devopsy.kinsta.cloud

Author: Scot Finnie

  • The Top 5 Ways DevOps Fails – and How to Prevent Them

    The Top 5 Ways DevOps Fails – and How to Prevent Them

    The working hypothesis for a Gartner report on how to avoid failure with your DevOps effort is that, unless the trajectory changes, “Through 2023, 90 [percent] of DevOps initiatives will fail to fully meet expectations.” The reason? You could boil it down to management not fully understanding DevOps tenets and nuances—things such as targeting business value, measuring success, meeting cultural/organizational challenges, fostering collaboration, understanding the inherently iterative process and managing for realistic expectations.

    DevOps is about people from multiple disciplines coming together to solve problems streamline workflows, develop new processes, break down silos to develop business value. “A lot of people fixate on the tools,” said George Spafford, senior director analyst, Gartner and co-author of the report. “They identify best-of-breed, lay out their toolchain and say, ‘Ta-da!’”

    The tools are important, but they are not the difficult part of DevOps. They are not a key obstacle. To succeed with DevOps, you often have to do something much more difficult: Change mindsets. Gartner identified these five top reasons for the failure of DevOps initiatives:

    1. Doing DevOps for DevOps’ Sake

    It’s important for DevOps initiatives to be grounded in generating business value. If the main goal is to achieve DevOps, you won’t be able to demonstrate sufficient value to sustain support. Failure to identify a business goal and focus the effort on attaining that goal can shut down the DevOps initiative  in only a few months. Identify business value by working closely with business stakeholders on a project-by-project basis to identify opportunities or obstacles and define the business value that will be developed. Avoid the temptation to rush DevOps implementation by taking too large a leap. Trust the DevOps process.

    2. Don’t Ignore the Culture

    DevOps can be intimidating in the early stages. Done right, it usually involves organizational change. People need to understand from leadership that this is not optional. They also need to understand what DevOps is and ultimately how it will affect them. Otherwise, what they imagine will tend to be much worse. It’s also of chief importance to communicate the business goals, both for instituting DevOps and the business value that you hope to generate. Senior leaders must recognize that DevOps teams represent “a fundamental shift away from traditional command-and-control hierarchical management models,” according to the report. “To improve agility, decision-making must move to where the information is.” Relaying information up and down the chain of command to make a decision isn’t feasible with DevOps. The teams need to be empowered to make decisions themselves. For that to work, key DevOps team members need to be fully aware of senior management’s strategic priorities.

    DevOps thrives in an open, honest environment where a frank exchange of perspectives is discussed, analyzed constructively by the group leading to decision-making. Many DevOps initiatives fail because they give short shrift to selecting people with the right temperament to help manage this process. Gartner suggests choosing people who you would describe as being team players, trustworthy, motivated, accountable, smart, experienced and effective communicators. Other words that come to mind include responsible, high emotional IQ, fair-minded, open-minded, aboveboard, not prone to casting blame, respectful and collaborative. In the end, for DevOps to work, you need to foster the type of environment people from multiple disciplines, with multiple agendas, can come together in a small team and get things done.

    3. What We Have Here is a Failure to Collaborate

    DevOps should not be limited to just IT and Ops. For it to succeed in generating business value, DevOps teams must work with other groups and stakeholders. This work “requires a systemic perspective and involvement, not uncoordinated silos,” according to the report. That said, while there is no magic number of DevOps team members, some have recommended five to 10 people. When you get into the range of 20 people, you may find that you DevOps effort is less effective. (Aside: It is possible to have more than one DevOps team.)

    In considering collaboration, don’t forget senior management. Look to gain the support of an executive who can help champion the effort. Finally, you know that DevOps is working when DevOps team members are collaboratively compromising deferring to each other’s needs. The trick is to solve multiple problems with every decision.

    4. DevOps is Iterative

    Too many companies get caught up in the notion that they can launch DevOps in a single bound. According to the report, historically, traditional transformation approaches have a high failure rate. “DevOps involves too many variables for this type of approach to be successful in a large IT organization,” Gartner noted. “An incremental, iterative approach lets organizations focus on continual improvements and avoid the risks” of what is often termed the “big bang” approach. DevOps by its very nature is focused on continuous improvement with an iterative process, rolling out smaller improvements more quickly. Any thought of launching it all in one fell swoop misses the point and shows a distinct lack of understanding about the principles of DevOps.

    5. Set Realistic Expectations

    DevOps and DevSecOps are enjoying the huge level of interest and popularity among enterprises. When that happens, the hype tends to overinflate expectations about the outcomes. Gartner advises that you manage expectations by agreeing on objectives and success metrics. Establish your starting point with whatever metric you adopt, then pursue the goal iteratively. According to the report, “DevOps is not a one-time effort; rather, it’s about trying over and over again.”

    — Scot Finnie

  • Study Reveals Pain Points for Enterprise Workers

    Study Reveals Pain Points for Enterprise Workers

    While it may be an afterthought at some companies, C-suite leaders would do well to keep an eye on workforce trends to optimize productivity and work quality.

    A study commissioned by Workfront, a work-management software provider, offers insights into what many enterprise workers consider to be obstacles to getting their work done. You already know that unnecessary meetings and mountains of email are on the list, but you might gain some perspective from some of the study’s other conclusions.

    Released recently, “State of Work 2018-19” was fielded by Regina Corso Consulting in June and gathered some 2,000 responses from U.S. employees who work for companies with at least 500 employees, use a computer on the job and collaborate with colleagues on projects.

    Enterprise Workers Have a Love-Hate Relationship with Automation

    Almost 70 percent of respondents believe that automation will give them more time to do their primary job responsibilities. And 86 percent of U.S. workers believe that the rise of automation will help them think of work in new and innovative ways. But half of the respondents also report that they know people who have already lost their jobs because of automation. Millennials are twice as likely as Baby Boomers to worry that automation will cause them to lose their jobs. Overall, what may be the most surprising data point is that the study’s findings clearly show workers to be generally optimistic about automation.

    Recommendation: Your company needs an expert or a task force out front, reviewing business process in all parts of the company, helping to formulate automation strategy, and finding the best ways to test and rollout automation solutions. Be ahead of the curve enough to give some thought to retraining people whose jobs might be affected for new roles.

    U.S. Workers Need Help to Foster Innovation Within Their Company

    Many are frustrated by the lack of prioritization and/or structure for innovating. Almost 65 percent of the respondents reported that their companies regularly ask employees to think about new ways of doing things. And almost 60 percent said that they’re barely getting their day-to-day work done and don’t have time to think beyond today’s to-do list. Millennials are more concerned with the importance of innovation and more frustrated by their inability to free up time to focus on it than Baby Boomers. Gen X falls in the middle.

    Recommendation: It’s not enough to just tell employees you want them to innovate. Innovation requires a different kind of thinking than tactical day-to-day decision-making. They may need brainstorm sessions. You might need to give them strategic problems to work on. You might need to suggest business processes that need streamlining. But more than anything else, you need to give them permission to take a specific amount of time per week or month to work on this longer-term problem.

    Workers are Skeptical of the Quality of Their Colleagues’ Work

    This has the potential to be divisive. U.S. workers are dubious about the quality of coworkers’ work and rate themselves as being more productive than those around them. Since the majority of respondents answered that way, you have to wonder what corrosive quality of the U.S. workplace is making people think that.

    Recommendations: Workfront offers some suggestions worth passing along: 1. Ask your team members what they think of each other. Use what you learn to guide you in the following steps. 2. Provide visibility into the work that everyone is doing so that team members realize what each member is tasked with. 3. Celebrate success and accomplishment. And spread that recognition around the entire team, if possible. This requires significant effort on the part of management, but the result is pride and mutual respect.

    C-Suite: The Key to Managing Multigenerational Teams

    Workfront has produced the “State of Work” survey for five years. While it hasn’t previously gathered trend information across years, one of the clear trends is the difficulty of managing the several generations in the workplace, each with its own set of priorities, said Chris O’Neal, product evangelist at Workfront.

    “For the first time in history, we’ve got four or five generations working alongside each other in the workforce. We’ve got baby boomers all the way up to gen z,” O’Neal said. “So, every company out there needs to come to terms with the concept of melding together a multigenerational workforce. And there are differences. In broad strokes, the way we look at it is that there are digital immigrants and digital natives. They’re all important; they all bring something to the table, whether it’s experience and leadership to adaptability and just-in-time skills.

    “We believe that this multigenerational workforce needs to be led by an executive team that understands that they have different ways of looking at the world, different ways of working,” he added. “They need to be brought together by shared purpose and shared values within the organization.”

    — Scot Finnie

  • NASA/JPL Tech-Incubation CEO Talks Artificial Intelligence, Part II

    NASA/JPL Tech-Incubation CEO Talks Artificial Intelligence, Part II

    This is the second installment of a two-part Q&A series on artificial intelligence (AI) with AJ Abdallat. It covers AI’s potential significance to computing; how AI affects jobs; the employee specializations needed to fuel AI; big data; and cultural challenges in the enterprise. Be sure to check out Part 1 as well.

    AJ Abdallat is a serial entrepreneur who, from 1998 to 2011, worked with NASA’s Jet Propulsion Labs (JPL) and Caltech on a series of tech incubation startups. The goal was to commercialize NASA technologies, many of which were artificial intelligence-related.

    In 2014, Abdallat co-founded and became CEO of Beyond Limits, a startup that secured licenses to NASA AI technologies for use in commercial applications. Beyond Limits currently holds exclusive licenses to 42 blocks of sophisticated intellectual property developed through NASA R&D investment, a $150 million AI technology head start under the aegis of Caltech’s Office of Technology Transfer and Corporate Partnerships program. Abdallat’s company is a leader in cognitive technology, which goes beyond conventional AI, binding deep learning and machine learning tools with symbolic AIs to emulate human intuition.

    We interviewed Abdallat while researching the story, “AI Delivering on the Business Analytics Promise.” With AI poised to potentially revolutionize business, Abdallat’s career, which blends AI science and commercialization of the technology, makes his insights fascinating.

    devopsy.kinsta.cloud: How does the advent of AI as used by enterprises compare to earlier significant computing milestones like networking, the internet, or cloud computing?

    Abdallat: Part of me wants to quash as much artificial intelligence speculation as possible. The hyperbole. But frankly, I think AI is potentially more transformative than the other things you mentioned, which are just infrastructure. AI is starting to help us pose the questions that we ultimately want to answer. What’s unique about AI is that it’s an introspective exercise. Every new success for AI is giving us some insight about how we work.

    AJ Abdallat
    AJ Abdallat is co-founder and CEO, Beyond Limits, an artificial intelligence firm. He’s an engineer and entrepreneur.

    As we integrate AI, the trend may not be that transformative initially. Improvements will likely be focused on doing it faster, bigger with lower downtime and more efficiency. But what comes next is thinking about all the humans involved in that process, and what they touch and how they work. I think once AI starts to integrate with that activity, and act at that level within the enterprise, this will truly change how we think about what a business is meant to do. I’m a little biased in this, but you might go so far as to say we are entering the era of the cognitive corporation, where man and machine at all levels of the enterprise will operate in a synergistic fashion to solve problems, each learning from the other.

    If you think about most business processes and most problems that humans find interesting to solve, they are heterogeneous. They move through a pipeline of different specialists that look at separate pieces or provide specific oversight to balance specialized objectives. If I can couple my engineering and sales activities at the enterprise level, and use AI to mediate the trade-offs of both of those objectives, and I have these people processes synchronized with machine processes, it gives me the ability to learn how these two potentially juxtaposed business ends are coupled, where they’re divergent, and what kind of switches I can flick or knobs I can turn to make the net positive of these two forces all the greater.

    devopsy.kinsta.cloud: Is artificial intelligence intended to replace humans or help them do their jobs?

    Abdallat: In some places, as with any kind of automation, you will see some jobs go away. But the labor that was there before finds a new role working with AI, and I think those new roles, especially at the level of knowledge work, will be very interesting.

    As good as artificial intelligence systems are, they’re not going to set our objectives. They can move toward them, and think outside the box, which is one of things that we work hard to do at Beyond Limits. But being able to deal with the nuances of problems is still something that humans excel at. So, finding the things that are worth teaching to AI, to learn and to figure out, that’ll still always be a job in the hands of humans. I imagine that’s also when it gets kind of cyclical. I’m going to learn something, the machine’s going to discover a new pattern, we’ll optimize an approach that I didn’t think could be optimized to such a degree. That’s going to give me new questions, which makes me give it new training data, and the process continues like that.

    devopsy.kinsta.cloud: What types of software engineers are now or soon will be in demand to support AI, machine learning and this new industry?

    Abdallat: One of the things we’re starting to see is a change in job definition. You probably hear about data scientists and machine learning scientists. What we’re going to see more of is taking that science and flipping it with engineer. There’s a ton of stuff in the laboratory that works and is great. And scientists can write papers and come up with nice coefficients for polynomials that solve problems. But at the end of the day, there’s an infrastructure that makes all this work. So, we will probably begin to see new job titles emerge, like data engineer. A data engineer is maybe the bridge between the work that the data scientist does and the infrastructure in which his data lives. He or she is much more of a software engineer but has some knowledge of the data science workflow and algorithms germane to data and AI work.

    Data engineers are becoming more important, and we’re finding they fit somewhere between IT and the business. I think that’s important. It’s the same with machine learning people. For example, the actuary of the future will simply be a data scientist who works in insurance. Systems analysts will gradually become systems data scientists. Of course, to make all this stuff work securely, we’ll probably see a ballooning of the specialized security people to support the work that goes into the systems engineering. Unfortunately, the world is more adversarial than we would like.

    devopsy.kinsta.cloud: Does AI present a cultural challenge to enterprises?

    Abdallat: Earlier big data solutions and the fact that AI looks promising for big data have paved the way. AI asks for similar machineries, although it can be used in smaller footprints when available. Culturally, some IT organizations are more progressive than others. Some like to play with new toys and would be stoked to get this down, assuming they have the budget, and AI may be enough to motivate them to allocate budget.

    In other cases, there may be entrenched opposition to change. But it’s such an exciting field that whenever you start talking about artificial intelligence, you’re going to get buy-in. Some healthy skepticism is a good thing too. You can try to channel it into constructive ways of alleviating the concerns that people bring up, and assess whether they’re just nay-saying the next new thing or they’re aware of a valid institutional or physical barrier to success.

    — Scot Finnie

  • NASA/JPL Tech-Incubation CEO Talks Artificial Intelligence

    NASA/JPL Tech-Incubation CEO Talks Artificial Intelligence

    AJ Abdallat is a serial entrepreneur who worked on artificial intelligence (AI) projects and commercializing smart sensors at the NASA Jet Propulsion Laboratory at California Institute of Technology (Caltech) from 1998 until 2011. Later he founded, launched and ran several spinoff companies from Caltech/JPL, doing AI work in support of the space program and various defense agencies. NASA, for one, has used AI in its deep space missions and on the Mars Rover for more than 10 years.

    In 2014, Abdallat cofounded Beyond Limits, which secured licenses to NASA AI technology with the goal of commercializing the technologies. Beyond Limits owns exclusive licenses to 42 blocks of sophisticated intellectual property (IP) developed through NASA R&D investment, a $150 million AI-technology head start under the aegis of Caltech’s Office of Technology Transfer and Corporate Partnerships program.

    Beyond Limits enhances the AI IP developed for NASA/JPL, and drives new IP and innovations through its mission to bring advanced AI technology to commercial applications. The company is an AI leader in cognitive technology that goes beyond conventional AI, binding deep learning and machine learning tools together with symbolic AIs to emulate human intuition.

    We interviewed Abdallat, who is CEO of Beyond Limits, while researching the story, “AI Delivering on the Business Analytics Promise.” He offered a lot of interesting commentary that just didn’t fit that earlier story. With AI poised to potentially revolutionize business, the fact that Abdallat’s career has bridged the gap of advanced AI science and commercializing it makes his insights fascinating on topics including digital transformation, IoT, big data and what AI can and can’t do.

    Note: This is Part 1 of a two-part series.

    devopsy.kinsta.cloud: What three key things about AI should every C-suite member know?

    Abdallat: No. 1 is understanding the transformative power of AI. Most of the AI out there today is not going to replace a human. There is a balance needed between understanding the promise of AI and not being unduly fearful of losing their jobs due to the much-publicized peril. AI is going to advance a lot of progress that’s already in place, not necessarily upend everything. So yes, there are transformative solutions in AI, but I think there are lots of places for it to plug in with what you’re already doing today, in ways that can considerably augment what is done, and free up time for individuals in your organization to turn their minds to the harder problems that AI hasn’t even cracked the shell of yet.

    N0. 2 is business practicality. For every AI algorithm, there are 100 articles telling you that it’s a good thing or a bad thing. It either doesn’t matter or it’s going to change your life. I think one of the things that’s important to realize is that AI needs to start with a problem to solve. So, being cognizant of which of your business problems have the biggest potential payoff in their resolution is how you should think about applying AI, as opposed to what AI would be good at solving. The chances are, if you have a problem, AI can shed some light on it. Prioritizing how you describe that problem will be more important than selecting the right AI hammer to swing at a particular nail. Understanding what your problem really is helps artificial intelligence experts devise solutions for you.

    AJ Abdallat, CEO, Beyond Limits
    AJ Abdallat is co-founder and CEO, Beyond Limits. He’s an AI engineer and entrepreneur.

    The third thing the C-suite should know about AI is timing. The time to start thinking about this is not now, it was yesterday. Companies that started experimenting with AI a few years ago are reaping the rewards today. One large, recent study shows that 75 percent of surveyed executives say that AI will be “actively implemented” in their companies within the next three years. So, it’s happening right now.

    A lot of people reading the news can’t help but wonder maybe today should be the day to act, because every day I read about something new. In fact, the sooner you can modernize your digital infrastructure, the sooner you can bring AI to address your business issues. You need to level the playing field with potential competitors. Today is the day to upgrade your networks to get more digital, get more instrumentation. The benefit is it will optimize a lot of what you’re doing today, even without AI, but it also makes the AI integration possible. AI can’t work if it can’t see the data.

    devopsy.kinsta.cloud: Does AI just pave the cow path or create a whole new business model?

    Abdallat: It depends on your perspective. AI can help to automate a lot of things, and that would be paving the cow path. But in general, AI can help humans see problems in new ways. Let’s say I have a bunch of data and I don’t know what’s important in it. This is the kind of thing where we used to have pre-formed opinions, where we would have a group of statisticians help us answer. Now, with an afternoon and an algorithm I can get you a hypothesis of what’s important in your data. Is that automation? I don’t know, it’s automation of ideation, which is certainly more esoteric, and that’s where we’re headed. But I think there’s probably room for both. AI will achieve new business models, but we’re also trying to get AI to do things we’ve been doing forever. It’s really a function of how. That’s what AI changes: how we do things, not so much why. Why you should change things is ultimately something the C-suite should determine.

    devopsy.kinsta.cloud: CEOs make decisions about the future. Could artificial intelligence help?

    Abdallat: Forty-six percent of executives are fearful that their business may get disrupted by an AI-powered startup, rather than a direct competitor. So, it’s a competitive necessity. Today, big companies are using AI for analytical, predictive, diagnostic and industrial control purposes. Some AI touches ordinary people through services, but they probably aren’t aware. What we’re looking at more and more is: How do I stay aware of a big stream of data and change my hypotheses as that stream comes online? There are companies looking at this, like Beyond Limits. It will become more common, but it’s going to be less about data at rest and more about data in motion. How can I quickly translate the data that I find for my search into some kind of action? That is the next big step for big data, analytics and artificial intelligence.

    devopsy.kinsta.cloud: People say lack of data and missing data are issues. Is that true?

    Abdallat: Yes. One of the things we bump up against is, What happens when I don’t have all the data, or we missed some of it? A lot of machine learning approaches are bumping into this problem as well, and what they need is human intuition or expertise to guide them over those initial stumbling blocks. That’s when the unconventional comes into play.

    IoT is an excellent example of missing data and so on. The internet of things (IoT) brings us new orders of magnitude of data that we need to analyze at higher frequency time scales than we’ve been asked to do before. IoT also forces us to do it out at the edge, away from the super computer that lives in the cloud that can help us answer a bunch of questions efficiently. It’s challenging to make decisions quickly enough at the edge without the ability to go back to the cloud. Because here, the speed of light without exaggeration is a factor. You’re not going to be able to get inside that decision loop if you’re waiting for light to travel back to the server farm and out to the edge again with the information needed to make your decision. You will have to bind some of the intelligence to the edge to be inside the control loop, so to speak.

    IoT also relies a good deal on sensor data that has far less redundancy. That means you’re going to get data drop out. On a single installation, some of the sensors just turn off. Others might be generating data that we know, with some prior knowledge, is out of spec; we know not to believe it. These are the kinds of problems you’re going to have when you interface with industrial machinery and lightweight distributed sensors at scale. The challenge is how to deal not only with the new stream of data, but also the inconsistency in its delivery.

    Watch for Part 2 of this series, which will cover big data/unstructured data, business process issues, human factors, and where AI is headed.

    — Scot Finnie

  • 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

  • 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

  • Study: CIOs Want a Chief Data Officer

    Study: CIOs Want a Chief Data Officer

    Quick, what does CDO stand for? Before you say chief digital officer, think again. A new study points to the chief data officer as the more important of the two titles, finding 82 percent of CIOs believe there is a compelling case to hire a CDO in most organizations. The survey, commissioned by Experian Data Quality, was completed by 200 CIOs and 50 chief data officers in June and released in August.

    But while the report stresses the importance of the CDO to companies that want to become data-driven, it also reveals severe challenges for some CDOs, including very high expectations, insufficient resources and, despite the C-level title, a lack of authority to get the job done.

    The survey states that the core responsibility of the CDO is “to define a data-management strategy and translate it into tactical, [achievable] steps for the business.” To make data actionable, it needs to be accessible, accurate and standardized. Here’s a simplistic example of why that is important: Let’s say a data analyst needs to create a report that isolates customers based in New York City. If some of them are listed as being headquartered in NY, NY and others are listed as based New York, NY and still others are in the database as New York City, NY, that’s not standardized. The data analyst would need to realize the data is splintered and needs to be stitched together to get accurate results.

    Making your business data-driven is part of the potential promise of digital transformation. CDOs and their ilk face a daunting task. Transforming data into business intelligence is the alchemy of this age.

    Chart courtesy of Experian Data Quality

    The study identifies three main drivers for hiring a CDO: capitalizing on big data opportunities, creating a competitive advantage and reducing risk on data-driven projects. These roles go far beyond the data management and data governance toward enabling business innovation. Over the next few years, the CDOs surveyed predict that their top business challenges will concern data security, keeping pace with technology advances and managing increasing user expectations.

    Perhaps one of the biggest challenges some CDOs face is the inconsistency across companies of the nature of their role and their stature within the organization. Bottom line: They may not have the authority they need to succeed. The survey reveals, for example, that the average tenure of a CDO is only two years. Many CDOs report that despite their C-level title, they are not considered to be a part of the C-suite. Although 37 percent report directly to the CEO, a larger percentage reports to the CIO, CTO or some other part of IT. That can create some friction because it’s important that CDOs are able to bridge the gap between IT and lines of business—and empower the business.

    Tips for Structuring the CDO’s role

    In creating a new CDO position, the C-suite should be advised of these common challenges faced by CDOs identified by the survey:

    • Staying within budget
    • Keeping up with evolving technologies
    • Knowing how to interpret information
    • Finding/hiring people with data expertise
    • Managing ever-increasing user expectations
    • Balancing the needs of business users and IT
    • Having a lot of responsibility with little room for error
    • Getting pulled into the weeds
    • Maintaining data security

    You should consider extending these responsibilities to your CDO:

    • Having significant control of the management and use of company data
    • Playing an active role in digital transformation
    • The responsibility for enterprise-wide governance and utilization of information as an asset
    • Manager of a team of experienced data employees
    • The purview to implement different approaches to managing data as an asset
    • The purview to generate business intelligence that unlocks and supports business innovation
    • The authority to be recognized by the entire company is the person overseeing data strategy and process
    • Recommend: At least a dotted-line report to the CEO.

    — Scot Finnie

  • Amazon’s Macie: Machine Learning to Protect Data in AWS

    Amazon’s Macie: Machine Learning to Protect Data in AWS

    Macie is a recently launched security service from Amazon for its AWS customers. It works by identifying and classifying sensitive data such as personal information or intellectual property, continuously monitoring that data for anomalies and giving customers detailed alerts and dashboard readouts related to the access or movement of the data. Macie is currently able to protect data stored in Amazon S3, and support for additional AWS data stores will be available later this year, according to AWS.

    Netlix is an early adopter of Macie and so far has seen good results. “Since we started using Amazon Macie we’ve found that it is flexible enough to solve a range of information security challenges that would have previously required us to write custom code or build internal tools, helping us move fast with confidence,” says Patrick Kelley, cloud security engineer at Netflix.

    In addition to Netflix, Amazon lists Autodesk and Edmunds as early users of Macie.

    Amazon claims that Macie uses machine learning to automate the process of discovering, classifying and protecting data stored in AWS. Macie starts out by creating a baseline of data access behavior and then monitors for actions that might indicate risk, such as large quantities of data being downloaded, sensitive data being configured to be externally accessible or credentials being stored insecurely. Macie provides a console front end. It also delivers alerts and recommendations on how to strengthen your security.

    The results of Macie’s analysis of your data may help you better understand where sensitive information is stored and how it’s typically accessed, including user authentications, locations and times of day. It can identify data with high business value, including programming languages to detect source code, logging formats, database backup formats, credentials and API key formats. This is one of its more powerful benefits. Anything that helps you understand your data is a major plus.

    Amazon’s new AWS security service supports 20 alert categories designed to provide early warning on security and compliance use cases, such as high-risk data events, API keys and credentials being stored within source code, unencrypted backups containing credentials and early stages of an attack, including behaviors indicating lateral movement, persistence mechanisms, back-door accounts and enumeration of role privileges.

    Whether Macie’s pricing makes sense for your company will depend quite a bit on the type of data you’re using in AWS. The pricing also involves the data analysis so it starts out higher as Macie analyzes your data store. On the other hand, if you are continuously adding significant amounts of new data, it may continue to get more and more expensive. Check out the detailed pricing information for some insight. Amazon lets AWS customers try before they buy Macie with this test drive. You may also find its FAQ and documentation useful in assessing the tool as well as more details about AWS security.

    Analysis

    Any enterprise application that mentions the terms artificial intelligence or machine learning in its marketing materials sets off alarm bells these days. It’s not uncommon to find that such laims are more hype than reality. Enterprise buyers, beware. But Macie’s ability to classify data in risk terms is well within the state-of-the-art of machine learning today. There are no apparent flights of fancy or flowery marketing language involved. Amazon isn’t promising unrealistic things from its new AWS security service. In fact, Macie looks promising for the many enterprises that are using AWS.

    — Scot Finnie