One thing that has to be central to your strategy is traceability. You may have come across the term a few times before. It’s commonly used elsewhere in the business world, especially with regard to supply chains. Basically, what it means is keeping track of a commodity or product at every stage of the production process. Records of the product’s entire manufacturing and distribution history are kept so that the sources of any problems can later be determined and dealt with. Traceability thereby ensures that suppliers can act quickly and decisively in the event of a product recall, for example. Another advantage of traceability is that it provides additional transparency, which helps to maintain consumer confidence. As consumers are becoming increasingly aware of how products are sourced and manufactured, this is now an important consideration. It reassures consumers that manufacturers and suppliers are aware of their concerns and that they’re looking out for their best interests. You can see already, then, how much of this also applies to mobile DevOps. Traceability in DevOps is about ensuring clarity, accountability and the best possible end product for the consumer.
Companies emerging from this recession will adapt processes to “vaccinate” their systems against the next pandemic. In response to supply-chain disruptions, Volkswagen is considering increasing its 3D printing capabilities in Germany, which would give the automaker a redundant parts source. The government-run Development Bank of Japan will subsidize the costs of companies that move production back to Japan. Bringing production back onshore while controlling costs will require significant investment in robotics and AI. Even companies that don’t have their own production capacity, such as online retailers, plan to use AI to improve the reliability of complex global supply chains. So a surge in demand for AI talent is inevitable. ... One relatively new risk that managers must tolerate pertains to data. Even companies that are not yet exploiting their data effectively now recognize it as a valuable resource. As startups deploy AI software systems that prove more accurate and cost-effective than human beings, their early-adopter customers must be more willing to trust them with proprietary data. That will allow AI companies to train new products and make them even smarter.
Within the AWS ecosystem, a number of services stitched together provide this experience. And on the analytics team at Equinox Media on which I sit, we’ve embraced this architectural pattern to it’s fullest — foregoing self-maintained, provisioned servers to handle data processing — and opting instead for a parade of SQS queues, SNS topics, Kinesis streams, and of course, Lambda functions. As a result, diagrams of our data pipelines bear a visual resemblance to a 6th grader’s Rube Goldberg project. And as the metaphor suggests, this paradigm presents new organizational challenges to keep maintenance costs low. When adopting the serverless platform, one thing you’ll quickly notice is a proliferation in the number of code repositories your team is maintaining. This is the result of the a common development pattern that calls for a 1:1 ratio of Lambda functions to repos. And while there are benefits to having your business logic fragmented into digestible, bite-sized chunks of code; there are a number of supporting services that are best not replicated and distributed among them.
When humans have specific types of problems, we’ve built and trained machines to solve those problems. Examples include machine learning or ML. The ML algorithms that can identify cancer in brain images. The algorithms can also determine the best placements or designs for online ads, and there are deep learning systems that can predict customer churn in business. At the moment, we can only imagine how much more productive we will become as we form symbiotic relationships with AI. Routine tasks that currently take hours or days could be abbreviated to 10 or 15 minutes with the aid of a digital partner. From simple exercises like finding a new restaurant to more expert tasks such as cancer detection, we will increasingly rely on machines for everyday tasks. Dependence on machines might begin as a “second pair of eyes” or “ a second opinion,” but our commitment to machines (and AI) will evolve into full-on digital collaborators. ... Machine learning could bring about a revolution in how we solve problems to which the principle of “optimal stopping” applies.
People have a degree of protection when they are sitting amongst their colleagues. When suspicious emails come in, it is far easier to speak to a colleague and verify its authenticity. However, as people are now working from home, and they are isolated and often alone, that becomes much harder. Where web and email has been the traditional vector for these kinds of attacks, we are now seeing phishing attempts across multiple platforms, including social media and SMS. Every nation is being targeted and phishing emails appear in almost every language. In many ways, this is the largest set of cyber campaigns we have ever seen. Many of these emails offer falsified information or promises of help related to the pandemic. In one campaign found by Proofpoint, they even promise cures – which is something that malicious actors know the public are interested in and are likely to immediately pay attention to. These attackers are after personal information from anyone and everyone such as login credentials, name, date of birth and government ID details, or want to trick victims into installing malware on systems. A mixture of old, reskinned and relatively new malware is being used to attack users. We are looking at a cybercrime gold rush.
Indeed, it is when talent, technology and collaboration come together, that incredible advances can be achieved and at scale. This is exemplified in the solidarity of the technology sector to make a difference, bringing people closer across work, learning and entertainment despite lockdowns, and combating the virus through telemedicine and AI-assisted diagnosis, alongside helping to accelerate the research and drug development innovation curve. A notable example is the rapid establishment of the HPC Consortium involving 11 tech firms assisting federal government, industry and academic leaders across the world with access to expertise and high performance computing capacity. With a mobilization such as this, it is no surprise that by early April 2020, 50 potential vaccines and nearly 100 possible treatment drugs were in development. A feat that would have been unimaginable just a few weeks ago and emergency initiatives and innovations like this can also lay the ground for long term change, from business and education, to healthcare and government.
IT automation architects are typically found in DevOps organizations. It's fruitless to focus on a comprehensive automation strategy without a cooperative, integrated DevOps structure already in place. Because of the specialized nature of the job, architects are typically found in larger enterprises or those, like many cloud-native startups, that have mature DevOps practices. There's a wide variety of job titles and associated skills found under the DevOps umbrella. For example, a recent DevOps skills report from the DevOps Institute, a learning association for DevOps professionals, identified more than a dozen DevOps job titles for which organizations are hiring. "DevOps engineer/manager" was the most common title, cited by 51% of survey respondents -- who were comprised of IT professionals, DevOps practitioners, HR managers and consultants. "Automation architect" was the 9th most cited job title at 15%. The following chart summarizes other notable job titles and their response rates. When the same group of survey respondents was asked to rate the importance of various skills to DevOps work, proficiency at automation ranked at the top, with 66% citing it as very important and only 1% listing it as optional or unimportant.
Once executives around the world realize that their employees can not only work in online-first environments but are thriving and being even more productive, with greater opportunities for collaboration with their peers, they will embrace this “new” way of doing business. That, in turn, will unlock many benefits of scale and productivity that were unimaginable in the previous decades. The key driver of change will be that, now, every vendor or business partner can be assumed an online-first operator, and dozens and hundreds of legacy barriers will disappear practically overnight. Essentially, every business on the planet not only can, but will run like a Silicon Valley startup. Imagine, instead of attending five conferences a year, we can attend and collaborate at 50 virtual conferences while being more efficient with our time, given the removal of all that unnecessary travel. Imagine, if instead of a few business development conversations in a given quarter, we are able to do one hundred, now that the vast majority of our peers are in the same Slack or Telegram groups. Imagine that instead of a few dozen local restaurants, we will now have the choice to order from thousands.
In addition to heightening risk exposure, the failure of critical endpoint controls to deliver their maximum intended value is also resulting in security investments and, ultimately, wasted endpoint security spend. According to Gartner, “Boards and senior executives are asking the wrong questions about cybersecurity, leading to poor investment decisions. It is well-known to most executives that cybersecurity is falling short. There is a consistent drumbeat directed at CIOs and CISOs to address the limitations, and this has driven a number of behaviors and investments that will also fall short.” “What has become clear with the insights uncovered in this year’s report is that simply increasing security spend annually is not guaranteed to make us more secure,” said Christy Wyatt, President and CEO of Absolute. “It is time for enterprises to increase the rigor around measuring the effectiveness of the investments they’ve made. By incorporating resilience as a key metric for endpoint health, and ensuring they have the ability to view and measure Endpoint Resilience, enterprise leaders can maximize their return on security investments.”
It's all about getting oriented and understanding the team, the work they're doing, and the company. I typically use a process which can be followed when landing somewhere new. It involves creating a snapshot of the situation in which you can begin to work with your team. This snapshot is formed of three things: your own observations, your manager's observations, and your team's observations. Your observations are what you see as you settle in and collect information from your team and your manager. We outline a number of techniques for new managers to ask questions to discover what's really going on inside the team, what they're working on, and where there may be ambiguities or frictions. These involve informal conversations, booking in weekly one-to-one meetings, and diving deeper into what they're building and why. Then, as well as doing this downward, we also do this upward by having the new manager ask their manager about the same things. Do they think differently than what the team reports? Why? Are they prioritizing well? If not, why not?
Quote for the day:
"There's a fine line between stubbornness and the positive side of that, which is dogged determination." -- @JebBush