DevOps offers significant advantages for IT operations by fostering a culture of collaboration and automation between development and operations teams. This integration leads to faster deployment of applications, as continuous integration and continuous deployment (CI/CD) pipelines streamline the release process, reducing time-to-market. Automation of routine tasks minimizes human errors and allows IT personnel to focus on more strategic initiatives. Moreover, continuous monitoring and real-time feedback loops enable proactive issue detection and resolution, enhancing system reliability and uptime. By breaking down silos and promoting shared responsibilities, DevOps also improves communication and efficiency across teams, leading to more stable and predictable operating environments. Overall, adopting DevOps practices results in increased agility, better resource utilization, and a more resilient IT infrastructure.

The Essence of DevOps

DevOps is a methodology that emphasizes the seamless integration of software development and IT operations. By promoting a collaborative culture, it breaks down traditional silos, enabling cross-functional teams to work in unison throughout the software development lifecycle. This integration is facilitated through practices such as continuous integration (CI), continuous delivery (CD), and the adoption of automation tools, all aimed at accelerating the deployment of high-quality software.

Transformative Benefits of DevOps for IT Operations

1. Enhanced Collaboration and CommunicationTraditional IT operations often suffer from a disconnect between development and operations teams, leading to inefficiencies and misunderstandings. DevOps addresses this by fostering a culture of shared responsibility and open communication. Regular interactions and collaborative planning sessions ensure that both teams are aligned with organizational goals, resulting in more cohesive and efficient workflows.
2. Accelerated Deployment and Time-to-MarketIn the competitive business environment, the ability to deploy applications swiftly is crucial. DevOps practices, particularly CI/CD pipelines, automate the building, testing, and deployment processes, significantly reducing the time required to bring new features or applications to market. This acceleration not only meets customer demands promptly but also provides a competitive edge.
3. Improved System Reliability and Stability System downtime can have severe repercussions, including financial losses and diminished customer trust. DevOps enhances system reliability through automated testing and continuous monitoring. Automated testing ensures that code changes are validated before deployment, reducing the risk of introducing errors. Continuous monitoring provides real-time insights into system performance, allowing for proactive issue resolution and maintenance.
4. Increased Efficiency through AutomationManual processes in IT operations are often time-consuming and prone to errors. DevOps leverages automation to handle repetitive tasks such as code integration, testing, and deployment. This not only speeds up processes but also minimizes human error, leading to more consistent and reliable outcomes. Automation frees IT personnel to focus on strategic initiatives that drive innovation and add value to the organization.
5. Cost Reduction Implementing DevOps can lead to significant cost savings. By reducing system downtimes through proactive monitoring and rapid issue resolution, organizations can avoid the substantial costs associated with outages. Additionally, efficient resource utilization and streamlined processes contribute to overall cost efficiency.
6. Enhanced Security and Compliance Security is a paramount concern in IT operations. DevOps integrates security practices into the development and operations processes, a practice known as DevSecOps. This approach ensures that security is considered at every stage of the software lifecycle, leading to more secure systems and easier compliance with industry regulations.
7. Scalability and Flexibility As businesses grow, their IT infrastructure must scale accordingly. DevOps practices support efficient scaling of applications and infrastructure. Through automated configuration management and infrastructure as code (IaC), IT operations can quickly adapt to changing demands, ensuring optimal performance under varying workloads.

Implementing DevOps in IT Operations

The successful adoption of DevOps requires a strategic approach that encompasses cultural transformation, process reengineering, and the deployment of appropriate tools.

1. Cultural TransformationFostering a culture that values collaboration, continuous learning, and shared responsibility is fundamental. Encouraging open communication and breaking down silos between development and operations teams lay the foundation for successful DevOps implementation.
2. Process AutomationIdentifying repetitive tasks and implementing automation tools can streamline workflows. Automated testing, deployment, and monitoring not only enhance efficiency but also reduce the likelihood of human error.
3. Continuous Integration and Continuous Deployment (CI/CD)Establishing CI/CD pipelines enables frequent and reliable code deployments. This practice ensures that code changes are continuously tested and deployed, facilitating rapid delivery of new features and bug fixes.
4. Infrastructure as Code (IaC)Utilizing IaC practices allows for the management and provisioning of infrastructure through code. This approach ensures consistency, reduces configuration drift, and enables efficient scaling of resources.
5. Continuous Monitoring and Feedback Loops Implementing comprehensive monitoring provides real-time insights into system performance. Establishing feedback loops ensures that information is continuously fed back into the development process, facilitating ongoing improvements and quick issue resolution.

Challenges and Considerations

While the benefits of DevOps are substantial, organizations must be mindful of potential challenges:

• Resistance to ChangeTransitioning to a DevOps culture may encounter resistance from staff accustomed to traditional workflows. Addressing concerns through training and clear communication is essential to facilitate acceptance.
• Tool IntegrationSelecting and integrating the right set of tools that align with organizational needs can be complex.

Few years back we use to talk and learn about software development lifecycle(SDLC) and the process it goes from requirement → designing → development → testing → deployment → and then maintenance. We were implementing the waterfall model moved to iterative model then to agile model of software development to DevOps.

But, now organisations are trying to integrate AI/ML into their process. This new concept of building ML systems adds/reforms some principles of the SDLC to give rise to a new discipline called MLOps.

MLOps is communication between data scientists and the operations or production team. It’s deeply collaborative in nature, designed to eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning.

This concept is deeply collaborative in nature between data scientists and the operations or production team, designed to eradicate waste, automate as much as possible, and produce richer, more consistent insights with machine learning (ml).

Who should learn MLOps?

• DevOps engineers
• Data scientists
• Data engineers
• ML engineers
• Data and Analytics Manager
• Business Analysts
• IT/Software Engineers
• Model Risk Managers/Auditors

What are the prerequisites to learn MLOps?

Basics of DevOps & Machine learning will help.

What are the benefits of MLOps?

• Reduced time to market of AI-driven products
• Enhanced user experience due to the fact that apps get timely updates
• Higher quality of predictions
• The ability of data engineers to focus on building new models — instead of diving into deployment routine.
• Rapid innovation through robust machine learning lifecycle management.
• Creation of reproducible workflows and models.
• Easy deployment of high precision models in any location.
• Effective management of the entire machine learning life cycle.
• Machine Learning Resource Management System and Control.

Why we should learn MLOps?

• Machine learning techniques have finally achieved enough effectiveness and practicality for mass adoption in companies and institutions. MLOps practice is going to be next buzzword of the industry.
• MLOps is still emerging – most companies are in the early stages of understanding and proficiency of this concept. The potential for MLOps professionals in this area, therefore, is enormous.
• ML-based solutions and MLOps become are going to be a crucial for businesses, the trend is for the degree of specialization to increase.
• In the recent four years, the hiring for MLOps roles has grown upto 74% annually.
• MLOps is predicted to grow rapidly in the coming years and is estimated to reach up to $4.5 billion by the end of 2026.
• MLOps being a new field, is witnessing a shortage of skilled professionals.

What is the future of MLOps?

During the few short years MLOPS has grown in popularity, several open source frameworks have emerged. In a move that shows the importance of this practice, as data and technology continue to expand and reach new heights, ML will help organizations of all types develop strong strategies, manage and succeed in the future.

How DevOpsSchool’s MLOps course/Training would help?

DevOpsSchool has a team of passionate instructors, educators, trainers, and mentors. We are highly motivated who build fresh and lasting learning experiences for our participants. Powered by our innovation processes, we provide an environment where learning is easy, constructive, and fruitful.

Reference:- https://www.devopsschool.com/certification/mlops-training-course.html

This MLOps course is a program which tackles the subject of deploying the Machine Learning models in production and at scale. Our MLOps course will help you to learn – best MLOps tools, techniques, and practices for deploying, evaluating, monitoring and operating production ML systems end-to-end. As part of this course, you will learn to deploy models into production environments using cutting edges open-source frameworks.

The productivity of cloud computing ordinarily envisages that IT operations for a company should have as many managers as possible. Numerous public cloud players have emerged to help different necessities “as-a-service,” from a simple virtual instance. “CloudOps” is an advancing term and pattern that endeavors to guarantee that the advantages of cloud-based frameworks can be very much protected without IT operations losing control.

Cloud operations management is a process that takes care of planning, managing, controlling, and eventually redesigning cloud operational processes. This includes the management of both software and hardware as well as network infrastructures to encourage a productive and lean cloud.

IT tasks, operations and strategies usually are manual, repetitive and unbending to assure that changes are controlled and perceived to forestall unplanned blackouts. Automation of IT processes and operations becomes as crucial as any environmental shift towards agile infrastructure where speed is viewed as everything. A situation like this is especially appropriate for cloud conditions where the framework is virtual and scalable, with users requesting instant access and full control of deployed services.

Numerous organizations’ IT teams have moved to the cloud incredibly, and they currently have a blend of cloud setups—regardless of whether they call it hybrid IT, hybrid cloud, or multicloud. The approaching X-factor is the way to deal with the resulting complexity. They’re prepared to set up standard working systems for long-term cloud operations.

Cloud operations and management can be perceived through different qualities like business process management, cloud infrastructure and capacity planning, infrastructure monitoring, performance monitoring, and incident management. Cloud tasks offer huge flexibility and fabulous options compared to traditional computing or storage options.

The cloud requires less hardware than customary computing structures which give more noteworthy flexibility and a lower cost. Proper planning of capacity prevents you from paying for assets that aren’t being utilized efficiently and saves you from pointless expenses. A cloud-based process management offers a wide range of groundbreaking capabilities within the company as well as stretches out to investors, customers and partners.

Since cloud systems are out of the direct actual control of operations teams, proper consideration should be taken by the way they do tasks. Often, the operations team is reliant upon the cloud supplier for all data, and the supplier plans and executes most platform-related fixes.

This absence of control really adds to the benefit of utilizing the public cloud. You turn over a large portion of the direct control to the public cloud supplier, which ordinarily includes giving up the tedious control of platforms down to the crude metal. In uncommon situations, it’s conceivable to keep up that control with some public cloud suppliers, however very few.

With continuous monitoring and regular updates, customers can accomplish a reduction in time with the assistance of observing and provisioning of the servers managed by the actual software. With regular monitoring, proactive and managed solutions are given to assess and expand the business objectives.

Continuous infrastructure monitoring defeats service disturbances like system outages, performance bottlenecks, and application failures, which are extremely hard to recognize in the n-level modern-day application stack. Incident Management gives a solid way to address SLA episodes by covering viewpoints to service runtime in the cloud with the assistance of monitoring and analysis of occasions that may not cause SLA breaches.

A report by the Cloud Industry Forum inferred that UK companies dedicate 19% of their IT budget plans to cloud framework, only in front of the 18% spent on-premises infrastructure, and that by 2022, only 12% of IT budget plans will be spent on legacy technology, as cloud utilization increments. Yet, conventional frameworks actually comprise the majority of systems that run businesses.

Overview: The Data-Driven Decision Making Series

Software delivery organizations must make fast decisions across three core activities: Product Management, Development, and Operations. The Data-Driven Decision Making Series examines how each of these activities can be guided by data rather than intuition alone:

• Product Management: Hypotheses steer the effectiveness of product decisions.
• Development: Continuous Delivery Indicators steer the efficiency of the development process.
• Operations: SRE’s SLIs and SLOs steer the reliability of production services.

Applied together, these three frameworks allow a software delivery organization to optimize for effectiveness, efficiency, and reliability simultaneously — without siloing these concerns into separate departments.

Why Data-Driven Product Management Matters

Building features that customers never use is one of the most expensive mistakes a product team can make. Feature prioritization is often driven by internal politics, loudest stakeholder voices, or gut feel — none of which reliably predict customer value.

Replacing opinion-based decisions with data from production requires a cultural shift: data must be treated as Process Indicators that describe what is happening in the delivery system, not as People KPIs used to evaluate individual performance. This distinction is critical. When data is used to measure people rather than processes, teams are incentivized to game the numbers rather than improve outcomes.

Leadership must set this norm explicitly. Without top-down support for treating data as a process lens, data quality degrades and the feedback loop breaks down.

Hypothesis-Driven Development (HDD): The Core Framework

Hypothesis-Driven Development (HDD) frames each feature as a scientific experiment. Before implementation begins, the team states what they expect to happen — and defines how they will measure whether it actually does.

A Hypothesis is expressed using a three-part structure:

We believe that this <Capability>
Will result in this customer <Outcome>
We will know we have succeeded when we see this <Measurable Signal> in production

Breaking Down the Three Components

• Capability: The feature or functionality being built. Describes what the system will be able to do.
• Outcome: The expected change in customer behavior or experience. Focuses on the user, not the system.
• Measurable Signal: A quantitative or qualitative indicator observable in production that confirms or refutes the Outcome.

This structure enforces a Build → Measure → Learn cycle. Each release becomes a deliberate experiment. The team does not just ship code — they also instrument the feature to collect the Measurable Signal, then evaluate results once the feature is live.

What This Means for Developers

Implementing Measurable Signals is not a product management activity alone. Developers must:

• Plan capacity for instrumentation as part of feature work — not as an afterthought.
• Apply Operations skills (logging, metrics, telemetry) during feature implementation.
• Treat the production environment as an active source of feedback, not just a deployment target.

This is where Hypotheses directly reinforce DevOps culture: the boundary between Development and Operations dissolves when developers are responsible for measuring what they build.

Hypotheses as a Bridge from Projects to Products

Many organizations are transitioning from project-based to product-based delivery models. Traditional project thinking scopes requirement engineering around development activities alone — requirements are written, features are built, and the project ends at release. What happens in production is someone else’s problem.

A Hypothesis with a Measurable Signal forces the requirement to include production evidence. The feature is not considered complete until the signal is observed. This naturally incorporates Release and Operations concerns into the requirement engineering process — which is precisely what a product mindset demands.

Additional benefits of Hypotheses in a project-to-product transition:

• Teams become more business-driven by focusing on Outcomes rather than output volume.
• Teams can operate with less management overhead, guided by Measurable Signal data rather than project governance.
• The current state of any feature is transparent through signal values, reducing the need for status meetings.

Real-World Application: Siemens Healthineers

The Hypotheses framework was introduced to the teamplay platform — a global digital healthcare service from Siemens Healthineers — across a distributed organization of 16 software delivery teams in three countries.

Enablement Approach

To support adoption, the team expanded their existing Business Feature template to include dedicated fields for Capability, Outcome, and Measurable Signals. They then ran focused workshops of 30–60 minutes with individual teams, taking a real in-flight requirement and reformulating it as a Hypothesis. After the feature was deployed to production, the team revisited it to evaluate the actual Measurable Signals.

This hands-on loop — define, build, measure — helped teams internalize the process quickly. Teams found that the Hypothesis definition discussions also improved the quality of their downstream requirement engineering, including Behavior-Driven Development (BDD) user stories, by forcing scope to be clarified early.

Example 1: teamplay User Administration

When the feature shipped, early quantitative signal values indicated that usage growth would take time to reach the defined thresholds. However, qualitative feedback from Sales indicated that customer onboarding had become significantly easier — an unexpected qualitative Measurable Signal that the original Hypothesis had not anticipated. This provided a basis for iterating on the Hypothesis definition itself to better reflect real-world customer value.

Example 2: teamplay Data Access Management

Adoption Observations

Teams responded positively to the Hypotheses framework from the outset. The process was easy to understand, and the definition discussions added immediate value to requirement scoping. However, implementation of Measurable Signals in production varied by team — only a subset fully instrumented their features initially. Increased release frequency was expected to accelerate adoption of production instrumentation over time.

One team encountered a notable edge case: during implementation of a Hypothesized capability, they discovered that an external framework had severe limitations that made the intended Capability impossible to deliver. Because the Hypothesis had defined the expected Outcome and Measurable Signal explicitly, the team could recognize early that the Outcome could not be achieved — and pivoted to evaluate alternative frameworks before shipping something that would have failed silently.

Documentation: Embedding Hypotheses in Your Toolchain

For Hypotheses to be consistently applied, they must be documented directly inside the tool where user stories are written — not in a separate wiki, spreadsheet, or document that teams forget to check. Extending your existing feature template (in tools such as Jira, Azure DevOps, or Linear) to include Capability, Outcome, and Measurable Signal fields ensures the Hypothesis is visible at the point where decisions are made.

Using Hypothesis Data for Prioritization

Once a body of Hypotheses has been tested in production, the resulting data becomes an input for prioritization decisions. This data arrives in two forms:

• Positively or negatively tested Hypotheses: Features that did or did not achieve their defined Measurable Signals.
• Unexpected insights: Qualitative or quantitative signals that were not anticipated in the original Hypothesis definition.

Product Owners and delivery leads can use this data to navigate trade-offs across three investment dimensions:

• Invest in new features to increase product effectiveness.
• Invest in development process improvements to increase efficiency.
• Invest in reliability work to improve service stability.

Translating Hypothesis data into consistent prioritization decisions requires practice. Teams benefit from explicit coaching on how to read and act on signal data — especially when signals are ambiguous or when qualitative and quantitative signals point in different directions.

Summary

Hypothesis-Driven Development gives product teams a structured, measurable approach to answering the hardest question in product management: are we building the right things?

Key takeaways:

• Express features as Hypotheses using Capability / Outcome / Measurable Signal triples before implementation begins.
• Plan development capacity for instrumentation alongside feature work — Measurable Signals require engineering effort.
• Treat Hypothesis data as Process Indicators, not as individual performance metrics.
• Embed Hypothesis documentation directly in your user story tooling so it stays visible.
• Use Hypothesis outcomes as a data input for ongoing prioritization decisions.
• Recognise that Hypotheses naturally enforce a DevOps mindset by requiring developers to think about production observability during feature design.

A team that consistently works with Hypotheses moves from being a feature factory to being a delivery organization that is accountable for the value it creates in production. Over time, this leads to higher user engagement, reduced waste, and more transparent — and less political — product decisions.

This article is part of the Data-Driven Decision Making for Software Product Delivery Organizations Series. Future articles cover data-driven decision making in Development (Continuous Delivery Indicators) and Operations (SRE SLIs and SLOs).

Automation Strategy Rethink
Hybrid IT Versus Disaster Recovery (DR) Confidence
Scaling DevOps Agility
Infrastructure Is Everywhere — So Is Your Data
Overwhelming Impact of IoT
Distributed Cloud
Immersive Experience
Democratization of IT
Networking — What’s Next?
Hybrid Digital Infrastructure Management (HDIM)
Splunk also published its list, which looks like this:

AI/ML/NLP
Blockchain
IoT/5G
Dark Data
IT Security
Social Engineering
Critical Infrastructure
Cloud Security
Threat Intelligence
IT Operations
Automation
UX/Consumerization
McKinsey also has a list:

Today In: Innovation
“As-a-Service” Consumption for Everything from Software to Hardware.
Mainstream Public Cloud
Increased Use of Open-Source Offerings, Up & Down the Stack
Cybersecurity
Mainstream Comfort with “White Box” Hardware
Internet of Things
Shift of the Hardware Infrastructure Market to Asia
DevOps for Software & Hardware
Container-First Architectures
Artificial intelligence & Machine-Learning
PROMOTED

Grads of Life BRANDVOICE
| Paid Program
Designing To Create An Equitable And Inclusive Talent Marketplace
ABB BRANDVOICE
| Paid Program
The Art Of A Smart City
Insights – Teradata BRANDVOICE
| Paid Program
How Pandora Knows What You Want To Hear Next
It’s helpful to look at these three lists and draw some insight into how three major vendors see the world. It would be difficult to find three vendors that have less in common than Gartner, Splunk and McKinsey, which is in itself valuable. Gartner “equips executives across the enterprise to make the right decisions and stay ahead of change.” Splunk “is the world’s first Data-to-Everything Platform.” McKinsey “help(s) organizations across the private, public, and social sectors create the change that matters.” Very different missions, to put it mildly. But all three have thought about what’s likely to happen to 2020 computing and communications infrastructures. So what do they think?

Here’s a chart that compares the areas according to who likes what:

Comparison Among Gartner, Splunk & McKinsey
Comparison Among Gartner, Splunk & McKinseySJA
Analysis

Industrial technology concept.
Industrial technology concept.GETTY
Automation is a favorite. Nothing surprising here. Blockchain is not. Nothing surprising here either. Hybrid IT Versus disaster recovery is interesting. According to Ross Winser (Senior Research Director at Gartner), as reported by Russ Weldon: “organizations are left potentially exposed when their heritage DR plans designed for traditional systems have not been reviewed with new hybrid infrastructures in mind. Resilience requirements must be evaluated at design stages rather than treated as an afterthought two years after deployment.” Good advice. DevOps is still popular. Everybody loves IOT and, though to a slightly lesser extent, AI/machine learning and natural language processing. Obviously.

The emphasis on cloud development platforms – Infrastructure as a Service (IaaS) – is growing – as it should. The very definition of infrastructure provisioning and management will grow as more “as-a-service” models emerge. In fact, we can expect the full integration of software-as-a-service, platform-as-a-service and infrastructure-as-a-service – along with all of the other as-a-service models that are now available and will be available soon. The overall movement toward – and therefore dependency upon – cloud providers describes an enormous opportunity and a corresponding risk – which is why cyber/cloud security is so important to cloud clients: cybersecurity, including cloud security, should preoccupy every infrastructure manager on the planet.

The cloud is also expanding to the edges of networks, so IT infrastructure managers (and CISOs) have to adjust their responsibilities (and SLAs) outward. Edge and hybrid cloud deployments only exacerbate infrastructure challenges – which should be front and center.

We can agree that the public cloud will grow, and more open source software will become part of the mainstream infrastructure architecture. But we should also agree that the spaghetti API world that lubricates infrastructure and applications will eventually crash under its own weight. Standardization should be a goal, though, as we all know, it’s impossible to achieve.

The Asian hardware invasion that only McKinsey predicts is unlikely. There are growing national security concerns and industry cross-pressures about products that might threaten the integrity of US networks and communications devices. It was reported as recently as November 22, 2019 that “American regulators voted to impose new restrictions on subsidies for American telecom companies Friday. The ruling is designed to constrain Chinese companies, including Huawei and ZTE.” This is techno-politico quicksand. CIOs and CTOs would do well to avoid stepping in it – even if their vendor choice is off the blacklisted vendor list – today.

Gartner’s emphasis on the democratization of IT is surprisingly ignored by Splunk and McKinsey. If we believe that PaaS will grow via cloud platforms, then the importance of low code/no code development capabilities increases. Gartner has this exactly right. Platforms like Microsoft’s Power Platform now enable near-no-code development of, for example, applications in robotic process automation (RPA). This is an irreversible trend and consistent with the growing infrastructure role that cloud-delivered PaaS will play going forward – and forever.

As suggested, everyone agrees that the management and support of the Internet-of-Things (IOT) and eventually the Internet-of-Everything (IOE) will impact infrastructure. Clearly, this makes sense. But the larger infrastructure requirements around IOT ecosystems should be anticipated as well. Connected devices will signal into the cloud where structured and unstructured sensor data meets analytics, which will increasingly integrate and process exploding volumes of (especially unstructured) data. A corresponding emphasis on data and analytics infrastructure is therefore appropriate.

The Return of Enterprise Architecture

barrier Under construction
Road barrier with the words Under construction. 3d render.GETTY
So where is all this going? The predictions Gartner, Splunk and McKinsey make all have some merit. First and foremost, infrastructure is going to the cloud. We also know that security is a must have – as the auditors love to tell us. But infrastructure should not be designed in a vacuum. It may be time to resurrect your outdated Enterprise Architecture and re-architect your infrastructure as part of an integrated business, applications, data and technology architecture.

Investment Strategies

Ultimately, this is all about resource allocation. The three vendors offer insight and advice which should be assessed in the context of your industry, your competition, your company, your leadership and your culture. Your appetite for digital transformation is the ultimate driver of change. If it’s low then discussions like these may be fun, but unproductive. But if the appetite is large then insights from companies like Gartner, Splunk and McKinsey – and others – can be extremely helpful.

Integrating Development and Operations

At the heart of DevOps is the idea that development and operations teams should stop working in silos. Historically, these groups maintained separate toolchains, separate budgets, and separate success metrics. Developers optimised for shipping new code; operators optimised for system stability. That tension created bottlenecks, slow release cycles, and finger-pointing when things went wrong.

DevOps addresses this in several concrete ways:

• Automation and agility for operations: Practices such as infrastructure-as-code and automated testing—concepts borrowed from software development—give operations teams the same reproducibility and speed that developers rely on.
• Shared goals: Teams align around product outcomes—uptime, release frequency, mean time to recovery (MTTR)—rather than departmental metrics that can conflict.
• Mutual context: Developers gain an understanding of production infrastructure; operators learn the architectural decisions behind the applications they run. That shared context leads to systems that are easier to deploy, monitor, and debug.

CI/CD: The Engine of DevOps Delivery

Continuous integration and continuous delivery (CI/CD) is the set of practices most closely associated with DevOps. Rather than accumulating months of work into a single large release, CI/CD breaks delivery into a stream of small, frequent changes:

• Continuous Integration (CI) means every code commit triggers an automated build and test pipeline. Bugs surface quickly—often within minutes of being introduced—while the context is still fresh in the developer’s mind.
• Continuous Delivery (CD) extends CI so that every build that passes automated tests is also deployable to production. Some teams go further with continuous deployment, where every passing build is automatically released without a manual approval gate.

In a mature CI/CD pipeline, automated tooling monitors running infrastructure and applications, alerts on anomalies, and can even roll back a bad deployment automatically. Small, incremental updates are far easier to diagnose and reverse than the monolithic releases of traditional release management.

To keep CI/CD manageable at scale, DevOps teams typically own a bounded portion of a larger system—one or a few microservices, for example—so they can iterate quickly without coordinating every change with dozens of other teams.

DevOps and Cloud-Native Applications

Cloud-native application design and DevOps are strongly complementary. Cloud-native applications are composed of microservices—small, independently deployable units of functionality—packaged in containers. Containers are lightweight, ephemeral runtime environments that bundle an application with its dependencies, making deployments consistent across development, staging, and production.

In a typical cloud-native DevOps workflow:

• Developers write and test individual microservices.
• CI pipelines build and test container images on every commit.
• CD pipelines push approved images to a container registry and trigger deployments.
• Operators manage container scheduling, scaling, and health using an orchestration platform such as Kubernetes.

Organisations with legacy monolithic applications have options: they can progressively decompose the monolith into microservices using DevOps practices, or they can build a parallel cloud-native replacement from scratch. Either path is non-trivial, but the result—applications designed for elastic scaling and continuous delivery—tends to be more resilient and easier to evolve than the original.

Key Considerations Before Adopting DevOps

DevOps is a broad term. Before embarking on an adoption, teams should be clear about what it means for their specific context. A few practical considerations:

Culture Change Is the Hardest Part

Tooling is the easy part of DevOps. The harder challenge is cultural: breaking down ingrained team boundaries, encouraging shared ownership of production, and building psychological safety so that engineers can raise problems without blame. A survey by the Cloud Native Computing Foundation found that cultural change was the top challenge organisations reported when moving to cloud-native, DevOps-oriented ways of working. (Note: the specific year and figures should be verified against the published CNCF survey before publication.)

People and Roles

Restructuring teams around DevOps naturally raises questions about job roles. Engineers who previously specialised entirely in operations or entirely in development may need to expand their skill sets. Framed positively, DevOps tends to make individual contributors more effective and more employable; framed poorly, the transition can feel threatening. Clear communication about why the change is happening and how roles will evolve is essential.

Start Small and Measure

Rather than transforming an entire organisation overnight, most successful DevOps adoptions begin with a pilot team or a single service. Define measurable outcomes upfront—deployment frequency, lead time for changes, MTTR, and change failure rate are the four metrics popularised by the DORA research programme—and use them to demonstrate value before scaling the approach.

Summary

DevOps is not a product, a job title, or a single tool. It is a set of cultural principles and engineering practices—CI/CD, automation, shared ownership, and continuous improvement—that help organisations deliver reliable software more frequently. When combined with cloud-native architectures, DevOps enables teams to move quickly without sacrificing stability. The investment required is real, particularly on the cultural side, but organisations that make it successfully report substantially faster delivery cycles and improved system reliability.

Although more than 60% of organizations have adopted DevOps at some level, according to Forrester, many continue to struggle with it. One reason: DevOps isn’t a hard-coded set of practices or principles, and this often leads to confusion and uncertainty, from the IT trenches all the way up to the C-suite.

This confusion is exacerbated by deep-rooted, conventional attitudes that are at cross-purposes with the lean/Six Sigma methodologies that formed the inspiration for DevOps. Just like the manufacturing firms that were trying to become lean a half a century ago, many existing software organizations don’t have the innate flexibility to reset their attitudes.

With this inflexibility comes a devotion to waste, although all players vehemently deny it. Waste in one form or another can be a serious impediment to virtually every linear, process-centered effort. I have seen it degrade DevOps workflows, and even derail entire projects.

Business leaders at these firms look at young, nimble companies that have built lean principles into their entire operating approach and culture, including software development. And they ask themselves, “Why can’t we get there?” The answer is simple: Those firms aren’t dragging around years of dead weight in the form of outdated attitudes and bloated processes.

But when leadership makes a meaningful commitment to eliminate wasteful, self-defeating activities; alerts its personnel that the effort is a business imperative; and empowers its most innovative thinkers to work on the problem, extraordinary results can follow.

Here are some scenarios that can help you identify these issues within your own organization—and how to move in the right direction.

[ Get up to speed on quality-driven development with TechBeacon’s new guide. Plus: Download the World Quality Report 2019-20 for lessons from leading organizations. ]

Are you blaming the right problem?
One impediment to DevOps success in many firms is the myopia that accompanies misplaced blame. Because many business leaders don’t accept that traditional, wasteful behaviors are at the core of their problems, they assume that failure is rooted in lack of technology or capacity. They think their teams can “tool their way” to a better outcome.

Here’s what happens next:

The CIO or CEO reads about nimble firms that are “getting DevOps right,” and sees they are using a stable of tools to automate their processes and enable continuous delivery.

Meetings take place with development and operations managers to determine which tools will be the most useful.

The development manager wants to order a handful of proven-process acceleration tools and implement them right away.

The operations manager worries that the tools will negatively affect compliance and security and requests time to evaluate them fully.

Six months later, nothing has changed.

If by some stroke of luck the development team gets approval to acquire the tools, at some point the operations manager still gets involved. He or she delays their adoption and integration into the development workflow out of fear that something will “break” or that governance will suffer.

The outcome is the same: Six months later, nothing has changed.

In either scenario, management fails to recognize that while using DevOps tools is a hallmark of efficient delivery pipelines, it doesn’t guarantee their success. Do teams need tools to expedite and optimize delivery? Certainly.

But tools only work if corporate mindsets, organizational structures, cultures, and internal processes—especially regarding governance—are just as lean and nimble. When those who are impeding the effort are not ready to accept responsibility, nothing happens.

Do internal attitudes conflict with your efforts?
Achieving the goals mentioned above can make a lot of people very uncomfortable—including people in the organization who act as conformity “enforcers,” like the operations manager in the scenario above. People in this role are traditionally charged with governance.

Understandably, these people feel that any change should be approached very cautiously. But caution is the enemy of lean, and it fosters one of the seven recognized areas of waste: over-processing.

At their cores, agile and DevOps are both expressions of lean principles, which are centered on ensuring that work moves quickly and smoothly through processes. Any barriers to that goal must be identified and re-engineered, or eliminated, to streamline the flow. (You can achieve this through value stream mapping.)

Yet the enforcer mentioned above has been given marching orders that are in direct conflict with the goals of efficient, expedited delivery. Until management addresses this issue, nothing will change.

So what should you do? Some organizations bring in outside experts to re-engineer processes and impose a new order on teams, but this can be problematic. If an existing team member has the expertise to lead the initiative, having this person take the lead can lessen internal resistance to the effort.

Either way, someone with leadership skills and a fanatical devotion to achieving a lean stance must sit at the helm of the effort.

[ Learn how to apply DevOps principles to succeed with your SAP modernization in TechBeacon’s new guide. Plus: Get the SAP HANA migration white paper. ]

Are bad habits increasing the challenge?
An equally significant hurdle to replacing waste with lean motion is the inertia that comes with habit. Here’s an example.

A traditional fabricator wants a custom maintenance management system (MMS) for spare parts and materials. The company brings in outside specialists to examine and map the firm’s processes, confirm its inventory, and set up the system.

The specialists conduct their investigation and work with the internal DevOps team to set up an MMS, import the inventory list, and assign SKUs to each item. They develop workflows to track the ordering, approval, requisitioning, procurement, and other processes.

The new system debuts, and the specialists explain to company personnel that, moving forward, everyone will request parts and materials using this system, and that all approvals will be handled through the system as well. Everyone nods their heads in agreement as the specialists wave goodbye.

At this point, the DevOps team takes over maintenance and updating of the system. Within one week, personnel have discovered they can print out the requests they log into the MMS, so they start walking them over to a supervisor for a manual signature rather than take a chance with the new system.

Staffers turn the signed requests into the DevOps team, expecting them to get the orders into the system. Frustrated, but not wanting to derail the fledgling effort, the team members comply.

Three months later, everyone has adopted this workaround, and the DevOps team spends its days in data entry rather than developing, testing, and deploying system code improvements. Shop floor workers are still getting their parts as quickly as before, but the entire IT process improvement effort has stalled.

This is just one example that plays out every day in firms around the globe, and neither agile nor DevOps by themselves will fix it.

Interrupt the cycle
You can’t just code these problems away; they’re hard-wired into many organizations through decades of familiarity and tradition. Enforcers, who often play a necessary role in ensuring security and compliance, resist new processes or workflows even when they have been fully tested. Their training, performance metrics, and bonus incentives are all targeted to reduce risk in ways that stifle change.

I have worked with many firms where the teams were able to deliver working code for an update every two weeks, but so many rounds of testing and evaluation ensued that multiple updates got bundled into one, resulting in a big, traditional release every six months.

To achieve the goals of DevOps and agile, you can’t let this situation continue.

Here’s the solution
Here are some ways your firm can start moving in the right direction.

Engage in value stream mapping (VSM) to identify where your value lies and prioritize what you need to accomplish. VSM is a flowchart-based mechanism to illustrate, analyze, and improve the steps to deliver a product or service to customers. It is something you should absolutely be incorporating into your development effort. However, in this scenario, we’re not talking about VSM for code delivery. VSM can also help you identify and eliminate the waste in your internal processes and culture.

Accept that this effort is a marathon, not a sprint. Develop iterative goals that can be benchmarked for progress on a consistent, scheduled basis. This will begin shifting the mindset from “Let’s make it flawless” to “Let’s make it good enough and keep moving forward.”

Establish KPIs and other metrics for process improvement, just as you would (or should) for code releases, with benchmarks to confirm progress or to address backsliding.

Identify a true innovator within your organization and put that person in charge of the effort. While this might be a developer, it doesn’t need to be. The type of change I am describing doesn’t involve mad coding skills; it requires persistence and persuasion. (Hint: The person in charge should not be an enforcer.)

Reinvent your control framework. The traditional, audit-based controls that have typically accompanied software development don’t work with DevOps. Controls and testing are vital, but they must be integrated into the release cycles and move just as quickly. (This is where the concept of continuous delivery morphs into “continuous everything,” but that’s a discussion for a different day.)

Bring enforcers into the conversation. Their buy-in is vital, so they must be reassured that the firm is still committed to risk management, and that it can coexist with innovation. Their concerns are also valid, so they should have the opportunity to present them in a meaningful way. Once the framework is in place and the enforcers start to become comfortable with results, their apprehension will lessen.

Once you begin seeing improvement, you can start evaluating and implementing process acceleration, automation, and integration tools to help you build, test, and deploy your applications more efficiently. While you are adopting these new capabilities, keep the ops manager apprised of the effort. Fear of irrelevance is as great as fear of the unknown.

Go forth and conquer DevOps like a startup.

Keep at it
This list can sound daunting, but don’t lose your courage. It’s a series of recommendations, not a sequence of steps. Change is most easily consumed one bite at a time.

No matter what pace you take, resist the urge to skip over poorly constructed processes, gaps, and weaknesses, thinking you will get back to them later. Chances are good that you never will. The inertia inherent in “that’s just how we do it” is too strong.

Stay focused, establish incremental goals, and celebrate your wins, no matter how small. Before long, you’ll be realizing long-term benefits—and greater customer value.

What Is the DevOps Market?

“Development to Operations” (DevOps) refers to a set of cultural practices, processes, and tools designed to tighten the feedback loop between software development and IT operations teams. By emphasizing automation, continuous integration and delivery (CI/CD), collaboration, and shared accountability, DevOps helps organizations ship software faster, with fewer defects, and at lower operational cost.

As enterprises increasingly treat software delivery as a core business capability, analyst coverage of the DevOps tooling and services market has grown significantly. The figures below are drawn from a Trusted Business Insights report covering the period 2019–2026.

Market Size and Growth Rate

According to the report, the global DevOps market was valued at approximately USD 2.77 billion in 2016 and was projected to expand at a compound annual growth rate (CAGR) of 18.60% through the forecast period ending in 2026.

The primary growth drivers identified include:

• Cloud adoption: Enterprises migrating workloads to cloud platforms accelerate demand for containerization, microservices, and Platform-as-a-Service (PaaS) tooling—all tightly coupled with DevOps practices.
• Automation investment: Reducing manual intervention in build, test, and release pipelines lowers cost and improves release frequency.
• IoT, mobility, and BYOD: Expanding device ecosystems increase software complexity, making automated lifecycle management more valuable.
• Regulatory pressure in financial services: Faster, auditable delivery pipelines help organizations meet changing compliance mandates.

Key restraints cited in the report include the complexity of DevOps adoption within legacy-heavy organizations and the lack of universally standardized tooling, which can lead to fragmented, hard-to-maintain toolchains.

Market Segmentation

By Deployment Model

• On-premises: Preferred by highly regulated industries (finance, healthcare, government) that require strict data sovereignty and audit controls.
• Cloud: The faster-growing segment, driven by the scalability and managed-service economics of public and hybrid cloud environments.

By Enterprise Size

• Small and Medium Enterprises (SMEs): Often adopt SaaS-delivered DevOps tools to minimize upfront infrastructure investment.
• Large Enterprises: Tend to deploy multi-tool, integrated DevOps platforms with significant customization and governance requirements.

By Vertical

Regional Outlook

North America — Dominant Market

North America held the largest regional share at the time of the report. The concentration of major DevOps vendors (IBM, CA Technologies, Cisco, Hewlett Packard Enterprise, Chef, Puppet Labs, Docker) in the United States, combined with high enterprise IT spending and mature cloud infrastructure, underpins continued leadership.

Asia-Pacific — Fastest Growing

The Asia-Pacific region was projected to grow the fastest over the forecast period, driven by rising investment in cloud and IoT infrastructure, smartphone penetration, and growing demand for digital services across India, China, Japan, and Southeast Asia.

Europe, Middle East & Africa, Latin America

These regions show increasing adoption, particularly in financial services (Europe) and telecommunications (MEA), though growth rates lag North America and APAC. GDPR and similar data-residency regulations influence deployment model choices in Europe.

Key Vendors and Competitive Dynamics

The DevOps tooling market is highly competitive. Vendors named in the report include:

• IBM Corporation
• CA Technologies (acquired by Broadcom in 2018)
• Cisco Systems
• Hewlett Packard Enterprise (HPE)
• Docker, Inc.
• Puppet Labs
• Chef (now Progress Chef)
• EMC Corporation (now part of Dell Technologies)
• Clarizen

The report notes that vendors are pursuing both organic and inorganic growth strategies:

• Mergers and acquisitions: CA Technologies’ acquisition of Automic (IT automation and service orchestration) was cited as a representative example of portfolio expansion through M&A.
• R&D and innovation labs: Major players are establishing centers of excellence and collaborating with research institutions to accelerate product development.
• Strategic partnerships: Smaller specialist vendors are partnering with platform providers to extend reach and integration depth.

What This Means for DevOps Practitioners

Market sizing reports like this one are primarily aimed at investors and business strategists, but the underlying trends carry practical implications for engineers and platform teams:

• Toolchain consolidation pressure: As vendors acquire each other, integrated platforms (GitLab, GitHub Actions, Azure DevOps, AWS CodePipeline) increasingly bundle capabilities that once required separate point solutions. Evaluate build-vs-buy trade-offs accordingly.
• Cloud-native is the default: The market’s shift toward cloud deployments validates investments in container orchestration (Kubernetes), infrastructure-as-code, and managed CI/CD services.
• BFSI acceleration: Financial services teams face growing pressure to adopt DevSecOps practices that satisfy both speed-to-market and compliance requirements simultaneously.
• Standardization still matters: The report’s citation of “lack of standardized tools” as a barrier remains relevant. Establishing internal platform engineering teams and golden-path templates can reduce toolchain fragmentation.

Limitations of This Data

This article is based on a 2019 market research report with a base year of 2016–2017. The DevOps landscape has changed substantially since publication:

• Several named vendors have been acquired (CA Technologies → Broadcom; Chef → Progress; EMC → Dell).
• New entrants—particularly GitHub Actions, GitLab CI, and cloud-native equivalents from AWS, Azure, and GCP—have materially altered competitive dynamics.
• Forecast figures (CAGR, market size) should not be cited as current without verification against a more recent primary source.

For current market sizing, consult up-to-date analyst reports from firms such as Gartner, IDC, or Forrester before making investment or vendor-selection decisions.

What Is the Ansible Automation Platform?

The Ansible Automation Platform brings together several components under one umbrella:

• Red Hat Ansible Tower – the control plane for managing, scheduling, and delegating automation jobs
• Red Hat Ansible Engine – the core runtime that executes playbooks and modules
• Red Hat Network Automation – purpose-built automation for network infrastructure
• New SaaS capabilities – Ansible Content Collections, Automation Hub, and Automation Analytics

Whether an organization is just beginning its automation journey or looking to scale automation across additional use cases and domains, the platform is designed to reduce the time to value by providing reusable, certified building blocks instead of requiring teams to start from scratch.

Key New Capabilities

Three new features shipped with the Ansible Automation Platform launch address content management, certified content discovery, and operational visibility.

Ansible Content Collections

Ansible has one of the largest open-source automation communities in the world, producing a continuous stream of modules, plugins, roles, and playbooks from Red Hat engineers, independent contributors, and technology partners. Before Content Collections, distributing and consuming this content was tightly coupled to Ansible release cycles, making it difficult to ship or consume updates independently.

Ansible Content Collections introduce a new packaging format that decouples content distribution from core Ansible releases. A Collection bundles modules, plugins, roles, and playbooks into a single distributable unit, allowing contributors to publish updates on their own schedule and allowing consumers to adopt new content without waiting for a new Ansible Engine release.

Key benefits of Content Collections:

• Lowers the barrier to entry for new automation adopters by providing pre-built, ready-to-use content
• Reduces maintenance overhead by scoping updates to individual collections rather than requiring full platform upgrades
• Enables partners and contributors to ship certified automation content independently

Automation Hub

Automation Hub is the centralized, hosted repository where Red Hat and its certified partners publish Ansible Content Collections. For enterprise customers, it is the primary place to discover and consume content that has been tested and is backed by Red Hat support.

At the time of the platform launch, partners including Cisco, F5, Google Cloud, Microsoft, and NetApp announced plans to publish certified, supported Collections through Automation Hub. This gives teams a curated, trusted source of automation content for common infrastructure and cloud platforms, reducing the risk associated with using community-sourced modules in production environments.

The distinction between certified content (available through Automation Hub) and community content (available through Ansible Galaxy) is important for enterprises with compliance or support requirements: certified Collections carry Red Hat’s quality and support guarantee, while Galaxy content does not.

Automation Analytics

Automation Analytics provides operational visibility into how automation is performing across an organization’s Ansible Automation Platform deployments. Rather than treating automation as a black box, the feature surfaces data that helps teams understand and improve their automation programs over time.

Automation Analytics surfaces information such as:

• Which modules and resources are used most frequently across the environment
• The overall health and performance of automation jobs
• Automation activity broken down by internal team or group

An opt-in benchmarking feature allows organizations to share anonymized automation metrics with Red Hat in exchange for seeing how their automation maturity compares to industry peer groups. This can help identify gaps and prioritize where to invest further in automation.

Why This Matters for DevOps and Platform Engineering Teams

The Ansible Automation Platform represents a shift in how Red Hat positions Ansible for enterprises: from a task-automation tool to a full automation operating model. The integration of content management (Collections), a certified content marketplace (Automation Hub), and operational telemetry (Automation Analytics) addresses three common friction points that organizations encounter as they try to scale automation beyond individual teams:

• Content sprawl – teams reinventing automation for the same systems because there is no shared, trusted library
• Governance gaps – uncertainty about whether community modules meet security or quality standards for production use
• Visibility deficits – no consistent way to measure automation coverage, performance, or ROI across the organization

By addressing these challenges within a single platform, Red Hat is positioning Ansible not just as an automation engine but as the foundation for an organization-wide automation practice.

Original announcement sourced from e3zine.com (November 2019).

What Is a Cloud Centre of Excellence?

At its most basic, a CCoE is a cross-functional team — either in-house, externally sourced, or a blend of both — with the expertise to plan, govern, and accelerate cloud adoption across an organisation. But defining it purely as a team undersells what it actually needs to be.

A CCoE is fundamentally a change-management vehicle. It exists not just to handle technical migration tasks, but to drive a shift in how the entire organisation thinks about and works with technology. That means its members must combine technical depth — cloud architecture, security, DevOps — with business acumen, product knowledge, and communication skills capable of engaging stakeholders at every level.

Why Cloud Adoption Fails Without a CCoE: The Silo Problem

In most organisations, cloud adoption starts organically. A forward-thinking development team adopts AWS for a new service. A data team moves its analytics workloads to a managed platform. Finance runs a SaaS product. These efforts are valuable, but when they happen in isolation they create inconsistent governance, duplicated tooling, security gaps, and unpredictable costs.

The root cause is rarely technology — it’s people and process. Without a coordinating body, individual departments make their own decisions about cloud platforms, identity management, networking topology, and compliance controls. The result is shadow IT at scale, with no unified strategy to rationalise it.

A CCoE breaks down these silos by creating a single source of truth for cloud strategy, standards, and best practices. It ensures that cloud adoption is a coherent, organisation-wide programme rather than a series of one-off projects.

What Is a CCoE Responsible For?

The responsibilities of a CCoE evolve as an organisation moves through the stages of its cloud journey — from initial planning, through migration, to optimisation and innovation. A practical way to organise CCoE responsibilities is around four distinct functional areas:

1. Structure and Operations

This function defines how the organisation operates in the cloud. It focuses on consistency — establishing account structures, landing zone designs, naming conventions, tagging policies, and the operating model for cloud-hosted workloads. Members in this area actively engage other departments, identify constraints (such as regulatory or security requirements), and surface opportunities to improve delivery — for example, identifying where CI/CD pipelines would reduce manual toil and deployment risk.

2. Evaluation

Cloud platforms release features and services at a rapid pace. The evaluation function keeps the CCoE — and by extension the business — ahead of the curve. These team members assess new services, proof-of-concept emerging capabilities, and advise on whether and how to adopt them. Over time, this function builds organisational confidence: teams learn to lead on cloud strategy rather than react to it.

3. Governance

Security and compliance cannot be retrofitted. The governance function embeds policy controls from day zero — identity and access management, data classification, encryption standards, cost guardrails, and audit logging. By automating policy enforcement through infrastructure-as-code and policy-as-code tools, the CCoE allows engineering teams to move quickly without inadvertently violating compliance requirements. Governance is the guardrail, not the gatekeeper.

4. Innovation

The cloud’s value lies partly in its ability to compress the feedback loop between idea and experiment. The innovation function within a CCoE encourages a culture of measured risk-taking: building prototypes, running experiments, measuring outcomes, and iterating. These team members work across departments to understand business problems and translate them into cloud-native solutions, operating with the grain of cloud architecture rather than against it.

Who Benefits from a CCoE?

When cloud strategy is consistent and well-governed, every stakeholder group benefits:

• Leadership and executives gain visibility into cloud spend, risk posture, and strategic progress. Decision-making becomes data-driven rather than anecdotal.
• Engineering and IT teams work from standardised templates, pre-approved patterns, and clear guardrails — reducing onboarding time for new services and cutting troubleshooting overhead.
• Product and business teams can self-serve cloud resources within defined boundaries, accelerating delivery without waiting for centralised approvals.
• End users and customers benefit indirectly from faster delivery cycles, improved reliability, and security practices that reduce breach risk.

Templatised, repeatable deployment patterns are a practical illustration of this benefit: once the CCoE defines a secure, compliant deployment pipeline for a workload type, every team that adopts it inherits the same baseline quality — without having to re-solve the same problems independently.

Principles for Building an Effective CCoE

Standing up a CCoE is not a purely technical exercise. The following principles guide successful implementation:

Cross-Functional Composition

A CCoE that is staffed entirely by infrastructure engineers will struggle to win adoption across the business. Effective CCoEs include cloud architects, security engineers, finance analysts (for FinOps), product managers, and representatives from major business units. This breadth ensures that decisions account for business impact, not just technical elegance.

Executive Sponsorship

Without visible executive support, a CCoE risks being ignored or undermined by departments that do not want to change their practices. Executive sponsorship signals that cloud governance is organisational policy, not optional guidance.

Start Small, Design to Scale

It is better to launch a small, effective CCoE than to wait until you have assembled the perfect team. Start with the highest-priority workloads and the most critical governance gaps. Build processes and tooling that can scale to cover the full organisation as the CCoE matures and gains credibility.

Leverage External Expertise Where Needed

If your organisation lacks the internal knowledge to bootstrap a CCoE, engaging an external cloud consultant or managed service partner is a pragmatic option. A good partner will help you define your cloud roadmap, identify skills gaps, assist with hiring or training, and act as a co-pilot as your internal capability matures. The goal should always be to build internal competency over time, not to create a permanent external dependency.

Embed a Culture of Continuous Improvement

Cloud strategy is not a project with a completion date. The CCoE should run retrospectives, monitor cloud usage and costs, track adoption metrics, and continuously refine its standards and tooling. A CCoE that stops learning will quickly fall behind a cloud landscape that never does.

Key Takeaways

• A CCoE is a cross-functional team that governs, guides, and accelerates cloud adoption across an entire organisation.
• It solves the silo problem by creating a unified strategy, shared standards, and consistent governance.
• Core CCoE functions cover operations, evaluation, governance, and innovation.
• Every stakeholder group — from engineers to end users — benefits when cloud adoption is coherent and well-governed.
• Successful CCoEs require executive sponsorship, cross-functional membership, and a commitment to continuous improvement.
• External advisors can accelerate the bootstrapping phase when internal expertise is limited.

A Cloud Centre of Excellence is not a luxury for large enterprises. For any organisation serious about making cloud investment pay off, it is the organisational infrastructure that makes sustainable, scalable adoption possible.

Original source credit: cloudcomputing-news.net

The Scale Problem No Dashboard Was Built For

Four billion is a number that challenges human intuition. Now picture four billion Kubernetes container instances — each one generating telemetry data on spin-up, runtime behavior, and spin-down — every single week. According to Nancy Gohring, senior analyst at 451 Research LLC, that is approximately the scale at which Google runs Kubernetes for its own internal workloads.

“If you think of Google launching 4 billion containers every week, they’re collecting a number of different data points,” Gohring said. “It’s not even just four billion pieces of data. The volume of operations data that people are faced with is just out of this world.”

Google is an extreme example, but the underlying trend applies broadly. Any organization running microservices at meaningful scale faces an exponential growth in operational signals: logs, metrics, traces, events, and alerts multiplying with every new service, replica, and deployment pipeline.

Autonomous DevOps Teams and the Oversight Gap

The shift to distributed, autonomous DevOps teams was designed to increase velocity. Small, self-sufficient squads own their services end-to-end — from writing code to running it in production. This model works well for speed, but it introduces a subtle organizational risk: no one may be watching the whole application.

“DevOps are running autonomously; they’re doing their own thing so they can move quickly,” Gohring noted. “But is there anybody overseeing the application that’s made up of maybe a thousand microservices? In some cases, the answer is no.”

When an application is composed of hundreds or thousands of loosely coupled services, each owned by a different team, systemic failures — cascading latency, inter-service dependency issues, degraded user experience — can go undetected until they become customer-impacting incidents. Individual team dashboards only show local health; end-to-end application health requires a broader view.

Tool Sprawl: When Every Team Chooses Its Own Monitoring Stack

The natural response to rising operational complexity is to add monitoring tooling. But in organizations where teams have full autonomy, this often results in a fragmented landscape where each team adopts whichever tool fits its immediate needs — Prometheus for one team, Datadog for another, a bespoke ELK stack for a third.

“As applications are getting much more complex, looking at that data in a siloed tool tends to obscure the bigger picture,” Gohring said. “In most cases, businesses aren’t going to just settle on one monitoring tool that does it all. If you have 100 DevOps teams and they have chosen their own monitoring tools, this is not efficient.”

The practical consequences of monitoring tool sprawl include:

• Correlation blind spots: When logs, metrics, and traces live in separate systems, correlating an anomaly in one service with a root cause in another becomes a manual, time-consuming process.
• Duplicated licensing and operational costs: Maintaining multiple monitoring platforms multiplies vendor contracts, training overhead, and integration work.
• Inconsistent alerting standards: Teams using different tools will configure thresholds, severities, and on-call workflows differently, leading to inconsistent incident response.
• Onboarding friction: Engineers rotating between teams or responding to cross-team incidents must context-switch between unfamiliar dashboards under pressure.

The Push Toward Autonomous and Unified Monitoring

The data volume problem is large enough that human operators alone cannot triage it in real time. This is pushing vendors and platform teams toward two complementary approaches:

1. Autonomous/AI-assisted monitoring: Systems that automatically detect anomalies, suppress noise, and surface only the signals that require human attention — reducing alert fatigue and speeding mean time to detect (MTTD).
2. Unified observability platforms: Consolidating logs, metrics, and traces into a single platform gives teams a shared language for application health and enables cross-team correlation without requiring everyone to adopt the same instrumentation library.

Neither approach eliminates the need for skilled engineers, but both aim to make the signal-to-noise ratio manageable at cloud-native scale.

Practical Takeaways for DevOps and Platform Teams

If your organization is feeling the strain of monitoring tool sprawl or drowning in operational telemetry, consider the following steps:

• Audit your current tooling landscape. Map which teams use which monitoring tools and identify where correlation gaps exist between services.
• Establish organization-wide observability standards. Define minimum requirements for logging format, metric naming conventions, and distributed tracing instrumentation — without mandating a single tool everywhere.
• Designate application-level ownership. Beyond service-level ownership, assign responsibility for monitoring the health of entire user-facing application flows, not just individual microservices.
• Evaluate consolidation incrementally. Full platform consolidation is disruptive. Start by federating data into a central store for querying and alerting, while allowing teams to retain their preferred instrumentation.
• Build runbooks that span services. Incident response documentation should reflect the reality that failures rarely respect service boundaries.

The Bigger Picture

The operations data avalanche is not a temporary growing pain — it is a structural feature of cloud-native architectures. As container density, deployment frequency, and microservice counts continue to rise, the gap between the data generated and a team’s capacity to interpret it will only widen. Organizations that address monitoring fragmentation proactively will be better positioned to maintain reliability and developer productivity at scale.

Source interview originally published by SiliconANGLE / theCUBE, recorded at the Sumo Logic Illuminate event.

What Is Cloud Computing for Business Operations?

At its core, cloud computing for business operations refers to the delivery of IT resources—servers, databases, networking, software, analytics, and intelligence—over the internet. Central remote servers managed by a cloud service provider (CSP) store and process data and applications on behalf of the customer. This model lets business operators scale resources up or down on demand, pay only for what they use, and offload infrastructure management to specialised providers.

The practical benefits driving enterprise adoption include reduced capital expenditure, faster deployment cycles, built-in disaster recovery options, and global availability of services.

Service Model Segments

The cloud computing market for business operations is typically segmented by service model. Each model abstracts a different layer of the traditional IT stack:

• Infrastructure as a Service (IaaS): Provides virtualised compute, storage, and networking resources on demand. Customers manage the operating system and above, while the provider manages the underlying hardware. Examples include Amazon EC2, Microsoft Azure Virtual Machines, and Google Compute Engine.
• Platform as a Service (PaaS): Delivers a managed environment for developing, testing, and deploying applications without managing the underlying infrastructure. Examples include Google App Engine, Azure App Service, and IBM Cloud Foundry.
• Recovery as a Service (RaaS): Also known as Disaster Recovery as a Service (DRaaS), this model replicates and hosts physical or virtual servers in the cloud so that failover can occur quickly after an on-premises outage. It reduces the cost and complexity of maintaining a dedicated DR site.

Deployment Models

Beyond service models, organisations choose how to deploy cloud resources based on their security, compliance, and cost requirements:

• Private Cloud: Infrastructure is dedicated to a single organisation, hosted either on-premises or by a third-party provider. This model gives the greatest control over data sovereignty and compliance but requires significant investment.
• Hybrid Cloud: Combines private and public cloud environments, allowing workloads to move between them as computing needs and costs change. Hybrid architectures are common in regulated industries that must keep sensitive data on-premises while leveraging public cloud elasticity for less sensitive workloads.
• Others: Includes multi-cloud strategies (using services from more than one public cloud provider) and community clouds shared by organisations with common requirements.

Major Vendors in the Market

The competitive landscape spans hyperscale public cloud providers, enterprise software vendors, and specialist infrastructure companies. Prominent players include:

• Amazon Web Services (AWS) – the longest-established hyperscaler with the broadest service catalogue
• Microsoft Azure – strong enterprise integration with the Microsoft ecosystem and a large hybrid cloud portfolio
• Google Cloud Platform (GCP) – competitive in data analytics, machine learning, and Kubernetes-native workloads
• IBM Cloud – focused on enterprise hybrid cloud and AI, especially following its acquisition of Red Hat
• Red Hat – open-source middleware and OpenShift container platform, now part of IBM
• SAP Cloud Platform – targeted at enterprise ERP and business process workloads
• Oracle Cloud – strong in database-as-a-service and enterprise applications
• Salesforce Cloud – dominant in CRM and enterprise SaaS
• VMware – virtualisation and hybrid cloud management, including VMware Cloud on AWS
• Alibaba Cloud – the leading provider in China and a growing competitor in the Asia-Pacific region
• Cisco Systems – networking, security, and collaboration services in the cloud
• ServiceNow – IT service management and workflow automation delivered as SaaS
• Workday – cloud-native HR and financial management applications
• DigitalOcean – developer-focused IaaS with simple pricing and managed databases
• Adobe Cloud – creative and marketing cloud suites
• Kamatera, CloudSigma, HPE Cloud, Verizon Cloud, CenturyLink (now Lumen Technologies) – additional competitors serving enterprise and mid-market segments

Regional Landscape

Cloud adoption rates and dominant providers vary significantly by geography. Understanding regional dynamics is critical for vendors expanding internationally and for enterprises evaluating data residency requirements:

Americas

The United States remains the largest single cloud market globally, driven by mature enterprise IT spend, a concentration of technology companies, and strong demand for digital transformation. Canada, Mexico, and Brazil represent important secondary markets, with Brazil being the largest cloud market in Latin America.

Asia-Pacific (APAC)

APAC is one of the fastest-growing regions for cloud services. China’s market is dominated by Alibaba Cloud, Tencent Cloud, and Huawei Cloud alongside the global hyperscalers operating through local partnerships. Japan, South Korea, India, Southeast Asia, and Australia each present distinct regulatory and competitive environments.

Europe

Europe’s cloud market is shaped heavily by GDPR and data sovereignty concerns. Germany, France, the UK, Italy, Spain, and Russia are the largest markets. The EU’s GAIA-X initiative reflects ongoing efforts to establish European data infrastructure standards independent of US and Chinese hyperscalers.

Middle East & Africa

Adoption in this region is accelerating as hyperscalers open dedicated data centres in the Gulf Cooperation Council (GCC) countries, South Africa, Israel, Turkey, and Egypt. Government modernisation programmes and smart city initiatives are key demand drivers.

Key Market Drivers

• Cost optimisation: Shifting from capital expenditure (CapEx) to operational expenditure (OpEx) reduces upfront hardware investment and enables more predictable budgeting.
• Scalability and agility: Organisations can provision resources in minutes and scale globally without physical hardware procurement cycles.
• Remote work and collaboration: Distributed workforces require cloud-hosted productivity tools and secure remote access to applications.
• Digital transformation: Cloud platforms underpin modern application development practices such as DevOps, CI/CD, microservices, and serverless computing.
• Data and AI workloads: Managed machine learning and big data services lower the barrier to entry for AI-driven business operations.

Challenges and Risks

• Security and compliance: Storing sensitive data with a third-party provider introduces shared-responsibility security models that organisations must understand and manage actively.
• Vendor lock-in: Proprietary managed services can make it difficult and expensive to migrate between providers.
• Cost management: Without proper governance, cloud spend can grow rapidly and unexpectedly—a discipline now known as FinOps addresses this challenge.
• Latency and connectivity: Workloads requiring ultra-low latency may not be suited to centralised cloud regions, driving interest in edge computing.
• Regulatory constraints: Data localisation laws in countries such as Russia, China, and India can restrict where data may be stored and processed.

Emerging Trends

• Multi-cloud and hybrid cloud: Enterprises increasingly distribute workloads across two or more providers to avoid lock-in and improve resilience.
• Serverless and containers: Kubernetes and serverless platforms abstract infrastructure management further, letting teams focus entirely on application logic.
• AI and ML as cloud services: Pre-trained model APIs and managed ML pipelines are making artificial intelligence accessible without specialised hardware expertise.
• Edge computing: Extending cloud services to the network edge reduces latency for IoT, manufacturing, and retail use cases.
• Cloud-native security (CNAPP): Integrated security posture management tools are converging to address the complexity of protecting multi-cloud environments.

Summary

Cloud computing for business operations encompasses a broad and competitive market spanning infrastructure, platform, and recovery services. For DevOps and platform engineering teams, understanding the segmentation by service model, deployment type, and regional dynamics is essential when making architecture and vendor decisions. The market continues to evolve rapidly, with multi-cloud, AI integration, and edge computing defining the next wave of enterprise adoption.

Note: Market size figures and CAGR projections referenced in the original source report (via ReportOcean, 2019) were not independently verified and are not reproduced here. Readers requiring current market size data should consult up-to-date research from Gartner, IDC, or Synergy Research Group.

Overview

By 2017, DevOps had been influencing software and operations culture for roughly a decade. To understand how far the industry had actually come, Atlassian partnered with xMatters—a technology partner providing collaboration tooling for DevOps workflows—to survey practitioners across a range of industries. The resulting report highlighted meaningful progress as well as persistent gaps in three critical areas: Culture, Monitoring, and Incident Management.

DevOps Is Working—For Those Who Practice It

According to the survey, 41% of respondents reported actively practising DevOps. Of that group, more than 65% said their DevOps initiatives were delivering the benefits they had expected, including:

• Faster time to market
• Improved customer experience
• Fewer customer-facing incidents
• Faster incident resolution times
• A higher pace of innovation

The remaining 59% of respondents either did not know what DevOps was or were unsure whether their organisation was practising it. Those respondents were excluded from the detailed survey questions. This gap underscores a significant split in the industry: organisations that have embraced DevOps hold a measurable competitive advantage, while many others still treat it primarily as a buzzword.

For additional context, the 2016 Puppet State of DevOps Report noted that high-performing DevOps teams deploy code approximately 200 times more frequently than low performers—a figure that illustrates the scale of the performance gap between adopters and non-adopters.

Culture: Shared Tools, Not Shared Knowledge

Tool sharing between development and operations teams was the strongest indicator of cultural alignment found in the survey. 80% of companies reported that dev and ops share at least some tooling—a foundational step toward a genuine DevOps culture. Many of those organisations also shared information generated by those tools, though often with restricted access.

However, tool sharing alone does not equal a healthy DevOps culture. Genuine knowledge sharing—where cross-functional teams can access information dynamically and in real time—was far less common. Only 17% of companies reported having open information available to cross-functional teams in dynamic formats such as wikis and chat platforms. The large majority indicated that information is shared reactively and in static formats only when requested.

This gap matters because shared context is what allows teams to respond quickly to change. Without it, the cultural promise of DevOps remains only partially fulfilled, even in organisations with strong tooling adoption.

Monitoring: Extensive Data, but Still Missing Issues in Production

Organisations surveyed had invested heavily in monitoring. Key findings include:

• 75% monitored both infrastructure and applications/services
• 60% monitored transactions
• 51% monitored user experience
• Nearly two-thirds said their monitoring solutions could predict potential issues before users were affected

Despite this monitoring coverage, 50% of surveyed companies still reported finding issues in production after code was released. The likely cause is a combination of gaps in transaction and user-experience monitoring and the fact that barely half of surveyed organisations practised automated testing as part of their QA process.

The takeaway is straightforward: monitoring is necessary but not sufficient. Without automated testing integrated into the delivery pipeline, monitoring alerts act as a reactive safety net rather than a proactive quality gate. Organisations that combine broad monitoring with robust automated testing are better positioned to catch issues before they reach production.

Incident Management: Process and Information Bottlenecks

Incident management is often considered the most rigorous test of DevOps maturity. How quickly a team can detect, communicate, and resolve a major incident reveals the true state of its processes, tooling, and culture. The survey results showed that most organisations still had significant room for improvement:

• 50% of companies had to wait for the operations centre to formally declare a major incident before a response could begin
• 43% used manual processes to update customers and internal stakeholders during incidents
• 34% experienced delays in resolution while waiting for subject-matter experts to become available

The common thread across these bottlenecks is information and process overload. Monitoring tools generate large volumes of data, but translating that data into actionable decisions under pressure requires clear runbooks, automated alerting, and pre-defined escalation paths. Organisations that rely on ad-hoc communication and manual handoffs during incidents will consistently struggle to meet resolution time objectives.

Key Takeaways for DevOps Teams

The 2017 Atlassian/xMatters report painted a picture of an industry in transition. DevOps was clearly delivering value for its practitioners, but most organisations had not yet realised its full potential. Based on the findings, the most impactful improvements organisations could make were:

1. Move from tool sharing to knowledge sharing. Adopt dynamic, open documentation platforms (wikis, persistent chat channels) so cross-functional teams can access context without waiting to be told.
2. Pair monitoring with automated testing. Monitoring production is essential, but automated testing earlier in the pipeline prevents many issues from reaching production in the first place.
3. Automate incident response workflows. Reduce reliance on manual communication and expert bottlenecks by implementing on-call automation, pre-defined escalation policies, and status-page tooling.
4. Define what a major incident means before one occurs. Clear, agreed-upon criteria for declaring incidents remove the delay caused by waiting on operations centres to make a call.

DevOps practices were well-established enough by 2017 to show measurable competitive benefits for adopters. The challenge for the years ahead was—and in many organisations remains—moving beyond surface-level tool adoption to genuine cultural and process transformation.

IT teams have to automate where possible to free up time, while encouraging individuality within the team.

This article isn’t about DevOps or any fancy management paradigm — it’s about IT operations best practices that every organization can use to maximize team productivity. Managers and IT administrators alike can take these practical steps — there’s nothing fancy about documenting a patch procedure — to prevent common snags and foster long-term good morale.

SMART targets

One of the biggest dangers to IT team efficacy is a project that becomes a time sink. We have all experienced it. Put an end to dragging projects by verifying that tasks are SMART: specific, measureable, achievable, realistic and timely.

All projects should achieve all the above. If the project does not meet these criteria, analyze and review it. It is OK to question project scope and demand to prevent a loss of time, resources and political or financial capital.

Write it all down

IT documentation is the least popular activity among systems administrators. Often, there is a lack of documentation even within the best teams.

What does IT documentation have to do with team morale? Staff retire or leave and all too often the knowledge leaves with them. Those left on the team waste time reinventing procedures and reverse engineering previously known steps.

If the specialist in a certain area isn’t available and the documentation isn’t good, a problem or procedure has three possible outcomes:

1) It is made low-priority, wastes cycles and/or slows the deployment;

2) It is done, but not to operationally documented standards or best practices; and

3) It is implemented incorrectly and must be remediated. Incorrect implementations can have bad consequences.

Train IT staff on how to create good documentation and organize it effectively so that others can find it. As long as concise, correct documentation is available, implementation becomes a simple process of understanding, learning and following the defined documents. All documentation should clearly define processes that are easily reproduced. Wikis are easy and inexpensive to run, leaving no excuse not to have a go-to place for documentation.

Good documentation doesn’t replace professional knowledge transfer and good ol’ show and tells. These trainings build generalist knowledge thanks to team experts, and encourage communication and trust.

With trust comes latitude

About those experts — each team member has an area where they excel and enjoy solving problems. For example, one team member is a code guru, while another takes to managing infrastructure hardware.

A wise IT manager lets each team member play to their strengths when possible. The team as a whole has a breadth of skills that should be assigned appropriately to maximize overall productivity.

Automate everything possible

Every team member, regardless of specialized expertise, should meet a certain prescribed level of understanding around automation. Consider it an IT operations best practice to automate the low-end, manual jobs under an admin’s control. This gives the team back valuable time to work on other issues.

Prioritize what to do

IT teams, particularly distributed ones, struggle to understand precisely what the workload is and what is expected of each team member. Frequent meetings keep everyone informed, and prevent people from duplicating work or treading on each other’s toes. Team handovers are an area to diligently improve. Effective meetings provide visibility into the changes occurring with current hot issues.

Standardization makes life easier

Any system that deviates from standard wastes time and resources — treat servers like cattle, not pets. Create plans to remove nonstandard systems as a best practice. Not only do nonstandard systems add to the complexity of IT operations, they go wrong at the worst possible moment. The troubleshooting admin is less likely to have a plan of attack or fast path to resolution on a specialized, unique configuration. If certain systems must be kept as “pets,” resort to good documentation to keep the peace.

Accidents happen

Lastly and perhaps most controversially, understand that accidents will happen to even the best IT team. No matter how good the documentation and the administrator, errors occur during IT operations. IT operations best practices and diligent staff members only serve to minimize the error rate, not reduce it to zero. As a team, learn why the error happened and update procedures and implementations to negate the issue in the future.

There are several ways to increase productivity and happiness along the lines of these IT operations best practices. The secret in daily operations is to automate where possible and cross skill team members. And always be on the lookout for time-sink projects, ready to say no as needed until the project is in an acceptable form for all.

The first time I heard the word “BizDevOps,” I had one question: How is this development different from earlier efforts to engage business executives in delivering great software?

I was skeptical, but not dismissive. Yes, BizDevOps is a buzzword, but it felt like more than that. I was excited. After all, this buzzword had “biz” in the name. The very emergence of the term was a victory of sorts. This thing we call BizDevOps acknowledges something my colleagues and I have been writing about for a long time: No company can deliver great software without engaging top business executives in the process.

No one developing software today disputes that. And yet, sustaining the engagement of business executives continues to be frustrating for software pros. Countless articles on this site and others chronicle those frustrations. Top managers tout the strategic importance of software to the bottom line, but they remain reluctant to devote full-time product owners to development projects. Another, not atypical example: A mature Agile team, deep into a development project, reached an impasse, where the CEO held one vision and the software pros an altogether different one. Weren’t these problems we solved years ago?

And then there are the software delivery challenges. Companies that made deep commitments to DevOps admit that the cultural differences between development and operations prevent them from delivering better software faster and more frequently. Will the emergence of BizDevOps change all this? Will business, development and operations come together to deliver great software? Has the right time finally arrived?

I posed those questions to Flint Brenton, CEO of South San Francisco-based CollabNet, which sells software development tools and services, and operating partner at the private equity firm Vector Capital. “What is the biggest hurdle to making BizDevOps happen?” I asked him. “It’s hard to change human behavior,” he said.

Here are some highlights of my recent conversation with Brenton.

Agile paved way for BizDevOps

The widespread use of Agile techniques is driving the move to BizDevOps. In spite of recent criticism, “Agile remains the pre-eminent means of new development and has created a need for speed,” Brenton said. Put another way, business executives share the expectation that software must be developed and delivered faster and more efficiently than it is now, and that sets the stage for BizDevOps adoption.

BizDevOps: Why now?

But the biggest reason why BizDevOps is happening now is that technology is capable of driving business outcomes in ways that weren’t possible in the past, Brenton said. “The primary way to deliver that value is through software.” That idea has been around for awhile and, more recently, the concept has taken hold. “The whole marketplace has evolved to the point where most enterprises are now software companies,” he said, referring to businesses that don’t sell software, per se.

Brenton’s definition of BizDevOps illustrates his point: “BizDevOps is the ability to take an idea conceived by the business, create value from that idea in software and deploy it so customers quickly realize the value of what was conceived.”

BizDevOps: The people piece

Getting management and employees to embrace the concept of BizDevOps is one thing. But how you get them to change their behavior to support the effort is the million-dollar question, Brenton said. “How do I get my company to change, when we have been doing it one way for many years?”

One widely understood way to change the culture is to change the people, he said. “The culture has to evolve to support the digitization [that BizDevOps depends on],” he said. Obviously, you can’t change everybody, so hire people who embrace digitization. “They’re young, they’re just out of college.”

He’s not suggesting all hires must be young to embrace new ways of working. “What we’re experiencing now is a once-in-a-generation change,” he said. “You can learn it, internalize it and reinvent yourself. It’s a decision we can all make.”