There are lots of reasons a company might entertain automating processes with robots. According to Kern, the main reason is a labor shortage. Prior to COVID-19-related slowdowns, a competitive labor landscape and rising costs of living in many countries around the globe made hiring tough for skilled and unskilled positions alike. Automation, which often promises ROI efficiencies over time, particularly when it comes to repeatable tasks, is an attractive solution. "Robots can save money over time, not just by directly eliminating human labor, but by cutting out worker training and turnover," according to the Lux report for which Kern served as lead. "Most companies turn to automation and robotic solutions to deal with labor shortages, which is common in industries with repetitive tasks that have a high employee turnover rate. Companies also frequently use robots to automate dangerous tasks, keeping their employees out of harm's way." Post-COVID-19, there are also considerations like sanitation and worker volatility. As I've written, the perception of automation is changing almost overnight. Where robots were once, very recently, associated primarily with lost jobs, there's been a new spin in the industry to tout automation solutions as commonsense in a world where workers are risking infection when they show up at physical locations.
The traditional infrastructure team still operates ADCs and load balancers in the data center, while preferring the vendors they have worked with in the past. DevOps and CloudOps have taken control in the public cloud, choosing to use software and cloud provider services that are more integrated with their DevOps toolchains. This fractured operations model is problematic. Companies with divided Layer 4-7 operations are less likely to be successful with this infrastructure. EMA research participants also revealed why they feel a need to close this operational gap. First, 43% of enterprises said this situation has introduced security risks. In most enterprises, application delivery infrastructure is an important component of overall security architecture. Companies need to take a unified approach to network security. Research participants identified compliance problems (36%) and operational efficiency (36%) as the top secondary challenges associated with fractured operations. And 30% said platform problems -- such as issues with scale, performance, functionality or stability -- are a major challenge.
Technology providers to specific areas of finance have created significant businesses. Across the insurance ecosystem, Guidewire, Applied Systems, and Vertafore capture $10 billion of value. BlackKnight, the leading analytics provider to the mortgage industry, is an $11 billion business. Are you thinking about managing financial documents for your public company? You may turn to Broadridge, which makes a pretty penny in this business, boasting a $13 billion market cap. While these are massive markets, it is not easy to disrupt incumbents. A combination of regulatory hurdles, entrenched behavior, low risk-tolerance, and the benefits of larger balance sheets have kept upstarts at bay for decades. However, as venture capital supports the ecosystem, modern technology creeps into the sector (cloud, APIs), connectivity and data exchanges improve, and consumers grow tired of incumbents, the tide continues to shift. This shift and the challenge to the status quo by fintech upstarts will have lasting effects. Even when incumbents acquire their biggest disruptors, such as Visa’s acquisition of Plaid, innovations pioneered by those startups become integrated into the system and help move the industry forward.
What strange times we live in. Who’d have thought that I’d be writing an article on how Microsoft is the best thing to happen to Google Chrome? A few years ago the idea of Microsoft getting involved in an open source project would cause a mixture of laughter and dread. You know… Microsoft, the foe of open source who had a CEO that once said that Linux was “a cancer that attaches itself in an intellectual property sense to everything it touches.” The company that couldn’t make a decent web browser to save its life. But, believe it or not, I really do think that Microsoft’s involvement has made Chrome a much better browser. ... Basically, since dropping its opposition to open source, and not only embracing it, but putting its money where its mouth is, the thought of Microsoft being involved with an open source project is no longer the stuff of nightmares. It’s proved to be a valuable contributor to the open source community already. But how does this affect Google’s Chrome browser? Well, ever since Microsoft stopped using its own web engine, EdgeHTML, for its Edge web browser, and instead built a brand-new version that’s based on Chromium, it’s been contributing a steady stream of fixes and new features to Chromium – and those have not just been benefitting Edge, but Chrome as well.
The technology provides a low code, cloud-based authoring experience for the business user to create bot scripts with a desktop recorder, without the need of IT. These scripts are executed by digital robots to complete tasks. Digital robots can run on-demand by the end-user or by an automated scheduler. Arguably, WDG is on a par with Softomotive – acquired by Microsoft for considerably more money. What is clear is these RPA firms are offering pretty much the same functionality for the basic scripting and recording. WDG is focused heavily on quality customer service ops and is great at integrating with chatbots, digital associates and other AI tools. Pre-Covid, most RPA was focused on low-risk back-office processes, especially in finance. Now customers are desperate to automate the customer-facing and revenue-generating processes and need tools proven to work in the environments. Noone has a huge advantage in the CX automation space so this provides a greenfield opportunity for IBM. The WDG automation software sits under IBM Cognitive and Cloud giving it a broader playing field to compete with the likes of MSFT, Pega, Appian, and even ServiceNow. Arguably, this is the real play that excites IBM’s top brass.
Restated — domain knowledge is the learned skill to communicate fluently in a group’s data dialect. Its component parts are: general business acumen + vertical knowledge + data lineage understanding. For example, a data scientist in people analytics requires a foundational knowledge of the business + human resources + the inner-workings of their company’s HR tools and processes which create the data they work with. Those processes and other inputs to the dataset are crucial. A data scientist can’t create meaningful insights before they understand what the data is saying today. Is it telling a story? Is it, or subsets of it, too polluted to use today? Are some data points proxies for or inputs to others? The more complex your business processes and associated data lineage, the longer your data dialect will take to learn. For digital native companies whose data collection is automated with intuitive dialects (i.e. a “click” is a “click”), domain knowledge can be developed much more quickly than for large, longstanding companies which have undergone transformations, acquisitions and/or divestitures. If you hire a data scientist, how long will it take them to learn your data dialect? And can you provide air cover for them to do so before applying pressure to produce “insights?”
A recruiter can learn a lot about the candidate in that half hour, including any side projects they might be involved in or games they've written. These "are often a window into a developer's willingness to take initiative," Volodarsky said. Learning what a developer does in their spare time can also provide great insight into their personality, he said. "Hiring great coders is important, but you also want to collaborate with interesting people, too." When it comes to hiring freelance developers it's important that they understand both the code and the nuances of the business they're contracting for, and this will come through in that conversation over a falafel, or the like, he said. In terms of motivating factors, not surprisingly, an overwhelming 70% said they were looking for better compensation, while 58.5% said they want to work with new technologies, and 57% said they were curious about other opportunities. Close to 70% of respondents said they learn about a company during a job hunt by turning to reviews on third-party sites such as Glassdoor and Blind. However, a large number also said they learned from viewing company-sponsored media, such as blogs and company culture videos.
Singapore over the past several years has invested significant resources towards becoming a digital economy, rolling out an ambitious smart nation roadmap, driving the adoption of emerging technologies, and overhauling its own ICT infrastructure. With the global pandemic now adding new impetus to digital transformation, the government has made a concerted effort to drive digital adoption deeper into the business community and local population. It established a new office to work alongside the business community and local population to push the "national digitalisation movement". Initiatives would include the deployment of 1,000 "digital ambassadors" to help stallholders and seniors go digital and setting up of 50 digital community hubs across the island to offer one-to-one assistance on digital skills. A new ministerial committee will also coordinate the country's digitalisation efforts and focus on priorities such as assisting people in learning new skills and galvanising small businesses to go digital. More funds and resources have been further directed to facilitate digital transformation initiatives.
AIOps has generated industry hype since 2017, as advances in machine learning algorithms prompted IT monitoring vendors to envision a new method of automation for their products. At the same time, complex microservices infrastructures became impossible to manage entirely by human hands alone. Since then, AIOps tools have grown more sophisticated, adding automated remediation features to event correlation and automated root cause analysis, and AIOps vendors that began in specialized areas have also broadened the workloads their tools can support. Most recently, those vendors include Epsagon, which emerged in 2018 with AI-supported distributed tracing for serverless environments and expanded in 2019 to include container and cloud workloads. It now offers AIOps features it calls Applied Observability, which automate menial incident resolution tasks in response to metrics and logs in addition to traces. Last month, Epsagon launched a partnership with Microsoft centered on Kubernetes environments after previously inking a deal with AWS focused on its Lambda serverless compute service.
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