Generating vast quantities of data, organisations need to be aware of the level of data management required in order to successfully deliver a digital transformation in procurement. “Not understanding the data implications may result in budget overruns, overtime, or scope reduction in data management. Data is a key input for many processes and decisions in modern organisations, and underestimating its relevance can cause an inability to meet goals related to supplier enablement or PO automation due to capacity and scope constraints,” said Zycus. When it comes to the quality of data, process digitalisation is a key driver. Process digitalisation reduces human error; generates greater business insights, improves decision-making capabilities, and increase value creation. ... “In recent years, Procurement departments have become more prone to cyberattacks in the form of malware via a software update, attacks on cloud services, ransomware, business email compromise, attack on supply chain, etc.,” commented Zycus. Such threats can result in a loss of sensitive data and/or financial losses.
It is worth noting that the principle of coordinated working hours in offices grew out of working patterns in factories at a time when the technology for business was mainly an in-person exercise. Yet, as everyone who has been through the pandemic knows, knowledge workers no longer work that way ‚ we’re asynchronous, remote, and international. In many senses, this change in expectations is no change at all. Knowledge work has always been marked by a sense of asynchronicity. People meet, talk, agree, and then go off and work in small groups or alone. What has changed is that 65% of workers now have, and expect, more flexibility to decide when they work. ... Perhaps one of the most boringly predictable challenges remote workers face involves the tools they’re asked to use. On average, workers have 6.2 apps sending them notifications at work, and 73% of them respond to those outside of working hours, further eroding the division between (asynchronous) work time and personal time. ... A worker may find that they do their work at times that suit them best, but still feel pressurized to pretend to be present the rest of the time, too.
The metaverse has already become a playground for luxury fashion brands, with some launching their new collection in the virtual world and others partnering up with developers to create their own bespoke games. In the near future, we anticipate more brands to follow and break the boundaries between virtual and physical reality to create more innovative, meaningful interactions with consumers. We are in the very early days here and our team will be working on many different pilots and experiments. There are several use-cases for Web 3.0 in e-commerce. For example, brands looking to connect with loyal users and fans can provide additional value by way of gated commerce enabled through NFTs. At the same time, brands and artists can use NFTs to build and monetize communities. We can create immersive shopping experiences in the Virtual Worlds/Metaverse, an ever-expanding world of real-time, with the help of virtual spaces in 3D. We can also enable e-commerce landscapes based on the Blockchain that will allow anyone to trade physical products on-chain.
SaaS providers are unlikely to send infrastructure- and application-level security event logs to customers’ security information and event management (SIEM) solutions, leaving customers’ security operations teams lacking in terms of important information. This diminishes the ability to identify and manage potential security incidents. For example, it can be difficult to know whether and when a brute-force password replay attack is perpetrated against a SaaS customer user account. Such attacks could lead to undetected data breaches, resulting in the organization being considered liable for the data leak and for not reporting the incident to the appropriate parties (e.g., employees, customers, authorities) in a timely manner. ... It can be challenging for customers to understand the fundamental nature of a SaaS provider’s risk culture. Audits, certifications, questionnaires, and other materials paint a narrow picture of the providers’ security posture. Moreover, SaaS providers are unlikely to share their risk register with customers, as this would reveal excessive details about the SaaS provider’s security posture. Further, SaaS providers are unlikely to undergo detailed customer audits due to limited resources.
Supervised learning requires large amounts of labeled data. Labeling and annotation must be done manually by human experts, so it is laborious and expensive. Semi-supervised learning is a technique where both labeled and unlabeled data are used to train the model. Usually, the number of labeled data points is significantly less than the unlabeled data points. Semi-supervised learning exploits patterns and trends in data for classification. Semi-supervised learning is a technique where both labeled and unlabeled data are used to train the model. Usually, the number of labeled data points is significantly less than the unlabeled data points. Semi-supervised learning exploits patterns and trends in data for classification. S-GANs tackle the requirement for vast amounts of training data by generating data points using generative models. The generative adversarial network (GAN) is an architecture that uses large, unlabeled datasets to train an image generator model via an image discriminator model. GANs comprise two models: generative and discriminative.
The desirable end state - easier said than done - is to embrace an adaptive cybersecurity posture, supported by people, process and technology - that is more responsive to the dynamism of today's cybersecurity landscape. As research firm Ecosystm notes, "anticipating threats before they happen and responding instantly when attacks occur is critical to modern cybersecurity postures. It is equally important to be able to rapidly adapt to changing regulations. Companies need to move towards a position where monitoring is continuous, and postures can adapt, based on risks to the business and regulatory requirements. This approach requires security controls to automatically sense, detect, react, and respond to access requests, authentication needs, and outside and inside threats, and meet regulatory requirements." Adaptation is also likely in future to involve artificial intelligence. A golden example of applying AI adaptively for cybersecurity would be to be able to detect the presence of code, packages or dependencies that are impacted by zero-days or other vulnerabilities, and to block those threats.
One problem that comes up when implementing microservices is that the communication with front-end apps gets more complex. Now we have many servers responsible for different things, which means front-end apps would need to keep track of that info to know who to make requests to. Normally this problem gets solved by implementing an intermediary layer between the front-end apps and the microservices. This layer will receive all the front-end requests, redirect them to the corresponding microservice, receive the microservice response, and then redirect the response to the corresponding front-end app. The benefit of the BFF pattern is that we get the benefits of the microservices architecture, without over complicating the communication with front-end apps. ... Horizontally scaling on the other hand, means setting up more servers to perform the same task. Instead of having a single server responsible for streaming we'll now have three. Then the requests performed by the clients will be balanced between those three servers so that all handle an acceptable load.
Nowadays, a lot of it is people who are like, “Oh, my god, I feel like deep learning is starting to destroy expertise in my industry. People are doing stuff with a bit of deep learning that I can’t even conceive of, and I don’t want to miss out.” Some people are looking a bit further ahead, and they’re more, like, “Well, nobody is really using deep learning in my industry, but I can’t imagine it’s the one industry that’s not going to be affected, so I want to be the first.” Some people definitely have an idea for a company that they want to build. The other thing we get a lot of is companies sending a bunch of their research or engineering teams to do the course just because they feel like this is a corporate capability that they ought to have. And it’s particularly helpful with the online APIs that are out there now that people can play around with — Codex or DALL-E or whatever — and get a sense of, “Oh, this is a bit like something I do in my job, but it’s a bit different if I could tweak it in these ways.” However, these models also have the unfortunate side effect, maybe, of increasing the tendency of people to feel like AI innovation is only for big companies, and that it’s outside of their capabilities.
"Underneath virtual first is a number of tenets that define how we think about the future of work. One of those is ‘asynchronous by default,' the idea being that if we're going to have people working remotely, that shouldn't mean they spend eight hours a day on video calls. Instead, at Dropbox, you're measured on your output and the impact that you make, rather than how many meetings you can sit in. "That then led us to think about how much time we should be spending in meetings, and as a result, we rolled out something called ‘core collaboration hours’ where employees reserve four hours each day to be available for meetings. That means there’s times when you're open to meet with your team or anyone else in the company, but also that you've got those other four hours in the day to focus on the work that you need to do. "Does that mean you wouldn't flex that to meet with somebody who's in a different time zone or something else? Absolutely not. It's your time to manage as an individual, because we're measuring you on the impact and output that you're making.
Much before metaverse became popular, VR games like Minecraft and Roblox had captivated scores of young gamers. The immersive gaming experience delivered by AR/VR and the rapid growth of devices powered by AR/VR and XR has further accelerated the growth of metaverse to the current level. Meanwhile, the growth of high-speed Internet has acted as the catalyst driving this transformation. While VR heads top the list of gaming devices in the metaverse, mobile phones, gaming PCs, gaming consoles, and hearable/wearables are also evolving to suit the demands of metaverse applications. Metaverse also blends games with other apps like live streaming, cryptocurrencies, and social media, creating several possibilities for players to transact across the ecosystem chain. For example, gamers can use the NFTs/cryptocurrencies in metaverse to purchase digital assets, which they can preserve for another game, maybe from a different publisher. Thus players will earn greater value for money while also enjoying a near-real world gaming experience with possibilities never imagined before.
Quote for the day:
"Most people live with pleasant illusions, but leaders must deal with hard realities." -- Orrin Woodward