“FinOps brings financial accountability — including financial control and predictability — to the variable spend model of cloud,” says J.R. Storment, executive director of the FinOps Foundation. “This is increasingly important as cloud spending makes up ever more of IT budgets.” It also enables organizations to make informed trade-offs between speed, cost, and quality in their cloud architecture and investment decisions, Storment says. “And organizations get maximum business value by helping engineering, finance, technology, and business teams collaborate on data-driven spending decisions,” he says. Aside from bringing together the key people who can help an organization gain better control of its cloud spending, FinOps can help reduce cloud waste, which IDC estimates between 10% to 30% for organizations today. “Moving from show-back cloud accounting, where IT still pays and budgets for cloud spending, to a charge-back model, where individual departments are accountable for cloud spending in their budget, is key to accelerating savings and ensuring only necessary cloud projects are implemented,” Jensen says.
The principles that guide enterprises in the way they approach IoT analytics data are: Data is an asset: Data is an asset that has a specific and measurable value for the enterprise.Data is shared: Data must be shared across the enterprise and its business units. Users have access to the data that is necessary to perform their activities; Data trustees: Each data element has trustees accountable for data quality; Common vocabulary and data definitions: Data definition is consistent, and the taxonomy is understandable throughout the enterprise; Data security: Data must be protected from unauthorised users and disclosure; Data privacy: Privacy and data protection is considered throughout the life cycle of a Big Data project. All data sharing conforms to the relevant regulatory and business requirements; and Data integrity and the transparency of processes: Each party to a Big Data analytics project must be aware of and abide by their responsibilities regarding the provision of source data and the obligation to establish and maintain adequate controls over the use of personal or other sensitive data.
The remote and hybrid work trend is the most disruptive change in how businesses work since the introduction of the personal computer and mobile devices. Then, like now, the conversation was lost in the weeds. Should we allow PCs? Should we allow employees to bring their own devices? Should we issue pagers, feature phones, then smartphones to employees or let them use their own? In hindsight, it's clear that all these concerns were utterly pointless. The PC revolution was a tsunami of certainty that would wash away old ways of doing everything. So the only question should have been: How do we ensure these devices are empowering, secure, and usable? All focus should have been on the massive learning curve by organizations (what's the best way to deploy, update, secure, provision, purchase, and network these devices for maximum benefit) And by end users. In other words, while everyone gnashed their teeth over whether to allow devices — or what kind or level of devices to allow — the energy could have been much better spent realizing the entire issue was about skills and knowledge.
Misra said that there are a few especially important components involved in the success of the data product partner role and the discipline of product management for analytics and AI initiatives. One is to ensure that the partner role is strategic, proactive, and focused on critical business needs, and not simply an on-demand service within the company. All data products should address a critical business priority for partners and, when deployed, should deliver substantial incremental value to the business. The teams that work on the products should employ agile methods and include data scientists, data managers, data visualization experts, user interface designers, and platform and infrastructure developers. Misra is a fan of software engineering disciplines — systematic techniques for the analysis, design, implementation, testing, and maintenance of software programs — and believes that they should be employed in data science and data products as well. This product orientation also requires that there’s a big-picture focus, not just by the data product partners but by everyone on the product development teams.
Cybercriminals target employees across different industries to surreptitiously recruit them as insiders, offering them financial enticements to hand over company credentials and access to systems where sensitive information is stored. This approach isn’t new, but it is gaining popularity. A decentralized work environment makes it easier for criminals to target employees through private social channels, as the employee does not feel that they are being watched as closely as they would in a busy office setting. Aside from monitoring user behavior and threat patterns, it’s important to be aware of and be sensitive about the conditions that could make employees vulnerable to this kind of outreach – for example, the announcement of a massive corporate restructuring or a round of layoffs. Not every employee affected by a restructuring suddenly becomes a bad guy, but security leaders should work with Human Resources or People Operations and people managers to make them aware of this type of criminal scheme, so that they can take the necessary steps to offer support to employees who could be affected by such organizational or personal matters.
More often than not, some resources are underutilized. This usually stems from overbudgeting for certain processes. For instance, a cloud computing instance may be underutilized to the point that it uses less than 5% of its CPU. Note that with cloud services, you pay for the storage and computing power, rather than the space. In the instance highlighted above, it’s clear that there’s a case of significant waste. In your bid to optimize costs, it’s best to identify these idle instances and consolidate the workload into fewer cloud instances. It can be difficult to understand how much power the system uses without adequate visualization. Heat maps are highly useful in cloud cost optimization. This infographic tool highlights computing demand and consumption’s high and low points. This data can be useful in establishing stop and start times for cost reduction. Visual tools like heat maps can help you identify clogged-up sections before they become problematic. When a system load becomes one-directional, you know it’s time to adjust and balance it before it disrupts your processes.
Adding to the motivation to exit China and Taiwan was the saber rattling and increasingly bellicose tone from Beijing to Taiwan, along with fairly severe sanctions on semiconductor sales from the U.S. Department of Commerce. This has led some US-based cloud service providers, such as Google, AWS, Meta, and Microsoft, to look at adding server production lines outside Taiwan as a precautionary measure, according to TrendForce. There have been a number of other moves as well. In the US, Intel is spending $20 billion on an Arizona fab and another $20 billion on fabs in Ohio. TSMC is spending $40 billion on fabs in Arizona as well, and Apple is moving production to the US, Mexico, India, and Vietnam. TrendForce also noted a phenomenon it calls “fragmentation” as an emerging model in the management of the server supply chain. It used to be that server production and the assembly process were handled entirely by ODMs. In the future, the assembly task of a server project will be given to not only an ODM partner but also a system integrator.
Red Hat provides automated installation and upgrades for most common public and private clouds, allowing you to update on your own schedule and without disrupting operations. This process is perhaps one of the biggest differentiations between OpenShift and the standard Kubernetes environment, as it provides a runbook for updates and uses this to avoid disruption. If you’re running a cluster of OpenShift servers, you will be able to upgrade while applications continue to run, with OpenShift’s orchestration tools moving nodes and containers as required. When it comes to managed on-premises Kubernetes OpenShift is perhaps best compared with Microsoft’s Azure Arc tooling, which brings Azure’s managed Kubernetes to on-premises, using the Azure Portal as a management tool, or VMware’s Tanzu. They are all based on certified Kubernetes, adding their own management tooling and access control. OpenShift is more a sign of Kubernetes’ importance to enterprise application development than anything else.
Piyush Pandey, CEO at Pathlock, a provider of unified access orchestration, says budget constraints will affect both solution purchases, but also potentially the staff required to run them. “This will likely drive the consolidation of solutions that span across multiple organizations, such as access, compliance, and security tools,” he says. “This consolidation into platforms will help organizations prioritize their resources -- time, money, and people.” He says organizations that focus on comprehensive solutions can drive more synergies across different departments to be compliant. “This won't just be about cost savings, however -- it will also help reduce the complexity of their infrastructure, eliminating multiple standalone tools and solutions,” Pandey adds. Mike Parkin, senior technical engineer at Vulcan Cyber, a provider of SaaS for enterprise cyber risk remediation, explains the global financial downturn has hit multiple sectors, which means budgets are short overall. “The challenge will be keeping cybersecurity postures strong, even in the face of budget cuts,” he says.
Quote for the day:
"Leadership development is a lifetime journey, not a quick trip." -- John Maxwell