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  • 5 Key 2018 Tech Predictions for the C-Suite

    5 Key 2018 Tech Predictions for the C-Suite

    The most important digital trends for C-level business leaders to consider in 2018 relate to digital transformation, artificial intelligence (AI), internet of things (IoT), General Data Protection Regulation (GDPR) compliance and cybersecurity. Read on for insights and predictions about why these topics will matter so much to C-suites in 2018.

    Prediction 1: Digital Transformation—All Aboard!

    As one of the armaments of business innovation and disruption, digital transformation has become such a buzzword that it turns people off. But in 2018, fates likely will be sealed. A company has either transformed, is on its way to doing so, is actively planning its transformation or has decided not to get on that train. That last group won’t be invited to the AI party and are making themselves targets for disruption. According to Forrester, more than 60 percent of executives believe they are behind in their digital transformation. That’s OK, as long as they keep moving. CEOs need to fund this expensive endeavor from the outset and not try to make it pay for itself. Nickel-and-diming digital transformation could be the last mistake a CEO makes.

    Prediction 2: By 2020, AI Will Be a Game-Changer

    Every so often a technology comes along that has the potential to completely change everything around it. The last such technology was cloud computing. And like cloud, which had several precursors, AI has been around for a long time. AI and machine learning/deep learning are catching up to the initial promise, and that will become more evident as the next few years unfold.

    The best options for near-term success with AI are business intelligence and decision support. Some enterprises are using machine learning/deep learning to plumb big data—especially unstructured data such as text and video—to drive new forms of data analytics.

    Even so, many companies will strike out with AI/machine learning largely because it’s no paint-by-numbers endeavor. Companies need talented people who know how to harness this technology, but those people are scarce and don’t come cheap. Companies need businesspeople actively making sure that the technology serves the business need, potentially in charge of the project. And companies need the right data. An AI-based decision-support system, a common application of the technology, is fueled by quality data. Do you have it? This HBR.org article, “A Survey of 3,000 Executives Reveals How Businesses Succeed with AI,” will help get your organization oriented toward AI success.

    AI is likely to be a largely experimental for the next year to two years as organizations give it a shakedown cruise. But early adopters have had some success, and many see potential for AI to deliver revenue growth.

    Ultimately, AI and machine learning/deep learning will be looked upon as major advances with far-reaching effects, much like cloud computing and the internet. In 2018, it may not seem that way as enterprises experimenting with it feel the pain of separating the hype from the reality, adjusting business processes to make it work, cultural resistance from tech teams and a lack of available talent. Stick with it to reap eventual rewards.

    Prediction 3: IoT—A Thousand Points of Entry?

    Enterprise implementations of IoT functionality are very popular these days. But security vulnerabilities and issues are both inherent and legion with this technology. It simply hasn’t been thought through. In 2018, the disregard for IoT security solutions will come home to roost for several major corporations. Until the security issues are resolved, IoT will begin to be seen by some, at least, as a promising technology that’s not ready for prime time.

    Prediction 4: The GDPR Powder Keg

    By the end of 2018, some companies will be getting out of European markets, while others will look for a means of circumventing the EU’s GDPR. Still others will be scrambling to comply. And a last group will be doing what they’re doing now: taking the risk that the cost of the penalty is less than the cost of compliance. Forrester predicts that 80 percent of firms will not fully comply with GDPR. The unfortunate part is that, while a company may be able to ride out a steep fine on non-compliance, damage to its brand may be irreparable or very slow to heal.

    Companies also can’t just throw money at GDPR—it probably will require new companywide policies and a shift in cultural thinking about personally identifiable data and the individuals behind them. Some companies are going to find it very difficult to make that cultural change.

    Prediction 5: Cybersecurity Needs New Thinking

    Customer experience and security are at odds with each other. Passwords don’t work. Biometrics aren’t completely safe. Security awareness is treated as an afterthought. Security breaches are an everyday headline. What’s it going to take to make society wake up? There is a huge need for a new type of user authentication system, as just one example. There are significant opportunities for those that can deliver new technologies, new products and new solutions. We need to think differently about cybersecurity. We need to teach it in schools. In 2018, we will hit bottom. It will become apparent to all of society that our security measures are antiquated and have no hope of meeting the rising threat from many quarters. As the digital world continues to assimilate more and more of our previous analog way of life at a rapid clip, security has trailed far behind. Cybersecurity desperately needs innovation.

    — Scot Finnie

  • AI Delivering on the Business Analytics Promise

    AI Delivering on the Business Analytics Promise

    There’s a revolution underway in data analytics that could create a competitive advantage for companies that jump on it. C-suites, take note: AI-powered business analytics is finally delivering future-facing data points that CEOs need to guide their businesses to the sweet spot.

    The artificial intelligence (AI) technologies machine learning and deep learning are being used today to power through mountains of big data, much of which is previously inaccessible unstructured data such as text, video, customer reviews and customer feedback. Data scientists analyze the trends the machine learning uncovers, resulting in human-vetted, AI-derived, predictive analytics that provide strong decision support.

    Companies using AI-enabled business analytics are seeing positive results, yet most believe we’re just at the tip of the iceberg in utilizing AI’s capabilities in decision-making. Indeed, almost 70 percent of the CIOs who responded to a recent survey said they believe decisions made by machines will soon be more accurate than those made by humans.

    “Machine learning and deep learning have fundamentally changed enterprise data analytics,” said Tom Wilde, CEO of machine learning solution provider indico. He pointed out that a large majority of data is unstructured; in fact, according to some measures, as much as 80 percent of enterprise data today is unstructured. “In many cases, the unstructured data is where the real signal can be found,” and, as such, AI identification and categorization techniques can be used to develop analytics that could be used to improve a company’s product, predict future sales, identify cross-selling opportunities and reach new markets.

    “Machine learning supercharges business analytics and data science,” said Scott Zoldi, chief analytics officer at FICO. Machine learning algorithms explore relationships and data that even the best data domain expert may not think to explore, but, properly used, “Machine learning augments data scientists to make their discoveries seem superhuman. Data scientists that use machine learning will ultimately drive differentiated business value for their organizations.” In other words, AI is like using newly invented night-vision glasses for deep dark data—it should give a company a tactical advantage.

    What C-Suites Should Know about AI and Analytics

    AI and business analytics may seem like a panacea for what ails a business. But it’s not a plug-and-play technology—AI requires constant care and feeding to churn out the trends behind the data. Here are three things the C-suite must understand before adopting AI as a business driver:

    1. AI in its current form is unlikely to replace a human being. It has transformative power, yes, “but AI is going to further a lot of progress that’s already in place, not necessarily upend everything,” said AJ Abdallat, CEO of AI firm Beyond Limits, an AI company. AI will be used to create great things in years to come, but it’s not a magic bullet, it’s not infallible and it requires humans in great measure today. People should not be concerned about their jobs, nor should there be any expectation that AI might save money on personnel. These cultural concerns, along with a not uncommon resistance to change, are one of the barriers to entry.
    2. If your company punted on digital transformation, you’ll probably need an outside vendor to help you foray modestly into artificial intelligence. “The sooner you can modernize your digital infrastructure, the sooner you can bring AI in to address your [larger] business issues,” said Abdallat. In many ways, the ability to employ machine learning and AI may wind up being looked at as one of the main positive outcomes of digital transformation. Working in concert, these technologies are probably your best protection against sudden disruption by a digital-native startup with good ideas.
    3. “Too many CEOs delegate AI to their innovation teams, who conduct small-scale experiments. It’s important for CEOs to bring their own AI vision to their organizations,” said Gurjeet Singh, executive chairman and co-founder of Ayasdi. CEOs need to apply AI to the most pressing problems facing their businesses. “The world is going to change far faster than they can grasp. They need to set course immediately.”

    Adopt AI Soon

    One thing that has become clear as more companies join the AI fray, either as purveyors or consumers of the technology: The time for digital infrastructure improvements and adoption of AI is now. That said, the scope of enterprises that have already adopted AI is comparatively narrow. Companies currently using artificial intelligence to explore big data include the obvious: Amazon, Apple, Citi, DoCoMo, Facebook, Google, HSBC, IBM, Lockheed, Microsoft, Mt. Sinai, Tesla and Twitter. Many other companies are experimenting with AI in one facet of their business.

    How significant a trend will the use of AI by enterprises be? Is it at the scale of networking, the internet and cloud computing? “AI will have the same or greater impact on businesses for the next 20 years,” Wilde predicted.

    “Part of me wants to quash as much of the AI speculation as possible. The hyperbole,” Abdallat added. “But frankly, I do think that AI is potentially more transformative [than the internet, cloud etc.] … I know I’m a little biased in this, but we might be entering the era of the cognitive corporation, when man and machine at all levels of the enterprise will [cooperate] to solve problems, each learning from the other.”

    — Scot Finnie

  • Strap in, CIOs: Analysts Predict Wild Ride for 2018-2020

    Strap in, CIOs: Analysts Predict Wild Ride for 2018-2020

    Digital transformation is the underlying theme of many of the 2018 and beyond IT predictions made recently by analyst firms Forrester, Gartner and IDC. Technologies such as cloud, AI, data analytics, big data, blockchain, IoT, conversational interfaces, DevOps and cybersecurity will also force CIOs to perform a juggling act. The rapid evolution of these technologies combined with the business opportunities they unlock will create a pressure cooker for CIOs, become an everyday topic of discussion for C-suites and possibly even a preoccupation of boards of directors.

    We’ve analyzed the predictions by the three research firms and have gathered the key themes as the seven most pressing issues for the C-suite next year and beyond:

    1. CIOs are at a Crossroads, Representing Both Challenge and Opportunity

    Forrester believes that CEOs will make digital business a top priority in 2018 and CIOs will lead the digital transformation at most companies. In a November report, “CIOs Make the Chief Digital Officer Obsolete,” Forrester summed up: “In 2018, boardroom discussions will focus on digital. Key questions will revolve around platform-based business models, new partnership ecosystems, and new ways to organize for success. CIOs will build new skills and competencies to help drive this change.”

    The challenges for CIOs during this period will demand that they step up, expanding their digital skill sets and employing technologies with both eyes focused on business value. Forrester predicts that CIOs will need to orchestrate the underlying technologies and supplier relationships for companies to manage their platform-based business approach. CIOs would also be smart to leverage cloud computing and open source software, and build on containers, APIs and microservices.

    CIOs also may need to develop new leadership skills to direct their company’s digital transformation. That includes displaying a laser-like customer focus, attracting the necessary talent, restructuring their organizations and developing the culture required to deliver tangible business results. Doing so, Forrester said, “will present CIOs with the opportunity of a lifetime, allowing them to demonstrate the level of trust and performance that their senior peers and boards require, and positioning them as leaders of the digital transformation — even the enterprise.”

    IDC’s November “Worldwide CIO Agenda 2018 Predictions” offers predictions that dovetail with Forrester’s view:

    • By 2020, 60 percent of CIOs will implement an IT business model and culture that shifts focus from IT projects to digitally oriented products.
    • By 2020, 60 percent of the CIOs who have crossed the digital divide will prevail in C-suite turmoil in competition to become digital business leaders for their enterprises.

    Advice to CIOs: Find a leadership coach. Choose one or two lieutenants you trust to delegate important roles to. Find a senior product manager-type person in your company to help you become more knowledgeable about generating business value. Make a point to talk to your CEO about the advantages of digital transformation. Develop a plan for adopting a new digital governance model. What should your digital platform look like?

    2. Digital Transformation Becomes Real for Most Companies in 2019-2020

    We’ve been hearing for a couple of years that companies are digitally transforming their businesses. The truth is that those companies represent the leading edge and they are a small minority. Most CIOs are still stuck doing more with less, the hangover from the last recession. CEOs: Your CIO needs more budget to position your company for digital success over the next few years. IDC predicted that “through 2019, 75 percent of CEOs and their enterprises will fail to meet all their digital objectives, dragged down by conflicting digital transformation imperatives, ineffective technology innovation, cloud infrastructure transition and underfunded end-of-life core systems.”

    That’s a lot of failure. CEOs and CIOs are going to need to work together to make digital transformation a reality. It’s not just about updating your technology, it’s about business innovation.

    Key points on digital transformation from IDC’s CIO 2018 predictions:

    • By 2019, 60 percent of CIOs will complete infrastructure and application replatforming using cloud, mobile and DevOps, clearing the deck for accelerated enterprise digital transformation.
    • By 2019, 60 percent of IT organizations will have deployed digital transformation platforms supporting new customer- and ecosystem-facing business models.
    • By the end of 2019, digital transformation spending is expected to reach $1.7 trillion worldwide.

    3. AI is No Panacea

    Forrester named one of its 2018 prediction reports “The Honeymoon for AI Is Over.” Conventional analytics are undergoing full disruption, largely because AI is perceived to be the solution to both programming analytics and solving the big data riddle. And while it’s likely that AI/machine learning will be able to help with both problems, you can’t just snap your fingers and make it so. Harnessing AI successfully requires specialized engineering talent, quality data, objective planning and some trial and error. According to a Forrester survey, 70 percent of respondents say their firms will have implemented AI in the next 12 months. They chose decision management as their most important use. One of the key points to remember is that the decision-making abilities of it machine learning tool can’t over calm a lack of quality data. Forrester warns: “Don’t be fooled by vendor claims that more intuitive human-to-machine interfaces are [a magic potion] that reduces the need for data and metadata management and curation.”

    Quick AI predictions from IDC:

    • By 2019, 40 percent of digital transformation initiatives will be supported by cognitive/AI capabilities, providing timely critical insights for new operating and monetization models.
    • By 2020, 85 percent of new operation-based technical hires will be screened for analytical and artificial intelligence skills, enabling the development of data-centric digital transformation projects without hiring new data-centric talent.

    4. One-Third of Enterprises will Abandon Big Data Repositories in 2018

    Has your company built a large data lake that hasn’t delivered any significant business value? Well, 2018 may be the year when you rethink that decision, according to Forrester.

    On the other hand, Forrester believes that AI will be able to resolve the longstanding issue of the boundary between structured and unstructured data because of vendors including Digital Reasoning, Expert Systems and Stratifyd.

    As you make your move to digital transformation, having more actionable data based on your pool of big data, especially unstructured text data, could deliver book valuable business insights. This might not be the best time to give up on big data. Especially if you’re actively engaged with AI.

    5. Blockchain: Confusing, Skills Gap, Steeped in Hype—but Full of Potential

    Blockchain will begin to show next year just how difficult it will be to employ but, at the same time, that it’s worth exploring.

    “By the year 2020, the banking industry will derive $1 billion of business value from the use of blockchain-based crypto-currencies,” states Gartner’s October “Top Strategic Predictions for 2018 and Beyond.” “When compared to the estimated $7.6 trillion that is the industry gross output of the world’s banking industry, $1 billion may not seem like much. However, the value is more about the tacit endorsement of crypto-currency as a legitimate option by the banking industry.” Cryptocurrency is just one application of blockchain.

    Advice: Build your blockchain ecosystem early, Forrester recommends in its 2018 prediction report, “Be Ready to Face the Realities Behind the Blockchain Hype.” “The blockchain terminology will remain confusing,” Forrester noted. “The term ‘blockchain-inspired’ is already a common descriptor for software that has some of the characteristics of blockchain or reflects some of the principles of the blockchain network, but isn’t a blockchain in the true sense.” There is a growing skills gap pertaining to software engineers and security professionals who have experience with blockchain. It’s a nascent technology; make sure your team sets realistic goals.

    6. IoT Hits Prime Time, but it Might Not Be Ready

    By 2020, IoT technology will be in 95 percent of new product electronics designs, Gartner said. IoT’s growth is very rapid, even for the computer industry. But that growth comes at a cost. The technology has not overcome what many believe to be rampant security vulnerabilities. “Through 2022, half of all IoT security budgets will be spent on fault remediation, recalls and safety failures rather than protection,” Gartner predicted.

    Forrester is far more positive about the business advantages of IoT in its report, “IoT Moves from Experimentation to Business Scale.” Its predictions are also more detailed in the several areas where IoT might gain traction in 2018. But the security issues are not lost on Forrester, either. The research firm predicted: “IoT will be at the center of broader and more damaging cyberattacks. The Mirai botnet was a harbinger of future IoT security-related attacks and breaches. … Most firms still haven’t shown a consistent commitment to mitigating IoT-specific threat vectors.”

    Advice: IoT is an accident waiting to happen. For B2C companies, the consumer backlash could be fierce if there is a major incident. Make 2018 be the year that you start plugging up those IoT security vulnerabilities and protection gaps.

    7. Most Companies Won’t Comply with GDPR in 2018

    The European Union’s General Data Protection Regulation (GDPR) takes effect May 25, 2018. Forrester predicted in its November report, “A Year of Reckoning,” that 80 percent of companies will fail to comply with the GDPR. Perhaps more to the point, Forrester additionally predicts that 50 percent of those non-compliant firms will do so intentionally, “meaning they have weighed the cost and risk, and are taking the path that presents the best position for their companies.” The other half of the 80 percent will try to comply but will fail.

    Advice: It seems likely that some large company will be made an example of. You don’t want that to be your company.

    Takeaways

    Those are the standout IT-related issues for the C-suite in 2018 and beyond. If you weren’t tuned in to some of them, knowing is half the battle right? Well, probably not. These are tough challenges. Each organization has its own strengths and weaknesses. But generally speaking, if you parse this list by investing time and budget on the first four while minimizing the potential downsides of last three, you’ll be doing very well.

    — Scot Finnie

  • IPsoft’s 1Desk Advances AI Labor Force Reality

    IPsoft’s 1Desk Advances AI Labor Force Reality

    IPsoft wants to put a digital face on the future of IT operations that will be infused by artificial intelligence (AI). Its recently launched 1Desk service desk combines the Amelia digital assistant with IPsoft’s Apollo virtual engineer technology to automate multiple backend processes.

    Allan Anderson, director of enterprise solutions for IPsoft, says 1Desk effectively combines two longstanding areas of IPsoft research to provide IT organization with an ability to manage IT at scale using digital personal assistants and virtual engineers.

    IPsoft has been providing managed services based on its Apollo AI framework for several years. The launch of 1Desk creates an opportunity for IT organizations to not only employ that AI framework on their own, but also combine it with a digital assistant technology christened Amelia that IPsoft has trained to handle a variety of technology and business process requests.

    Anderson says the virtual engineer AI technology developed by IPsoft make use of machine learning algorithms to continually monitor how humans complete a task. Armed with that data, those virtual engineers can then incorporate that information into their knowledge base to learn a process or fine-tune an existing one.

    A continuing shortage of IT skills in key areas is significantly reducing resistance to AI technologies being applied inside IT organizations—AI technologies may enable organizations of almost any size to operate IT environments at unprecedented levels of scale while minimizing human errors. IPsoft, says Anderson, expects virtual engineers to be employed as a digital labor force capable of performing tasks such as identifying and killing a long-running query to configuring and executing runbooks across the most complicated data center environments. To facilitate the automation of the those processes, IPsoft has developed a Digital Labor Studio tool that organizations can use to create and train a virtual agent.

    There’s no doubt that many of the tasks associated with managing an IT environment are repetitive. In fact, any repetitive IT task is arguably a ripe candidate for automation. How those advances will impact DevOps processes remains to be seen. It’s not difficult to imagine that each development team will be assigned its own digital assistant. The role of the IT operations staff then would evolve into one where a smaller group of people train and manage a pool of digital labor.

    IT professionals are generally used to the idea that IT will be used to automate a task that used to be part of someone else’s job. But now IT is being used to automate many of the tasks performed by IT professionals. It may take some time for them to first accept that idea and then evolve their skills accordingly. There will also be a need for some level of human involvement when it comes to IT. But instead of thinking in terms of managers and administrators, perhaps the brave new world will be one where everyone in IT is now the manager of their own digital force of labor.

    — Mike Vizard

  • AT&T Democratizes Acumos Code for AI

    AT&T Democratizes Acumos Code for AI

    AT&T and India IT outsourcing company Tech Mahindra announced that code the companies jointly developed to drive artificial intelligence (AI) applications is being contributed to the Linux Foundation.

    Mazin Gilbert, vice president of advanced technology for AT&T Labs, says the goal of Acumos is to not only democratize access to AI software, but also consume it as a microservice with a standard set of DevOps processes.

    Gilbert says AT&T decided to contribute Acumos to the Linux Foundation because it’s become clear the proprietary implementations of AI platforms will prove to be too difficult and costly to support. AT&T is addressing this issue because it currently has 10 proprietary AI platforms attached to its network and was looking at having hundreds attached within a matter of months.

    Making Acumos available as an open-source project should go a long way to reducing that complexity by providing a standard interface to integrating various AI platforms, says Gilbert. Acumos includes a standard mechanism for downloading AI code from an app store, bringing AI functionality to where data resides instead of requiring developers to load data into a proprietary cloud service to access AI functions. In the case of AT&T, that integration is accomplished via the company’s Indigo software-defined network (SDN).

    Gilbert notes that much of what passes for advanced AI today has been around for decades. Many of the algorithms being used to drive AI applications are now over 50 years old. The only real differentiation is how these algorithms are being combined to drive applications to access enough data to make the algorithms useful. Because of that simple fact, AT&T doesn’t believe giving Acumos to the Linux Foundation represents any major loss of intellectual property.

    As AI becomes more democratized, DevOps teams will manage massive amounts of distributed data pipelines. AI applications require access to massive amounts of data that must be consumed via a consistently applied information architecture. Sometimes referred to as DataOps, the pipelines created by operations teams enable the algorithms embedded in a developer’s application to continually learn about changes that impact the recommendations being surfaced by the AI engine.

    At this point it’s now only a matter of time before every application is infused with some form of AI. In many cases, the front end to those AI capabilities will be a voice-enabled digital assistant trained to optimize a specific processor or answer a broad range of capabilities. Applications that don’t provide these capabilities soon will be deemed archaic. The result from a DevOps perspective should be a massive series of rolling upgrades to infuse AI capabilities into existing applications or replace them with one that provide those capabilities. Regardless of how that is accomplished, without some form of integrated DevOps processes in place there can be no data for AI applications to consume.

    — Mike Vizard

  • The Need for Speed Demands New-Era APM

    The Need for Speed Demands New-Era APM

    Applications have become the primary conduit by which customers experience a brand, making the application experience critical to business success. As such, continuous delivery and highly available systems that deliver optimal end user experiences at lightning speed have become top priorities for organizations. Modern IT is all about speed, efficiency and customer experience. Simply put, companies that develop new applications and services the fastest will win.

    That’s where containers and microservices come in. Containerized microservice applications enable organizations of all sizes to approach the ultimate goal of continuous integration and continuous delivery. This accelerates everything within the application life cycle, from initial development to component updates and new functionality.

    To match the development speed that microservices and containers enable, Ops teams need tools that can also operate hyper-fast, providing an immediate understanding of the impact of changes made, real-time analysis of vast amounts of data across applications and technologies, visibility across a range of languages and infrastructures, and knowledge of how the application is performing for end users.

    In fact, we are at a critical inflection point in application performance management (APM), where a generational shift is needed to keep pace with the new hyper-dynamic application architectures consisting of microservices, containers and hybrid cloud environments. To fully understand the impact of this shift, we can examine the genealogy of APM.

    APM Evolution

    The first generation of APM, established at the end of the last century, collected statistics and metrics out of system and application components, providing a first generation of user-defined transaction profiling by using instrumentation technology. APM 1.0 required a lot of effort to implement and maintain, but helped to monitor and analyze the performance of newly developed 3-tier applications.

    The second generation of APM was driven by more distributed, service-oriented architectures (SOA) and the rise of more elastic, virtualized or cloud-based infrastructure. APM 2.0 added application topology discovery and could analyze distributed applications. User experience monitoring of web and mobile apps quickly became part of APM 2.0 as more businesses depended on the service quality of their applications. With a primary focus on application monitoring, not management, APM 2.0 required an even broader set of subject matter experts to configure and interpret monitoring data from dashboards and reports. Similar to APM 1.0, the second generation still required way too much human analysis and setup to fully understand application problems and derive actions.

    The continuous change in today’s modern dynamic applications makes it difficult, if not impossible, for those older tools to keep up. Throw in additional architectural challenges such as hybrid clouds, containers and orchestration (such as Kubernetes and Mesosphere) and the picture gets murkier, all in the name of high-speed delivery. Meanwhile, the microservice architecture and development strategy spawns wide technology diversity—each engineer can choose the programing language, middleware and databases best suited to code their individual microservice. Scale, complexity and constant change is the new reality for today’s dynamic applications.

    Wanted: A New Breed of APM

    In this environment, traditional IT monitoring tools won’t work. These new applications use a vastly larger set of technologies, must scale dynamically and are in a constant state of flux.

    In this effort to engineer for speed and efficiency, complexity and scalability have increased exponentially, requiring continuous monitoring and more visibility than ever before. As organizations embrace agile methodologies for development and containerized microservices for deployment, developers have shifted their thinking in technology choices. Developers now continually select the most optimal technology for their individual goals regardless of DevOps’s ability to configure, deploy and manage the specific technology.

    With several teams across the enterprise constantly publishing new software releases, understanding the impact of these new releases and changes to the overall environment has become a significant challenge. Today there is no single source of truth assisting communication between various organizations, resulting in what seems a wild west–like environment for IT. With so many groups and technologies in play, everybody seems to be speaking a different language, especially when it comes to delivering high-quality applications. In this environment, a new breed of APM  is needed that can provide a “Truth Machine”—complete with deep visibility across all application components in the technology stack.

    This new breed of APM must be automatic and dynamic to deal with the ever-changing world of applications running in containerized microservices. To deliver these critical capabilities, the tool must provide real-time visibility into dynamic, containerized, microservice applications, regardless of language or infrastructure. New thinking is required to meet the challenges of these complex, large and dynamic application environments; a new monitoring and management approach that can handle dynamism by:

    • Delivering real-time analysis of vast amounts of data across all applications and technologies.
    • Conducting continuous discovery and automatically mapping every component, host and container, (hosted on-prem, private cloud, public cloud), to the exact architecture of complete technology stack.
    • Visualizing the application architecture and delivering actionable suggestions that assure the operational health of systems and applications.
    • Providing real-time accuracy to visualize, structure and identify problems immediately.
    • Monitoring the complete set of diverse technologies that run in microservice environments.
    • Operating in a heterogeneous environment and automating deployment across nearly all programming languages.

    Artificial Intelligence for Next-Generation APM

    DevOps and IT organizations are struggling to match the speed of agile development, and this is creating exponentially more challenges around new application architectures. The only constant in these modern application environments is change. Whether change is due to microservice architectures, container orchestration or simply more agile delivery execution to effectively manage microservice applications, DevOps needs up-to-date visibility, a precise picture of the application’s structure and an immediate understanding of its health. To accomplish that, DevOps must be able to apply artificial intelligence to the act of monitoring modern dynamic applications.

    Rather than spending time configuring and reconfiguring monitoring tools, organizations must automate management steps with as little human setup and configuration as possible. To have complete control and visibility of their environments, regardless of language and infrastructure, organizations must use a modern application management solution that is automatic and AI-powered to deal with the ever-changing world of dynamic containerized microservice applications.

    About the Author / Pete Abrams

    Pete Abrams is a co-founder and the chief operating officer at Instana, the leader in application performance management for containerized microservices applications. Formerly vice president of customer centric innovation at AppDynamics, Pete has spent more than two decades in the technology industry focused on introducing new products to market and ensuring customer feedback and desired capabilities are incorporated into future product innovation. Connect with him on LinkedIn.

     

  • BMC to Infuse AI Across Software Portfolio

    BMC to Infuse AI Across Software Portfolio

    At its recent BMC Exchange event, BMC Software announced it will be infusing machine learning algorithms and other artificial intelligence (AI) technologies across its entire portfolio of IT operations software.

    The first instance of BMC software to make use of machine learning algorithms is the latest version of TrueSight AIOps platform, which ingests data from multiple data sources to automate processes across BMC service desk, IT operations and tools for managing DevOps processes. BMC also is working with IBM to employ the cognitive computing services known as the IBM Watson cloud service across its portfolio.

    In addition, the company launched TrueSight Cloud Cost Control, a tool for managing and tracking usage of cloud services across multiple providers, and BMC SecOps Policy Service, which easier to embed compliance and testing via an external service that gets included in their software development processes.

    Finally, BMC announced it has signed an alliance with Amazon Web Services (AWS) under which the two companies will work to accelerate the migration of application workloads to the AWS public cloud.

    Dan Streetman, a senior vice president for worldwide sales and marketing at BMC, says his company is developing a product strategy around three major themes:

    • IT organizations will require management tools that will work across multiple clouds;
    • As part of general “shift left” developers now expect to be able to programmatically invoke IT operations and security services; and
    • IT professionals now expect the tools they use to manage IT to have the same types of advanced capabilities that consumer applications provide via speech interfaces such as Apple Siri or Microsoft Cortana. Various IT and customer service desks also soon will use speech interfaces to provide a highly automated self-service experience to end users.

    In fact, given the complexity of the IT tasks that now need to be managed, Streetman says a race is now on between IT vendors to live up to those expectations. Much less of a concern, he says, is any notion that AI technologies might one day replace IT professionals that by and large are already overtaxed; rather, it is apparent that IT vendors are now being evaluated on their ability to maximize existing investments in IT professionals that are already in short supply. The real opportunity is to free IT professionals to enable them to concentrate on higher-value tasks.

    Naturally, it remains to be seen how various AI technologies will impact the management of IT. It certainly will take a while for IT administrators to put their faith in AI. But as those technologies continue to mature, there may soon come a day when IT professionals look back with wonder at all the things they used to have to accomplish one manual task at a time.

    — Mike Vizard

  • Machine Learning Is Working for CIOs, Vendor Survey Finds

    Machine Learning Is Working for CIOs, Vendor Survey Finds

    AI’s time has finally come, if a new survey commissioned by ServiceNow is to be believed. Its results showed that nearly 90 percent of CIOs are at least dabbling with machine learning, with 53 percent calling it an area of strategic focus for their companies. What’s more, almost 70 percent of the CIOs surveyed say decisions made by machines will soon be more accurate than those made by humans.

    The new survey was conducted by Oxford Economics on behalf of IT and enterprise software-as-a-service vendor ServiceNow. The survey, titled “The Global CIO Point of View,” was completed by 500 CIOs in 11 countries across 25 industries.

    The report also fits neatly with evidence from other sources that CIOs are in charge of digital transformation. Just under 75 percent say they lead their company’s efforts to gear up for full-fledged digital business. Business intelligence derived from data analytics has always been a pillar of digital transformation. But with some 85 percent of CIOs reporting that machine learning provided “substantial value” or “transformative value” to the accuracy as well as rapidity of decisions, it’s beginning to change the way some organizations operate, according to the report.

    Within three years, two-thirds of the respondents expect to be making an investment in machine learning. Today, one-third are investing. The size of spend as a percentage of the overall IT budget is relatively modest, however: something like 5 percent for machine learning.

    One of the reasons for the move to machine learning is that sometimes you just have to do things smarter. “We were heading to a tipping point almost where there was just so much work coming in,” said Dave Wright, chief innovation officer, ServiceNow, talking about IT shops. “There was just so much work coming in; the workload just continued to increase year over year. CIOs want to know, ‘How do I get to the point where I can deal with this, because I can’t constantly throw heads at it.’” That’s why the report advises that companies direct their early machine learning efforts at the most commonly used services, freeing resources for more rewarding efforts.

    Machine Learning’s Obstacles Aren’t What You Think

    Machine learning technology has matured so much in recent years that it’s no longer the prime obstacle for CIOs. Instead the challenges are outdated business processes, incomplete insufficient skill sets, lack of quality data, insufficient data sources, risk assessment conflicts and budget limitations. When data is siloed across the company, accessing all the right data for the best decision-making is not always easy or possible. There’s also some inertia built into the organizational structures of most companies; the notion of making business decisions based on machine learning (a.k.a. “decision automation”) may require some handholding, for example. Technology is not the problem.

    Early adopters to machine learning, which represent just under 10 percent of the survey sample, share some characteristics and ways of doing things: They prioritize talent. They are working on better business processes and have a road map for future changes developed by monitoring mistakes made by machines. The road map is also guided by the need to implement policies and processes that improve data quality. They have strong technology foundations, with an emphasis on data analytics, cloud, mobile and IoT. And they expect results. Nearly 90 percent of this 10 percent of early adopters expect decision automation to support topline growth.

    What may be more interesting is that, of the other 90 percent of the CIO respondents, nearly 70 percent expect to develop revenue from machine learning. Looking at the data behind this report, the CIOs who’ve gotten their feet wet with machine learning are very excited by its prospects.

    The report outlines five steps to achieve value from machine learning that are worth a quick summary:

    1. Build your technical foundation so as to improve data quality. Successful companies had already invested significantly in analytics, for example.
    2. Prioritize automation of the most common business use cases; they will return the greatest business benefit.
    3. Build an exceptional customer experience. If customers see the man behind the curtain in a machine learning-based tool, it may become a very negative experience for them. Spend extra time making the entire customer journey seamless.
    4. Attract employees with new skills and double down on culture.
    5. Measure and report. The benefits of machine learning may not be obvious to all. CIOs must set expectations, develop success metrics and build a sound business case to acquire funding. Build some success stories.

    — Scot Finnie

  • New Relic Delivers on AI Promise to Automate IT Operations

    New Relic Delivers on AI Promise to Automate IT Operations

    New Relic at a FutureStack 2017 conference unveiled an instance of its monitoring tools that employs machine learning technologies and other forms of artificial intelligence (AI) to provide advice on how to optimize an IT environment.

    Nadya Duke Boone, director of product management for New Relic, says New Relic Applied Intelligence (NRAI) will be made available as part of the company’s Digital Intelligence Platform. In addition, New Relic is making available three dedicated NRAI capabilities in the form of Radar, NRQL Baseline Alerting and New Relic APM Error Profiles services. These services are designed to help IT operations teams spot abnormal behavior across billions of pieces of data and predict where problems may arise. The services ultimately will surface prescriptive recommendations for problems before they occur.

    The goal, says Duke Boone, is to leverage New Relic’s software-as-a-service (SaaS) approach to IT monitoring tools democratize access to AI technologies. That’s possible, she says, because of the massive amount of data New Relic collects. That data is then fed into machine learning algorithms to detect patterns indicative of a variety of potential issues.

    Duke Boone says that intelligence is then delivered as a personalized Radar news feed that is similar to a social media news feed. That news alert might, for example, might inform an IT administrator that, based on characteristics of their IT environment, an issue is likely to arise. IT operations teams also have the option to integrate that Radar news feed with a collaboration platform such as Slack to alert the rest of the IT operations team to a potential issue.

    NRQL Baseline Alerting allows IT operations team to set thresholds for specific applications that when exceeded will generate an alert. New Relic APM Error Profiles allows IT administrators to launch queries against NRAI to detect potential issues that might arise from outlier applications in the IT environment, adds Duke Boone.

    New Relic doesn’t see AI replacing IT operations teams as much as augmenting them, Duke Boone notes. Most organizations can’t hire enough IT staff to manage their IT environment. As those IT environments become more complex, it’s becoming clear that IT professionals will need to leverage AI technologies to keep all the applications they deployed running optimally, she says.

    New Relic has been investing extensively in AI ever since it previewed these capabilities in the form of a Project Seymour initiative it revealed last year. Now the company and its rivals are locked in an arms race to develop and implement machine learning algorithms that should enable IT operations teams to manage applications at unprecedented levels of scale.

    In some quarters AI is viewed as an existential threat to the IT operations teams. In reality, AI will be used to automatically resolve low-level issues that collectively conspire to rob IT organizations of the time they need to focus on new innovative ideas that add value to the business. In fact, assuming many of those issues will be soon disappear it’s conceivable that all the drudgery associated with managing IT today made soon just fade away.

    — Mike Vizard

  • DevOps, Security, AI Convergence on Horizon

    DevOps, Security, AI Convergence on Horizon

    DevOps is regarded as practice that can drive performance advantages. Artificial intelligence is showing very real promise across the security market. Most public and private-sector organizations know that they must explore new and different ways of defending their networks against modern threats. These three factors make it clear that DevOps, AI and security are ripe for convergence.

    Defining AI

    Anyone reading this post doesn’t need me to explain to them what DevOps and security are; both of these categories are well-defined. However, AI is really in an emergent stage, with different types in use and many definitions floating around. Because of this, I think it is important to let readers know that in this post, AI refers to a class of technology that can:

    • Consume data, learn and get smarter on its own
    • Automatically identify and solve problems
    • Operate independently, without human supervision
    • Scale massively
    • Move at near real-time speed
    • Achieve levels of accuracy that are beyond human capabilities

    DevOps and Security are United

    DevOps and security have merged. For several years now, organizations have been leveraging automation technologies and DevOps practices to continuously deliver security and compliance policies across data centers and clouds.

    Further legitimizing the union is the fact that leading DevOps and automation players such as Puppet, Chef and Ansible have formed numerous partnerships that deliver effective security capabilities to their customers.

    There are, of course, other signs that show how successful the DevOps and security marriage has been. One of which is the emergence of the term “rugged DevOps.” It is now so widely recognized that it commands its own section on this site and is often the subject of discussion at several notable security conferences.  

    DevOps and AI are Dating

    The relationship between DevOps and AI is in the early stages, with the courtship between the two well underway. Examples of its progress are found in numerous blogs and articles, several of which include:

    DevOps and AI have certainly not yet intertwined as deeply as DevOps and security have. These examples (and others out there) do show they the two will continue to integrate beyond what we are currently witnessing.

    DevOps, Security and AI

    DevOps and security are wed. DevOps and AI are moving closer toward each other. It’s only a matter of time until all three get together.

    Because several DevOps leaders are already in the business of security, it’s logical to assume that a partnership between one of them and an AI-driven security provider will be what ultimately leads to a marketable solution.

    DevOps provider candidates with the potential to move on a partnership that can combine DevOps, security and AI have a raft of new and established security providers available to work with. Several of the startups in this class are recognized in the CB Insights AI 100 2017 infographic. Companies such as Cloudera (NYSE: CLDR), which is using AI and machine learning to solve security problems, are among the more established providers that may make for a perfect partner. 

    How quickly will the market see such a partnership? That remains to be seen. However, with DevOps now fully accepted as a methodology that can positively impact bottom lines, AI coming into its own, and the need for new and different ways of defending organizations against attacks at a critical stage, it seems almost certain that the convergence will be upon us sooner rather than later.

    — Joe Franscella