Dennis Monner, chief commercial officer at Aryaka, says he thinks what IT leaders are finding is that the talent that they really need on their teams is in short supply. “The boundaries between the traditional, functional disciplines are getting fuzzy, requiring a new breed of security professional,” he explains. “The cloud team needs to understand the network. The network team needs to understand security. It’s driving them to rethink their investment and hiring strategy.” He adds recruiting, training, and retention all takes real dollars from the budget that could potentially be deployed in services that guarantee performance. “You can only outsource security to a certain degree,” Haff cautions. “Even if you're 100% in a public cloud, you're still largely responsible for your own application security, as well as your internal access and authentication procedures.” While a cloud provider can implement all manner of security tech and processes if you don't control who has access, those won't do much good. “It was somewhat disappointing that, although our survey generally showed investments in people was a high priority, ‘hiring security or compliance staff’ was one of the lowest security funding priorities,” he adds.
Even the most reluctant individuals are likely to have succumbed to contactless payments and some form of digitised banking in recent times. This will have the positive impact of making the needed transition to biometrics more seamless. Using fingerprints or facial recognition to unlock phones or access apps is not unusual. If anything, they have been convenient and comforting additions to the surge of tech innovations over the last couple of decades. There is a relief in knowing that these portals are being secured by methods that are almost impossible to replicate. It is a breakthrough that financial players and governments in the world’s most developed countries still need to catch up with, as emerging economies have already capitalised on biometrics’ capabilities for almost a decade now. In India, for example, internal fraud and leakage from pension payments dropped by 47% after transitioning from cash to biometric smart cards. Because the solution bypasses the need for prior credit ratings or credentials, the country has also been able to catalyse safe online banking among previously unbanked adults since biometrics’ introduction in 2014.
Done right, DeFi offers traditional banks and financial services firms the ability to reduce costs, increase speed and attract new customers who are looking for simplified, more attractive, and secure solutions. When we look at the current payments ecosystem, we’re confronted with a maze of payments services, systems and rules which rely on a cacophony of different players. DeFi offers a solution to this inherent friction, delivering ecosystems than can run autonomously based on rules and verify transactions without human intervention. The main attractions of this innovation are two-fold. Firstly, it reduces inefficiency while eliminating fees, manual effort (e.g. for corporate actions) and intermediaries. Basic transactions can be executed at any time, from any place, with the only requirement having an internet connection and a compliant wallet. By removing the middleman in asset rights transfers, lowering exchange fees, and giving access to wider global markets, moving securities on blockchain could save between $17B and $24B in global trade processing costs.
The essence of a CI/CD system is to aim for green builds and to resolve issues quickly when a red build occurs, meaning a test failed. When the automated tests run, any failure results should be visible to all team members. Then, it should be a top priority for the team to make the build work again. Green builds and rapid fixes are critical for two reasons. First, when tests are failing, it is not possible to test forthcoming development and changes accurately. Secondly, continuous deployment will be halted, because no new and validated packages exist. Although it may seem like a frustrating situation to stop active development and instead focus on fixing failed tests, this mindset will ensure optimal application stability. An efficient CI/CD system should be the only path that leads to the production environment. In other words, if you have confidently built a CI/CD system with a comprehensive set of tests, there should be no other way to deploy applications to the production system. It can be highly tempting — and common — to maintain administrator privileges and deploy an application to the production systems just once.
The concept of blue-green deployment is to have (at least) two instances of an application running at one time. When a new version is released, it can be released to just one (or some) instances, leaving the others running on the old version. Access to this new version can be restricted completely at first, then potentially released to a subset of consumers, until confidence in the new release is achieved. At this point, access to the instance(s) running the old version can be gradually restricted and then these too can be upgraded. This creates a release with zero downtime for users. There are, of course, caveats. Any breaking change to data sources or APIs means that old requests cannot be processed by the new version, which rules out a blue-green release. It’s one of my favourite interview questions to ask how one might approach a breaking change in a blue-green environment on the off-chance that someone comes up with a great solution, but it would probably involve some bespoke routing layer to enrich or adapt "old" requests to the "new" system. At which point, you’d have to consider whether it isn’t better just to have some good old downtime.
The role of AI and data science in innovation and automation will increase in 2023. Data ecosystems are able to scale, decrease waste, and provide timely data to a variety of inputs. But laying the foundation for change and fostering innovation is crucial. With the use of AI, software development processes can be optimised, and further advantages include greater collaboration and a larger body of knowledge. We need to foster a data-driven culture and go past the experimental stages in order to change to a sustainable delivery model. This will undoubtedly be a significant advancement in AI. ... Over the past few years, IT systems have become more sophisticated. Vendors will seek platform solutions that offer visibility across numerous monitoring domains, including application, infrastructure, and networking, according to a new Forrester prediction. ... The automatic modification of neural net topologies and improved tools for data labelling are two promising areas of automated machine learning. When the selection and improvement of a neural network model are automated, the cost and time to market for new solutions for artificial intelligence (AI) will be reduced.
Increasing competition could leave gaps for European challengers to enter. The EU, however, has historically struggled to turn its world-leading research into big tech companies. One barrier is the notoriously slow and inefficient transfer of IP from academia to the economy. This problem is illustrated by the EU producing more research papers than the US, but turning far fewer into commercial applications. According to Luigi Congedo, a venture capitalist and Innovation Advisor at marketing firm Clarity, this weakness can be reduced by changing the EU’s investment framework. This, he argues, could stimulate a more effective technology transfer — and prevent promising startups from being acquired by Silicon Valley giants. “We need to create our Google, Facebook, and Microsoft, and, in order to do it, create a better environment to compete and do business across the continent,” he said. “If we fail in creating a real European platform for innovation and instead maintain the current ‘country-based model,’ all our emerging businesses will end up becoming M&A targets for American multinational companies.”
Thompson believes these failures are often owing to misaligned incentives: “Those who correctly estimate significant tail risks [i.e., deviations from the normal distribution in a statistical model] may not be recognized or rewarded for doing so. Before the event, tail risks are unknown anyway if they can only be estimated from past data,” and “after the event, there are other things to worry about.” In short, it was in investors’ interest to design a model that characterized unlikely risks as infinitesimally so, and regulators weren’t paying attention. So why should we bother with models at all? Occasionally, Thompson believes, they do get it right. Her preferred example concerns research by two chemists, F. Sherwood Rowland and Mario Molina, who in the 1970s modeled the potential impact on the ozone layer of the continued release of chlorofluorocarbons, or CFCs. Within 15 years of their research, an international agreement, the Montreal Protocol, had been signed to limit CFC use, and it is now possible that the ozone layer could recover to its 1980 level by 2050. “The acceptability of the model was a function of the relatively simple context and the low costs of action,” Thompson explains
With remote working on the rise – despite some companies attempting to go back to the office – global hiring will continue to increase. More and more people will be able to work in digital jobs that can be done from anywhere. “When you hire internationally, you have access to a much larger talent pool, and with the possibility of hiring employees to work from anywhere in the world, companies will have a unique opportunity of filling their roles in a more diverse way to increase cross-cultural competency in remote teamwork,” says Kelvin Ong, chief of staff at online software engineering school Microverse. However, Ong agrees with James Wilknson, that this means IT managers will have to develop their soft skills, such as explicit and clear written communication (“low-context communicaiton” and sending messages where there is a timelag before you get a response (“asynchrous communication”). ... Hedley says: “Most recessions are mild and temporary. While they are not fun, recessions can be endured. Second, business owners can, to a large extent, control their own destiny. And that’s especially true when it comes to identifying and hiring the talent that will move the needle.”
Authenticity is important in creating high-performing teams because it lays the groundwork for strong relationships and environments in which employees can bring their whole, best selves to work. Being authentic doesn’t mean bearing all your darkest secrets, but it does mean understanding your own personal style and drivers and helping your team understand those. Humans are wired for consistency, so when you show up consistently and authentically, your employees know what to expect, how to approach you, and what’s important. Better still, they feel they have space to share who they are and what drives them. ... Perhaps the most important tip, though, is to be present when you are with your team. Shifting to all virtual work over the last couple of years has taken a toll on our ability to focus in the moment. We are constantly typing emails while listening to conference calls or responding to chats and texts while also trying to write articles or create solutions for clients. The pressure to multitask is great, but the benefits of focus and attention are even greater.
Quote for the day:
"Be so good at what you do that no one else in the world can do what you do." -- Robin Sharma