What is the IoT?

The increasing prevalence of technology in our everyday lives has meant that the world around us is also getting smarter too.

Digital devices are not just in our pockets or our offices, but increasingly in our homes, buildings, and many places and cities. Taking a key role in helping collect, analyse and monitor data and information about their surroundings, these devices are able to communicate with each other through a massive intertwined network known as the ‘Internet of Things’.

Usually shortened to the IoT, this collective allows devices to connect and talk to each other and also to us, delivering reams of data and in-depth analysis that will hopefully increase the world around us.

Still largely a work in progress, the IoT is set to revolutionise how we live our lives, with experts tipping it to have the biggest technological impact since cloud computing.

Many consumers are already enjoying the benefits of the IoT, as popular connected home products such as the Amazon Echo smart speaker and the Nest thermostat from Google becoming common sights in buildings around the world.

But the wider world can also benefit from the IoT, with businesses being able to streamline previously complex and data-hungry processes through improved automation, freeing up employees for other tasks.

Public sector organisations such as hospitals can also use sensors to monitor patients more effectively, and local governments can monitor pollution, traffic levels, weather data, and much much more.

The IoT is growing fast, and is set to affect more and more areas of our lives in the years to come, resulting in a smart world that previously was only imaginable in science fiction.

Source: Techradar

What is DevOps, and why is it important for your organization to understand and implement? Alvaro Oliveira, VP of Talent Operations at Toptal, provides a comprehensive, non-technical explanation of what DevOps means, and explores why DevOps principles are crucial for enterprise organizations of all stripes to consider.

Though there are no surefire, “silver bullet” methods for improving IT efficiency, DevOps has yielded results that are hard to ignore. As its name suggests, DevOps combines software development and software operations principles with the goal of helping organizations develop products with greater speed and efficiency. TIAA-CREF, for example, has seen its $40 billion business make significant improvements through using DevOps principles. An interview by TechBeacon with TIAA’s Chief Digital Officer, Scott Blandford, explains how TIAA moved from clunky, legacy software and systems to “an agile-based DevOps approach” that yielded a four-fold increase in development productivity. Updates are released far more quickly, and “IT has regained the trust that had eroded over the years as it began meeting user expectations.”

Results from the 2017 State of DevOps Report suggest striking differences between high performance organizations employing DevOps principles and organizations that do not. According to the report, high performance organizations have far higher software deployment frequencies (46 times more frequent), far faster lead time for changes (440 times faster), and a significantly lower software change failure rate (five times lower) than their lower performance counterparts.

Despite these significant benefits, DevOps stands as a classic example of an important, relatively new technical concept that has been misused or misunderstood all too often. For many, the idea remains fuzzy, and even a basic definition of DevOps can prove elusive.

This lack of clarity could potentially have negative effects on organizations and teams attempting to implement DevOps principles, causing strategic confusion and impeding the speed and efficiency that DevOps is supposed to promote. As a DevOps engineer at IBM said in an article published by InfoWorld, “We needed to answer some basic questions and determine the problems we were trying to solve…If you don’t know how the work is actually done, you don’t know which problems are worth solving.”

As software development and operations become more closely intertwined, and as companies become increasingly reliant on cloud infrastructure, executives and project managers must develop fluency in DevOps to remain competitive and ensure their teams are performing at their full potential.

DevOps should not be thought of as another vague buzzword, but rather as an important concept with the potential to dramatically improve products and businesses. This article, aimed at a relatively non-technical audience, aims first and foremost to clarify exactly what DevOps is. Using specific examples, this article will then explore what DevOps principles look like when done well, and why DevOps matters for you and your organization.

DevOps as a Type of Engineer, Culture, and Practice

What is DevOps? In a previous article published by Toptal, Demir Selmanovic writes that “DevOps is a culture, mindset, and it is part of IT as (a) whole.” He writes further that DevOps is a practice that enables organizations to optimize speed and efficiency across IT functions.

Amazon Web Services, which is the biggest player in cloud infrastructure and has accordingly developed significant DevOps expertise, uses a similar definition, saying that “DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes.”

“DevOps people are basically those who have found interest in both systems administration and software development and decided to combine their skills to create a unified, better approach to both.”

These are both useful definitions for an audience already well-versed in related fields, but they may be too abstract for executives with less extensive technical backgrounds. Indeed, perhaps some of the confusion surrounding the definition of DevOps stems from the fact that it is often referenced simultaneously as a type of engineer, set of practices, and culture. While DevOps does encompass each of these elements, it is useful to begin by considering them separately.

To start with what a DevOps engineer looks like, Martin Chikilian, a software developer formerly for IBM and Hewlett Packard with over a decade of experience employing DevOps principles, puts it in simple, concrete terms: “DevOps people are basically those who have found interest in both systems administration and software development and decided to combine their skills to create a unified, better approach to both.”

They are able to maintain the servers, networks, and other types of infrastructure systems a company has, as well as actively iterate and improve on those systems through software development. As Chikilian said another way, “A DevOps person is someone who can leverage the foundations of software development to help themselves and companies build better tools to handle infrastructure.”

The “combination of cultural philosophies,” as Amazon puts it, refers to the combination of approaches used by software developers and those with infrastructure, or software operations, expertise. In breaking down the traditional barrier between these practices, a DevOps culture seeks to empower organizations to benefit from the distinct strengths that developers and infrastructure experts bring to the table. Successfully implementing DevOps principles “requires a change in culture and mindset” for companies that silo these different kinds of engineers. As Emily Dowdle describes at the 2016 Nordic API Platform Summit, removing barriers also helps alleviate the natural friction that can sometimes exist between developers and infrastructure experts and foster a more congenial, cooperative work environment.

Stated simply, DevOps is about translating complex manual processes involving error-prone human interaction into an instrumented approach that can be tested, measured, and easily scaled.

Armed with an understanding of what a DevOps practitioner and culture look like, what DevOps means as a practice becomes more apparent. Stated simply, DevOps is about translating complex manual processes involving error-prone human interaction into an instrumented approach that can be tested, measured, and easily scaled. For example, if a developer would like to create an environment that allows business users to provide feedback, he or she can initiate an automated process in which the developer can issue a command created by the DevOps team (rather than handing off a piece of code to the infrastructure team), which performs the relevant task in a consistent and tested way, getting to the expected results quickly and enabling collaboration.

A comprehensive definition of DevOps requires an understanding of what it means as a type of engineer, culture, and practice. Having explored what DevOps means from these perspectives, it is now important to delve into what DevOps looks like when successfully implemented.

Your DevOps Toolkit

In addition to the aforementioned cultural shift – going from a company that silos software developers and infrastructure experts to one that embraces their collaboration – companies need to understand a number of specific practices and tools crucial to DevOps. Below are three of the most crucial (though certainly not the only) of such practices:

Automation: Increased efficiency is central to DevOps, and this is significantly achieved through automating a range of relatively slow, onerous processes in software development and infrastructure maintenance. One specific example that Amazon cites is the practice of automatically sending out relatively small but frequent software updates. This practice takes the onus off of systems administrators, who might otherwise have to perform these updates manually. As Amazon notes, this practice also has the benefit of de-risking software deployment through enabling administrators to more easily catch and fix bugs that may arise. Automation is a cornerstone of DevOps and is crucial to the other DevOps practices discussed below.

Continuous Integration: On a fundamental level, DevOps is about close collaboration between engineers and, further, entire teams. Continuous Integration refers to the practice of engineers sharing and merging code in a central location. As Amazon explains, “In the past, developers on a team might work in isolation for an extended period of time and only attempt to merge their changes… once their work was completed.” Through Continuous Integration, engineers can more efficiently collaborate and avoid the bottlenecks associated with developing and integrating their code in a more piecemeal fashion.

Continuous Delivery: Continuous Delivery refers to the practice of automatically delivering and implementing software product changes as they’re made. In other words, Continuous Delivery is enabled by Continuous Integration, given that changes to code can only be effectively pushed to the entire system if the code is already housed in a central place. Automation is also crucial to Continuous Delivery. Indeed, the aforementioned example of sending small, frequent systems updates can also be thought of as an example of Continuous Delivery. Continuous Delivery enables organizations to implement changes and improvements quickly, and it allows engineers to focus their time more efficiently on other, complex issues.

Hiring the Right People

Of course, companies need to hire the right engineers for the above practices to be successfully implemented. DevOps engineers must have a skillset that combines software development and systems infrastructure expertise, and this combination may be difficult to find.

“It’s important that they be able to bridge the gap between the raw code and the challenges that the business has to face everyday – such as reducing friction between ideas, delivery, and consumers.”

Andrea Villa, a former DevOps engineer at Atlassian and system architect at CloudReach with 15 years of experience in system administration, information security, and networks, believes that DevOps engineers need a broad range of experience and knowledge to be successful.

“To be effective, today’s DevOps Engineer needs to have a developer background and possess a wide knowledge of system administration and network knowledge,” Villa said. “It’s important that they be able to bridge the gap between the raw code and the challenges that the business has to face everyday – such as reducing friction between ideas, delivery, and consumers.”

Qualifying as a strong DevOps Engineer is clearly easier said than done, and finding such engineers can be similarly difficult. Still, understanding the tools underlying DevOps practices can empower your organization to more accurately tailor hiring practices around finding the right talent and implementing these practices successfully.

Why DevOps Really Matters

Using DevOps practices comes with a range of benefits, some of which – including greater efficiency, security, and organizational collaboration – have already been articulated. The 2017 State of DevOps Report quantifies this increase in efficiency, reporting that high performing organizations employing DevOps practices spend 21 percent less time on unplanned work and rework, and 44 percent more time on new work.

More generally speaking, however, successfully implementing DevOps practices can have a profound impact on your company through improving efficiency and execution in areas that are both essential and decidedly unglamorous.

Fredrik Håård, an engineer with over 12 years of DevOps experience who worked as a Senior Cloud Architect at McKinsey and at Wondersign, articulates this point more fully.

“Good DevOps engineers must be a champion – and take responsibility for – all the areas that might not be prioritized by the organization such as data security, disaster recovery, mitigation, and audits,” Håård said. “The choices you make in DevOps can have long-lasting effects at a company.”

Microsoft has also seen these positive effects through implementing DevOps practices in its Cloud + Enterprise engineering group. To deliver the best results, Microsoft explains, “we needed engineering and operations to work closely together throughout the entire lifecycle of development from design to deployment in production.” Through shifting to a DevOps culture and using DevOps practices, Microsoft was able to scale and deliver high quality services to its customers far more quickly than it had previously.

Solving these problems, and improving efficiency, are great in themselves, but they are ultimately a means to a more fundamental end: producing amazing products and yielding greater profits and customer satisfaction.

DevOps engineers can effectively act as a company’s internal commando team, helping to solve a diverse range of problems that, though perhaps unglamorous or out of view, are absolutely crucial to a company functioning properly.

Solving these problems, and improving efficiency, are great in themselves, but they are ultimately a means to a more fundamental end: producing amazing products and yielding greater profits and customer satisfaction. Perhaps the most important reason for why DevOps really matters is that it can enable organizations to maximize each of these metrics.

Facing the Future with DevOps

In 2015, Andreessen Horowitz Partner Scott Weiss said that DevOps is “More than just a methodology. It’s a must-have skill set for the modern programmer — and is increasingly becoming its own department.”

This has proven true, as a range of tech companies, including Amazon, Microsoft, and Google, have built DevOps teams and dedicated significant resources to building expertise in the space. Weiss and other major tech players see DevOps as not simply a passing fad, but rather a key component in the future of software.

DevOps will continue to be particularly essential as industries become more reliant on cloud infrastructure. In a 2017 report, Gartner Research projected that the Cloud Services market will grow over 15% per year through 2020. To put this growth in terms of dollars, a recent article published by Forbes indicates that the cloud computing market will grow from roughly $260 billion in 2017 to roughly $411 billion in 2020. DevOps can make a huge difference in how quickly your company successfully migrates systems onto the cloud. At Toptal, we’ve used DevOps since the beginning – and it’s been integral to our success as a hypergrowth cloud company.

Just as the Cloud Services market is growing at a rapid pace, so too is the importance of DevOps. Whether in energy, healthcare, or higher education, your company will need to develop DevOps expertise. Understanding what DevOps is, how it’s implemented, and why it’s so important, represents an essential first step as you think about using this key practice going forward.

Source: Toptal

Data science and AI had a big year in 2019. AI funding in the UK surpassing the total for the previous year within the first six months of the year, according to research by Tech Nation and 80 percent of respondents to an MHR Analytics survey say they planned to hire a data scientist or seek data consultancy before the end of the year.

The rise is set to continue in 2020 as the disciplines continue to permeate more sectors and organisations, but the deployments will not be straightforward and there are major dangers ahead for those who get them wrong,

The most prominent of these risks involve the General Data Protection Regulation (GDPR). The law has been in place since May 2018, but it was only last year that the UK’s data protection watchdog issued the first penalties for breaches of its rules, slapping British Airways and Marriott with respective fines of £183m and £99m on consecutive days in July.

The growing number of breaches and the eye-popping penalties that they can incur leads Blake Collins, research analyst at cyber security firm SiteLock, to envision a booming demand for “data breach hunters” who search for vulnerabilities before threat actors can actively exploit them.

“This phenomenon is a byproduct of a systemic problem: technology can be difficult for many to understand and if not leveraged properly for proactive protection, could cost an organisation money in terms of downtime, loss of reputation, and resources spent fixing a security-related problem,” he said.

“The drawback? Unless you’re hiring a cybersecurity professional to find the leak, someone less honorable most likely will. Or worse, if a company is lucky enough to have this type of data responsibly disclosed for free, it may not recognise the severity of the issue and may not have the resources or know-how to address the issue effectively.”

John Buyers, who leads the commercial practice at international law firm Osborne Clarke, believes the growth in AI use cases will create friction with the high standard for consent set in GDPR.

“We’re already seeing instances of companies refusing to implement machine learning because of an inability to sufficiently meet GDPR consent standards for processing personal data – whether that is systems which recognise faces, understand voices or provide customised online experiences,” he said.

“This is something which has been explicitly recognised by some European regulators – most recently the Irish Data Protection Commissioner Helen Dixon at a speech in Dublin in November 2019.”

Enterprises will need to respond by making governance an integral part of their AI systems, as Dataiku chief customer officer Kurt Muehmel explains.

“Enterprise AI platforms will comply by incorporating governance systems to ensure AI is controlled and calculated and based on models that people can easily explain and understand,” Muehmel said.

Ethical AI

Any legal developments will be playing catch up to ethical concerns, which is set to become a business concern as well as a moral one as public awareness about privacy and bias continues to grow.

The risks of algorithmic decision-making will remain hard to address if the systems that deliver them stay hidden in black boxes. As a result, Matt Sanchez, Chief Technology Officer at CognitiveScale, believes that understanding fairness, bias, explainability and robustness of AI models will become as important as understanding their performance and effectiveness in 2020.

“In certain industries, it will be difficult to justify the value of an AI system without scoring and selecting models based on these additional criteria because the risk will be deemed too high,” he said.

“Look for most leading data science platforms and machine learning toolkits to start including tools to help developers understand these aspects of machine learning. Also, look for regulators, legislators, and courts of law to start asking to understand these issues at a deeper level as more cases surface where consumer trust is breached due to data misuse or perceived algorithmic deficiencies.”

The growing demand from both consumers and governments for greater scrutiny of AI and data-driven technologies leads Genpact chief digital officer Sanjay Srivastava to predict the rise of digital ethics officers.

“These officers will be responsible for implementing ethical frameworks to make appropriate decisions about the use of new technologies, considerations around security and bias, and preparing for the technology challenges still to come,” he said.

John Gikopoulos, global head of AI and automation at Infosys Consulting, envisions a similar role emerging with a more specific focus: the AI ethicist.

“In 2020, we will start to see enterprises employ people or even teams of people whose main role will be to formulate the ethics of our new AI-powered world,” he said. “These AI ethicists will need to liaise with the ecosystem of affiliated AI entities and gradually create, from the bottom up, the rules and conditions that will define the field of play.”

Adoption answers

Even if these ethical and legal barriers are overcome, developing AI solutions remains costly and resource-intensive. Sanjay Srivastava of Genpact predicts that businesses will respond by using a “transformation-as-a-service” adoption model.

“This model allows organisations to gain access to AI technologies that have already been trained on basic tasks and knowledge,” he said. “This, along with access to other data and cloud technologies, can significantly cut down on the resources required to keep up with shifting business strategies and customer demands.”

John Gikopoulos, Global Head of AI and Automation at Infosys Consulting, anticipates these issues triggering demand for a similar concept from the same nomenclature: AI-as-a-Service.

“What’s so exciting about AI-as-a-service is not just that the huge economies of scale will make the technology available to every organisation that wants to use it. It will also give us the much-missed ability to harness all the infrastructure, platforms and knowledge towards creating real and sustainable value,” he said.

“By packaging AI as part of a solution, we’ll make it much easier to identify valuable new use cases while providing a platform with end-to-end responsibility for delivering them.”

Srivastava also expects businesses to ease the path to adoption and democratise access to the technology by deploying AI accelerators that have been pretrained on the necessary domain expertise.

“By 2025, it’s estimated that organisations that are AI leaders will be 10 times more efficient and hold twice the market share of those who fail to embrace the technology,” he said. “Companies that fail to accelerate AI adoption will lose significant market share – making this a matter of survival for many organizations.”

Evolving skills

By 2025, around 463 exabytes of data will be created each day, according to research by special reports publisher Raconteur. Srivastava believes this growth will reduce the value of data and increase that of human judgment, as the vast opportunities hidden in the information need to be unlocked by someone who can make final decisions and drive action. He recommends that enterprises reskill their staff to find this value.

“Rather than sticking to traditional classroom settings, identify employee ‘experts’ who have knowledge others need and share that expertise, thus harnessing the collective intelligence within the organisation,” he said.

This will help deepen the pool of available AI talent, which will become harder to find as the demand for AI solutions grows.

John LaRocca, managing director at Fractal Analytics, expects businesses to adapt to this dilemma by enabling more applications to be developed by non-AI professionals.

“Non-AI practitioners, such as knowledge workers and analysts, who are not skilled AI practitioners (but have great domain expertise), will start to develop rudimentary applications aided by automated AI engines,” he said. “The onus will be on corporate training programs to retrain/upskill these new practitioners and on IT to enable them with automated AI environments that use AI itself (e.g., machine learning apps to help develops train models without having to write code).

“This is not unlike the historical lifecycle of analytics, and it will similarly benefit everyone in the ecosystem – businesses will expand their capacity to develop and benefit from AI apps, AI experts will be working on truly leading-edge applications and tie newly upskilled non-AI practitioners who will contribute more and have more marketable skills.”

Source: Computer World