created by the team and enforced by the team. When our working environment shifts as much as it has recently, consider establishing some new team agreements specifically designed to address remote work. Examples? On-camera expectations, team core working hours (especially if you’re spread across multiple time zones) and setting aside focus time during which interruptions are kept to a minimum. ... One of the huge disadvantages of a remote team is the lack of personal connections that are made just grabbing a cup of coffee or standing around the water cooler. Remote teams need to be deliberate about counteracting isolation. Consider taking the first few minutes of a meeting to talk about anything non-work related. Set up a time for a team show-and-tell in which each team member can share something from their home or background in their home office that matters to them. Find excuses for the team to share anything that helps teammates get to know each other more—as human beings, not just co-workers. 

Cisco introduces innovations driving new security cloud strategy

Ushering in the next generation of zero trust, Cisco is building solutions that enable true continuous trusted access by constantly verifying user and device identity, device posture, vulnerabilities, and indicators of compromise. These intelligent checks take place in the background, leaving the user to work without security getting in the way. Cisco is introducing less intrusive methods for risk-based authentication, including the patent-pending Wi-Fi fingerprint as an effective location proxy without compromising user privacy. To evaluate risk after a user logs in, Cisco is building session trust analysis using the open Shared Signals and Events standards to share information between vendors. Cisco unveiled the first integration of this technology with a demo of Cisco Secure Access by Duo and Box. “The threat landscape today is evolving faster than ever before,” said Aaron Levie, CEO and Co-founder of Box. “We are excited to strengthen our relationship with Cisco and deliver customers with a powerful new tool that enables them to act on changes in risk dynamically and in near real-time.

10 key roles for AI success

The domain expert has in-depth knowledge of a particular industry or subject area. This person is an authority in their domain, can judge the quality of available data, and can communicate with the intended business users of an AI project to make sure it has real-world value. These subject matter experts are essential because the technical experts who develop AI systems rarely have expertise in the actual domain the system is being built to benefit, says Max Babych, CEO of software development company SpdLoad. ... When Babych’s company developed a computer-vision system to identify moving objects for autopilots as an alternative to LIDAR, they started the project without a domain expert. Although research proved the system worked, what his company didn’t know was that car brands prefer LIDAR over computer vision because of its proven reliability, and there was no chance they would buy a computer vision–based product. “The key advice I’d like to share is to think about the business model, then attract a domain expert to find out if it is a feasible way to make money in your industry — and only after that try to discuss more technical things,” he says.

Be Proactive! Shift Security Validation Left

When security testing only kicks in at the end of the SDLC, the delays caused in deployment due to uncovered critical security gaps cause rifts between DevOps and SOC teams. Security often gets pushed to the back of the line, and there's not much collaboration when introducing a new tool, or method, such as launching occasional simulated attacks against the CI/CD pipeline. Conversely, once a comprehensive continuous security validation approach is baked in the SDLC, daily invoking attack techniques emulations through the automation built-in XSPM technology identify misconfiguration early in the process, incentivizing close collaboration between DevSecOps and DevOps. With built-in inter-team collaboration across both security and software development lifecycle, working with immediate visibility on security implications, the goal alignment of both teams eliminates erstwhile strife and friction born of internal politics. Shifting extreme left with comprehensive continuous security validation enables you to begin mapping and to understand the investments made in various detection and response technologies and implementing findings to preempt attack techniques across the kill chain and protect real functional requirements.

Unlocking the ‘black box’ of education data

Technology enables education leaders to understand a child’s learning journey in a way that hasn’t been previously possible. Be this through logging the time a child spends on a certain task, recording areas that students consistently do well or poorly in, or by noting hours spent in extra-curricular programmes. Edtech allows the collection and centralisation of data on a child across their years spent in school. This data can then be used to build up a holistic picture of the student’s learning to share with everyone who supports that pupil, from teachers, parents and carers to learning support assistants. They are all able to contribute to the discussion on a pupils areas for focus and improvement. Artificial Intelligence (AI) data analytics can be a valued tool in allowing teachers to visualise and assess the most effective ways of learning in the classroom, the metacognition processes occurring, and intervene if needed to support learning. Beyond the classroom, education leaders and policy makers can aggregate data to develop strategies and policies. 

How to Retain Talent in Uncertain Circumstances

“There was confusion and uncertainty, which led to a willingness for those professionals in those organizations to listen to the opportunities we had,” Sasson says. “There was no visibility whatsoever, which created an environment where they were more open to hearing what else was out there.” In some cases a company may be planning downsizing after a merger, and they may be allowing that uncertainty to linger because they want some employees to voluntarily find new jobs, Sasson says. However, in other cases organizations may want to retain their valuable talent, particularly in this tight job market. Just because there’s a merger or acquisition doesn’t necessarily mean that everyone will make a stampede to the door. ... Sasson’s team asked the employees at Proofpoint why they weren’t interested in new opportunities. “From what we understand, the CEO at Proofpoint and the Thoma Bravo team -- they seemed to do an excellent job of communicating the value of the acquisition and limiting the jitters that would typically be felt by the rank and file,” Sasson said.

Quote for the day:

"A leader should demonstrate his thoughts and opinions through his actions, not through his words." -- Jack Weatherford

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