On the whole, the goal is to make engineers autonomous as much as possible for organising their domain into the structure of the microservices they write and support. As a Platform Team, we provide knowledge and documentation and tooling to support that. Each microservice has an associated owning team and they are responsible for the health of their services. When a service moves owners, other responsibilities like alerts and code review also move over automatically. ... Code generation starts from the very beginning of a service. An engineer will use a generator to create the skeleton structure of their service. This will generate all the required folder structure as well as write boilerplate code so things like the RPC server are well configured and have appropriate metrics. Engineers can then define aspects like their RPC interface and use a code generator to generate implementation stubs of their RPC calls. Small reductions in cognitive overhead for engineers allows them to cumulatively focus on business choices and reduces the paradox of choice. We do find cases where engineers need to deviate. That’s absolutely okay; our goal is not to prescribe this structure for every single service. We allow engineers to make the choice, with the knowledge that deviations need appropriate documentation/justification and knowledge transfer.
The fundamental causes for the skill gap are myriad, starting with a lack of training and career-development opportunities. About 68 percent of the cybersecurity professionals surveyed by ESG/ISSA said they don’t have a well-defined career path, and basic growth activities, such as finding mentor, getting basic cybersecurity certifications, taking on cybersecurity internships and joining a professional organization, are missing steps in their endeavors. The survey also found that many professionals start out in IT, and find themselves working in cybersecurity without a complete skill set. ... The COVID-19 pandemic is not helping matters on this front: “Increasingly, lockdown has driven us all online and the training industry has been somewhat slow to respond with engaging, practical training supported by skilled practitioners who can share their expertise,” Steve Durbin, managing director of the Information Security Forum, told Threatpost. “Apprenticeships, on the job learning, backed up with support training packages are the way to go to tackle head on a shortage that is not going to go away.”
Using big data and analytics has always been on a steady growth trajectory and then COVID-19 exploded and made the need for data even greater. Companies and institutions like Johns Hopkins and SAS created COVID-19 health dashboards that compiled data from a myriad of sources to help governments and businesses make decisions to protect citizens, employees, and other stakeholders. Now, as businesses are in re-opening phases, we are using data and analytics for contact tracing and to help make other decisions in the workplace. There have been recent announcements from several big tech companies including Microsoft, HPE, Oracle, Cisco and Salesforce focusing on developing data driven tools to help bring employees back to work safely — some even offering it for free to its customers. The need for data to make all business decisions has grown, but this year, we saw data analytics being used in real time to make critical business and life-saving decisions, and I am certain it won’t stop there. I expect massive continued investment from companies into data and analytics capabilities that power faster, leaner and smarter organizations in the wake of 2020’s Global Pandemic and economic strains.
Thanks to a series of misplaced policy choices, the government has systematically eroded the permitted operations of the Indian outsourcing industry to the point where it is no longer globally competitive. Foremost among these are the telecom regulations imposed on a category of companies broadly known as Other Service Providers (OSPs). Anyone who provides “application services” is an OSP and the term “application services” is defined to mean “tele-banking, telemedicine, tele-education, tele-trading, e-commerce, call centres, network operation centres and other IT-enabled services”. When it was first introduced, these regulations were supposed to apply to the traditional outsourcing industry, focusing primarily on call centre operations. However, it has, over the years been interpreted far more widely than originally intended. While OSPs do not require a license to operate, they do have to comply with a number of telecom restrictions. The central regulatory philosophy behind these restrictions is the government’s insistence that voice calls terminated in an OSP facility over the regular Public Switched Telephone Network (PSTN) must be kept from intermingling with those carried over the data network.
Data bias is tricky because it can arise from so many different things. As you have keyed into, there should be initial considerations of how the data is being collected and processed to see if there are operational or process oversight fixes that exist that could prevent human bias from entering in the data creation phase. The next thing I like to look at is data imbalances between classes, features, etc. Oftentimes, models can be flagged as treating one group unfairly, but the reason is there is not a large enough population of that class to really know for certain. Obviously, we shouldn't use models on people when there's not enough information about them to make good decisions. ... Machine learning interpretability [is about] how transparent model architectures are and increasing how intuitive and understandable machine learning models can be. It is one of the components that we believe makes up the larger picture of responsible AI. Put simply, it's really hard to mitigate risks you don't understand, which is why this work is so critical. By using things like feature importance, Shapley values, surrogate decision trees, we are able to paint a really good picture of why the model came to the conclusion it did -- and if the reason it came to that conclusion violates regulatory rules or makes common business sense.
Compared to unit tests, this allows much more of the application code to be tested together, which can rapidly validate the end-to-end behaviour of your service. These are also sometimes referred to as functional tests, since the definition of integration testing may be applied to more comprehensive multi-service testing as well. It’s entirely possible to test your applications in concert with their dependencies, such as databases or other APIs they expect to call. In the course, I show how boundaries can be defined using fakes to test your application without external dependencies, which allows your tests to be run locally during development. Of course, you can avoid such fakes to test with some real dependencies as well. This form of in-memory testing can then easily be expanded to broader testing of multiple services as part of CI/CD workflows. Producing these courses is a lot of work, but that effort is rewarded when people view the course and hopefully leave with new skills to apply in their work. If you have a subscription to Pluralsight already, I hope you’ll add this course to your bookmarks for future viewing.
RPA is not on its own an intelligent solution. As Everest Group explains in its RPA primer, “RPA is a deterministic solution, the outcome of which is known; used mostly for transactional activities and standardized processes.” Some common RPA use cases include order processing, financial report generation, IT support, and data aggregation and reconciliation. However, as organizations proceed along their digital transformation journeys, the fact that many RPA solutions are beginning to integrate cognitive capabilities increases their value proposition. For example, RPA might be coupled with intelligent character recognition (ICR) and optical character recognition (OCR). Contact center RPA applications might incorporate natural language processing (NLP) and natural language generation (NLG) to enable chatbots. “These are all elements of an intelligent automation continuum that allow a digital transformation,” Wagner says. “RPA is one piece of a lengthy continuum of intelligent automation technologies that, used together and in an integrated manner, can very dramatically change the operational cost and speed of an organization while also enhancing compliance and reducing costly errors.”
“What is wanted is a new type of networking platform that establishes a reliable, high performance, zero trust connection across the Internet — meaning one that will only connect an authorised device and authorised user using an authorised application (ie ‘zero trust’),” he said. “With zero trust, every connection is continuously assessed to identify who or what is requesting access, have they properly authenticated, and are they authorised to use the resource or service being requested — before any network access is permitted. “This can be achieved using software defined networking loaded into the edge device or embedding networking capabilities into applications with SDKs and APIs. This eliminates the need to procure, install and commissioning hardware. Unlike VPNs, these software-defined connections can be tightly segmented according to company policies (policy based access), determining which workgroups or devices can be connected, and what they can share and how. “This suggests a new paradigm: an edge-core-cloud continuum, where apps and services will run wherever most needed, connected via zero trust networking access (ZTNA) capable of securing the edge to cloud continuum end to end...."
The leader of any transformation effort needs to be resilient and determined to deliver the program’s full potential. Yet that person also needs to understand and acknowledge the needs of employees during a radical upheaval. Sometimes leaders must be pragmatic—particularly when the company’s long-term survival is at stake. At other times, empathy and flexibility are more effective. One CEO brought determination and conviction to the company’s transformation, and he was able to tamp down dissent, gossip, and negative press. He was also willing to reverse his decisions on some matters. For example, one cost-reduction measure was a cutback in employee travel. Initially, the CEO told employees that they needed direct approval from him for any travel expenses above a certain amount. However, after about a year, he relaxed this policy after considering employees’ feedback. ... Transformations are a proving ground for leadership teams. They can be catalysts to long-term business success and financial performance—but companies undergoing a transformation underperform almost as often as they outperform. Our analysis shows that there is a systematic way to increase the odds of success.
Another surprising problem for the global manufacturing model is that shipping has actually become less efficient, largely due to business decisions of the shippers. Maersk, the world-leading Danish firm, continued to order ever-larger container ships after the financial crisis, convinced that consumer demand would quickly resume its previous growth. When it did not, the firm and its competitors were forced to sail half-full megaships around the world. Because the ships were several meters wider than their predecessors, the process of removing containers took longer. And they were designed to travel more slowly to conserve fuel. Delays became much more common, undermining trust in the industry. Without reliable shipping, Levinson writes, firms have chosen to hold more inventory — which flies in the face of the prevailing orthodoxy. But things have changed. Inventories can act as a buffer when supply chains are in distress. For firms, “minimizing production costs was no longer the sole priority; making sure the goods were available when needed ranked just as highly.” It seems inevitable that the coronavirus pandemic will reinforce this drift back toward greater self-sufficiency in manufacturing.
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