We are currently in a really weird space with regard to AI capability. For my AI friends, I will make the disclaimer that what we call AI today is not actually AI, it is more akin to LiSP “AI” than actual intelligence (and, in reality, is firmly somewhere in between). In short, we have great language processing tools that we call AI but that have no cognitive ability—but, man, are they useful.
And the suddenness with which the rest of the world realized they were useful was nothing short of astounding. One day we are using AI algorithms to process test results and filter security data; next thing you know, our business people are bringing up AI and how advanced it is in every meeting.
They’re not wrong, but we need to differentiate the technologies. The promise and danger of chatbots is not the same as the promise and dangers of AI in security (where it can be used by attackers as easily as defenders, btw). We’re going to have to work out the concerns and benefits in real-time, but we need to start by pushing back and making clear that it is not a panacea, any more than the last 50 technologies that came along. It has great promise to change the world, but so did VMs and Java and containers and …
This one seems bigger, but that’s largely because it came on so fast and with seemingly broad applicability. But in the end, it is good at processing large datasets and noticing both patterns and anomalies in the data. This is a great, hugely powerful tool, but for things with sparse data? Totally useless. This is critical to remember moving forward. AI that monitors a certain aspect of security in the organization can detect patterns and anomalies, but unless the dataset is massive, there will be a lot of false positives along the way, and a more traditional tool is preferred. If that same AI (same software, algorithm, etc.) is being trained on data from hundreds of companies? Sign the check, it’s probably going to be better and more forward-looking than your current solution.
And that’s the key. It took the entirety of the World Wide Web—including, I might add, The Gutenberg Project—to train the language processors, so unless that security AI has a massive dataset, it’s not likely ready for prime time.
In IT, look to AI today (and this will change relatively rapidly, so check back often) for large dataset processing and for advanced automation. Those are the two real use cases. More will come, but right now, the ability to find patterns in large datasets or to say, in real-time, “A+B+C = block that port!” are the biggest viable use cases.
Over the years, I have grown ever more skeptical of claims about removing the need for developers. Those claims have been made so many times that my friends and I have a stable of jokes about them. Java, XML, 4GLs, RoR, UI generation tools—the list goes on and on. And each one created even more code. I expect AI will be the same, but there is no denying it can offer initial frameworks, much like mobile UI generation tools do. We’ll have to see how much change that bit introduces.
And keep rocking it. While I hope my readers don’t need reminding not to upload their source code to any of these AI tools, I will point out that your (Dev and Ops) source is so proprietary and critical that employers freak out when it is uploaded to an AI chatbot. No one cares what is done with chatbot-generated source, but the DevOps team’s source is considered critical. Sleep well knowing that.
It seems IT practitioners haven’t realized yet the scale of digital disruption. Mired in the old IT thinking, they firmly believe that sophisticated tools are all businesses need to survive the ongoing digital disruption waves. Most of them warn, “Digital transformation is changing the way we do business, the way we deliver IT services must change, businesses need to adapt the way they work.” The problem is, there’s nothing new in what they offer to help businesses work the “right” way.
Indeed, the same old IT thinking’s practices are still applied with the illusion that deployment automation and configuration management tools on their own will make businesses agile, responsive to market opportunities, and therefore in line with the digital economy’s competitive challenges. That belief misses the point for two reasons: First, it’s an incorrect understanding of what business agility is, and second, it’s a misunderstanding of the global competitive environment’s transformation.
Turning your IT organization lean and agile is the competitive differentiator your business lines desperately have been calling for. Authentic DevOps implementation is the way to go. That’s the price to pay to keep up with the pace imposed by new leading-edge companies such as Google, Apple, Uber and the like.
The changing competitive environment forces businesses to focus on innovation, market responsiveness and timely revenue
A digital economy threatens the existence of all businesses. Yes, you read it right, their existence is at stake. And that fact is underestimated by IT vendors and consulting firms.
As illustrated, we’re shifting from business environments where established businesses (incumbents) control a large percentage of market segments to competitive contexts where, attracted by the amazing digital opportunities and the adoption of cloud which significantly reduces infrastructure expenses, a growing number of startups disrupt entire industries:
The proliferation of startups created by highly qualified unemployed seniors and creative entrepreneurs, coupled with the propagation of highly innovative digital services (e.g., Internet of Things [IoT] applications) substituting traditional services, have been disrupting the vast majority of industries.
Several examples substantiate that fact: the taxi industry shaken by the emergence of technology-driven companies such as Uber, the banking industry facing new breed of competitors including PayPal, Google and Apple; and the healthcare industry increasingly disrupted by companies such as GlowCap.
To grow and prosper in the digital era, your scale, brand equity or market capitalization aren’t enough anymore. To succeed, your business lines must be on top of the digital economy’s competitive standards: competitive innovation (the ability to continuously improve customer experience), market responsiveness (the ability to deliver added-value services to the right market segments at the right moment) and timely revenue (the ability to continuously increase customer value).
The digital economy has taken IT to an inflection point—deploying sophisticated tools isn’t enough; being lean and agile is the way to go
The obligation for business lines to focus on innovation, market responsiveness and timely revenue is unprecedented for techies; it forces them to think beyond tools and address issues as unusual as organizational and operational effectiveness.
Indeed, innovation, market responsiveness and timely revenue assume greater cross-functional collaboration, IT focus on business priorities, organizational flexibility and accelerated time to market. Make no mistake, you’ll never achieve them by only deploying tools, but by also making your IT operating model flexible through proactively eliminating the organizational and operational dysfunctions that prevent cross-functional collaboration, fast problem-solving and decision-making.
That’s what is meant by making the business agile.
The new IT thinking is summarized in that simple arithmetic: Digital Competitiveness = Lean and Agile Operating Model + Effective and Efficient Tools. Without the lean and agile operating model, innovation and timely revenue are wishful thinking. Without effective and efficient tools, innovation and market responsiveness are wishful thinking as well.
DevOps is the IT asset businesses need to succeed in the digital economy
Contrary to a now (unfortunately) established belief, DevOps isn’t a technology; Chef, Puppet, Jenkins, OpsWorks, alike aren’t DevOps, they’re only pieces of it. It’s important to be clear about it.
As I’ve been implementing it, DevOps is a system of principles, practices, roles and responsibilities, governance structure and tools purposely arranged to establish cross-functional collaboration, enable accelerated applications delivery and, in turn, make the business responsive to market opportunities:
As illustrated, DevOps as a business capability is made up of three components: the Rules of the Game, the IT Operating Model and the Continuous Delivery Pipeline (CDP). Let’s explore them.
DevOps Rules of the Game create the conditions for lean and agile IT environments
Rules of the Game are a fundamental dimension of DevOps: They enable the establishment of lean and agile environments. I refer to them as the DevOps non-technological piece.
In practical terms, they’re a set of principles, practices, roles and responsibilities and tools institutionalized to ensure accelerated applications delivery.
They serve as catalysts to specific organizational and operational effective practices. Let’s take the principle, “Facilitate with system thinking (involvement of all relevant stakeholders)”—it forces application development stakeholders to adopt a collaborative mindset, which in turn breaks down organizational silos to eliminate the impediments to operational flexibility and institutionalize cross-functional collaboration.
They’re the poor cousin of today’s DevOps practices, which ignore them in favor of more technical and technology issues. That’s a terrible mistake that reduces DevOps influence vs. the digital economy’s challenges.
The agile IT Operating Model establishes the lean and agile work environment
The IT Operating Model reflects the interactions that take place across your application development value stream. In practical terms it represents the work environment derived from the application of DevOps’ rules of the game.
I always remind my clients that DevOps’ rules of the game aren’t theories, they’re the foundations of a tangible strategic capability: the lean and agile IT environment. They demand a thorough transformation of your application development work space to put together business, application development, and operations:
The lean and agile IT work space for the digital era illustrated (Credit: O.C. Tanner)
The picture illustrates what’s meant by lean and agile IT environment, it substantiates Adam Jacob’s point that “Fundamentally DevOps is about taking the behaviors and beliefs that draw us together as people, combining them with a deep understanding of our customers’ needs, and using that knowledge to ship better products to our customers.”
The IT operating model is the poor cousin of today’s DevOps practices which ignore it in favor of more technical and technology issues. Once again, it’s a terrible mistake that reduces DevOps influence vs. the digital economy’s challenges.
Make no mistake, technology matters, the CDP helps to meet digital technologies challenges
The importance of the rules of the game and the resulting IT operating model don’t suggest at all that technology doesn’t matter. Make no mistake about it, the CDP is essential; I refer to it as the DevOps technological piece. Through deployment automation and configuration management tools, it significantly accelerates application development, testing, resources provisioning and deployment.
However, one thing to be aware of is, digital economy raises technological challenges that transcend speedy delivery issues. They include:
The importance of IoT
The pivotal role of predictive analytics
The increasing importance of APIs
As a matter of fact, IoT devices generate massive data and making use of it to generate revenue requires big data technologies to integrate, automate and analyze them.
On the other hand, this ocean of IoT data has generated an API business; the idea is, to monetize their data and generate revenue, businesses must provide their customers and partners with APIs that easily give access to their systems.
Implementing the CDP as part of PaaS cloud services can help to prosper in the digital economy; it allows businesses to benefit the cheap and on-demand computing, storage and network resources provided by the cloud.
Takeaway
In hindsight, with a business-oriented perspective, the ITaaS delivery model and DevOps follow the same goal: Provide business lines with the lean and agile IT environments they need to be flexible enough and responsive to meet the competitive challenges of digital economy:
Like DevOps, the ITaaS delivery model (…) takes advantage of an agile IT operating model to help businesses succeed in the digital era. It implements consumer-provider relationships across the IT value stream to ensure organizational flexibility and operational agility.
CIOs must realize that what matters to the business is to continuously generate revenue through improving customer experience and increasing customer value. Deploying applications at the speed of light for the sake of speed doesn’t make sense from a business perspective; as long as expected business benefits aren’t achieved, speed is a useless gadget.
Implement DevOps as what it is: a three-dimension IT capability including rules of the game, IT operating model and, of course, continuous delivery pipeline.
Big Data, a relatively new phenomenon driving the industry hype-cycle is a mainstay in the database business. IT companies are convinced over the endless value proposition of the operational insight that comes with leveraging Big Data ( or Big Small Data ), so much so that it’s easy to overlook its impact on traditional database technologies and practices.
