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Tag: automation

  • Survey Surfaces Raft of Enterprise IT Challenges

    Survey Surfaces Raft of Enterprise IT Challenges

    A survey of  enterprise IT leaders found less than a third (32%) are extremely confident that they have the right technology, processes and people in place to execute an effective approach to IT management.

    The survey, conducted by Rocket Software, polled 275 U.S. IT leaders in organizations with more than 1,000 employees. A full 93% of IT leaders are also committed to maximize the value of investments in existing platforms.

    As a result, the top priorities for IT leaders are hybrid cloud computing (65%), data and content management (68%) followed by DevOps and infrastructure and application modernization, tied at 58%, and automation (57%).

    A total of 62% noted they have a much greater focus on efficiency due to the economic landscape, with faster DevOps processes, automated processes and increasing overall output as the top three improvements that would be most impactful for increasing efficiency.

    The survey also suggested many organizations still have much work to do to achieve those goals, with 63% of respondents spending anywhere from six to 15 hours on manual data entry and analysis each week.

    Phil Buckellew, president of infrastructure modernization for Rocket Software, said most organizations are still trying to determine the right level of DevOps to adopt. Few organizations have implemented DevOps workflows on an end-to-end basis, with many employing a mix of DevOps and waterfall processes to build and deploy applications across a hybrid cloud computing environment, he noted.

    Overall, IT leaders are most worried about improving overall IT performance (60%), data security (50%), process risk and compliance (46%) and the need to improve agility (41%), the survey found. Respondents also noted that they measure success within their IT organization by increased efficiency (71%), optimized resources (67%) and reduced risk (63%).

    The survey makes it clear that, despite challenges, enterprise IT organizations are committed to hybrid cloud computing as they become more comfortable with using various platforms that are fit for purpose, said Buckellew.

    Mainframes, for example, continue to play a major role in running online transaction processing (OLTP) applications alongside analytics applications that are now being infused with artificial intelligence (AI) capabililities, noted Buckellew.

    A quarter of survey respondents also identified providing cloud applications with access to mainframe data as an area where they need additional tools, followed by automation (22%).

    The biggest automation obstacles are the complexity of implementing automation (18%), resistance to process change among workforce (18%) and lack of skillsets to implement automation (18%), the survey finds.

    As always, the biggest challenge in enterprise IT environments is often simple inertia. Organizations that have been building and deploying applications on mainframes, for example, have been relying on waterfall processes for decades. Many of them have now adopted DevOps processes to varying degrees, but few have completely replaced legacy approaches to building and deploying software, noted Buckellew.

    It’s not likely the management of complex hybrid cloud computing environments will be simplified any time soon, but progress continues to be made. Soon, AI may automate many existing manual tasks. In the meantime, however, the number of enterprise IT organizations standardizing on a single IT platform remains few and far between.

  • Coming Soon: AutoOps

    Coming Soon: AutoOps

    I’ve spent a lot of time looking at products in the DevOps toolchain and paying attention to where they are going, what they are doing that is new and how they are differentiated. That has led me to one inescapable conclusion: AutoOps is on the way.

    What is AutoOps?

    AutoOps is full robotic automation; the continuing progression of what has been building for the last couple of years. We have DevOps, GitOps, DevSecOps, IaC, CloudOps, AIOps … and the list is still growing. But they all move toward one thing: AutoOps.

    Now that development has been fully integrated into DevOps, the market is moving to do the same for operations. It is harder with operations because DevOps had Agile, but it is coming. And it is coming to your org.

    That is a good thing, in my opinion. Compliance almost demands it; what we currently call AI enables it and staffing in hard macroeconomic times will make it appealing.

    The questions that you should start asking now are what does the organization need to enable rapid deployment, management and upgrades while leaving humans in control for the majority of decisions?

    Some of you are living this. I have a friend that is in a GitOps shop with automated CloudOps where they are implementing AIOps to handle minor day-to-day things and monitoring for drift … but they’re the exception; most of us are nowhere near there.

    Knowing that is where we are heading means you can plan – and it is glaringly obvious this is where we are heading. Vendors are branching from their current subset of XOps to take us there and eventually, we will have a full end-to-end vendor that can do everything from GitOps to fully automated rollout, green/blue deployment, monitoring and drift correction with human intervention only where the organization wants it. Some vendors are already very close to this environment and I expect that several will get there in the next year or two via acquisitions or partnerships.

    We just don’t think of the overall DevOps/concept to retirement SDLC process in the absolute terms that AutoOps requires, but we’re going to want to because the environment is changing. The changes are for the better, but they will be massive and cause disruptions. However, they’ll make everything better in the end by eliminating error-producing bits while leaving us in control of the flow and process.

    Start Planning for AutoOps

    So start planning, understand what your organization wants and needs from the coming full-robotic automation I call AutoOps, and keep kicking rear. Eventually, we’ll have systems that can do a lot of Dev and Ops work, but that day is a ways off, and will require people to manage the systems that manage the systems… Funny but true.

    *I’ve never seen AutoOps used anywhere with regards to IT, I just thought it describes where we’re headed pretty well. Let me know if you’ve seen a better term coined by a smarter person. Or a not-smarter person that has a cooler name for it.

  • Mastering DevOps Automation for Modern Software Delivery

    Mastering DevOps Automation for Modern Software Delivery

    DevOps automation services are a crucial aspect of modern software development that helps organizations to improve their software delivery processes and keep up with the pace of innovation. With DevOps automation, developers can automate repetitive and time-consuming tasks, enabling them to focus on writing code and delivering features faster and with higher quality. By automating processes such as continuous integration, continuous delivery and infrastructure-as-code, organizations can reduce costs, improve collaboration between teams and respond quickly to changing business needs and customer requirements.

    In this article, we will discuss the basics of getting started with DevOps automation services, including popular tools and best practices that organizations can use to implement a successful automation strategy.

    Agility and Efficiency for Smart Digitalization

    Emerging technologies like multi-cloud, IaC, AI/ML and IoT have only increased the importance of DevOps automation, as they bring new challenges demanding agility, flexibility and efficiency in software delivery processes. Multi-cloud and hybrid cloud environments, for example, can benefit from automating the deployment and management of software across multiple cloud environments. Similarly, AI/ML and IoT require organizations to collect and process large amounts of data in real-time, which can be challenging without automation. DevOps automation can help organizations automate data pipelines and analytics, enabling them to process data faster and more accurately.

    Here are some benefits of DevOps automation:

    ● Speed and Efficiency: DevOps automation enables organizations to deliver software faster and with higher quality. By automating repetitive and time-consuming tasks, developers can focus on writing code and delivering features.
    ● Agility and Flexibility: DevOps automation provides organizations with the agility and flexibility to adapt to changing business needs and customer requirements. Automated processes make it easier to release software updates and respond quickly to market demands.
    ● Cost-Effectiveness: DevOps automation reduces the costs associated with software development by reducing manual labor and minimizing errors. It also enables organizations to use cloud resources more efficiently, which can result in cost savings.
    ● Improved Collaboration: Bringing the different stakeholders along with devs and operations, DevOps fosters a collaborative approach toward enhanced CI/CD. By automating processes, teams can work together more effectively and share knowledge and feedback in real-time.
    ● Scalability: DevOps automation enables organizations to scale their software delivery processes quickly and efficiently. Automated processes make it easier to deploy software across multiple environments and platforms, which is essential for organizations that need to scale rapidly.

    Getting Started With Efficient DevOps Automation

    Implementing DevOps automation requires a well-planned strategy, best practices and the right set of tools. In this section, we will discuss how to successfully implement DevOps automation in your organization.

    Understanding DevOps
    Before you begin with automation, it is essential to understand the principles and practices of DevOps. DevOps is a culture that emphasizes collaboration and communication between software development and IT operations teams to build, test and release software more quickly and reliably. There are some common tools that DevOps teams use for their work, collaboration and communication:
    ● GitHub: The Git repository hosting service used for version control and source code management functionality, especially for distributed projects.
    ● JIRA: A project management tool that helps teams plan, track and manage software development projects.
    ● Slack: A communication platform that enables real-time messaging, file sharing and collaboration.

    Choose the Right Tools
    Automation is at the heart of DevOps, and selecting the right tools is crucial. Choose tools that fit your requirements, budget and infrastructure. Some popular DevOps automation tools include:

    ● Jenkins: An open-source automation server that enables continuous integration and continuous delivery (CI/CD) pipelines.
    ● GitLab: A web-based Git repository manager that provides CI/CD capabilities, project management, and collaboration tools.
    ● Puppet: An open-source configuration management tool that automates the deployment and management of software across multiple environments.
    ● Chef: A configuration management tool that automates the deployment and management of software across multiple environments.
    ● Ansible: Open source tools for automating software provisioning, deployment and configuration management

    Identifying Processes for Automation

    First, list all the critical processes within your organization that can be enhanced with automation. Start with the repetitive and time-consuming tasks that can be automated, such as code testing, deployment and monitoring. There are a number of popular tools that can help with such automation:

    ● Selenium: An open source testing framework that enables automated testing of web applications.
    ● Docker: A containerization platform that enables the creation, deployment and management of containerized applications.
    ● Kubernetes: The open source container orchestration tool is essential in the DevOps pipeline for orchestration and automation of the deployment, scaling and management of containerized applications.

    Creating a Plan

    Once you have identified targets for automation and selected tools, create a plan for automation that includes a roadmap and milestones. Start with small projects and gradually expand the automation to cover all aspects of the software development life cycle. Some popular tools for strategizing these life cycles are:

    ● Trello: A project management tool that enables teams to organize and prioritize tasks using Kanban-style boards.
    ● Asana: A project management tool that enables teams to track and manage tasks using to-do lists, calendars and timelines.

    Training The Team

    Automation requires new skills, so it is essential to train your team to use the tools and processes effectively. Provide training and resources to help your team learn new skills and techniques. Some popular education and training resources include:

    ● Udemy: An online learning platform that provides courses on DevOps automation tools and techniques.
    ● Coursera: An online learning platform that provides courses on DevOps and related topics.
    ● Pluralsight: An online learning platform that provides courses on DevOps automation tools and techniques.

    Implementing Continuous Improvement

    A continuous feedback loop is essential for DevOps’ iterative process and continuous improvement. Integrate mechanisms for evaluating your automation processes, identify areas for improvement and make necessary changes. Here are two common tools used to do this:

    ● New Relic: An application performance monitoring tool that provides real-time visibility into the performance of software applications.
    ● Splunk: An operational intelligence tool that enables organizations to analyze and visualize machine data in real-time.

  • The Future of Continuous Testing in CI/CD

    The Future of Continuous Testing in CI/CD

    If you ever wonder why continuous testing is so important, think about the following: In the past, software testing was typically done after the code was written and sent to the QA department for independent testing. When bugs were discovered, the code was returned to the developers for correction. While this testing method worked, it was time-consuming. Nowadays, companies want to produce high-quality products quickly, so they need a faster and more efficient testing process. This is when continuous testing comes in.

    In this article, we will explore continuous testing from its current state and its future in CI/CD to the emerging technologies and trends that will shape its development. We will also cover the importance of continuous testing for businesses and some of its challenges.

    What is Continuous Testing?

    Continuous testing is a software testing approach in which tests are automated and run throughout the software development life cycle. It is a part of the continuous delivery (CD) process, in which software changes are delivered to customers continuously and iteratively.

    Continuous testing aims to identify defects and issues as early as possible, reducing the cost and time of fixing them later. Continuous testing provides immediate feedback to the development team, helping them to detect and address potential problems quickly and efficiently.

    According to the Reports and Data source survey, 21% of QA professionals have incorporated continuous testing, source code management and continuous integration into their DevOps process to speed up code release.

    Advantages of Continuous Testing

    Here are several advantages of implementing continuous testing as a part of the software development process:
    ● Continuous testing enables teams to identify bugs earlier in the software development process;
    ● Continuous testing provides immediate feedback to developers on the quality of their code, enabling them to address any issues immediately;
    ● You can minimize business risks by assessing and identifying potential issues before they become real problems;
    ● Automating your test cases can reduce the time spent on testing;
    ● Accelerating the process of software delivery to production helps release the product faster;
    ● You can run performance tests in similitude, which can increase the speed of test execution.

    Disadvantages of Continuous Testing

    While continuous testing has become an integral part of the software development process, it’s not without its challenges. As software development teams work to incorporate testing into their CI/CD pipelines, they may encounter obstacles that can hinder their progress.
    ● Continuous testing requires a different skillset than traditional testing, including expertise in DevOps, automation and CI/CD pipelines;
    ● Different tools and technologies are available for continuous testing, and choosing the right ones can be tough. Integrating these tools with other parts of the development process can also be challenging;
    ● To fully realize the benefits of continuous testing, you need to update your testing strategy by moving away from relying solely on traditional testing methods and adopting a well-defined approach to test data management;
    ● Failing to integrate code regularly can result in issues such as duplicated coding efforts and non-compatible code, leading to defects.

    Emerging Technologies in Continuous Testing

    With the increasing demand for faster and more reliable software releases, emerging technologies such as AI/ML, automation and DevOps appeared in continuous testing. These technologies help teams to automate and streamline their testing processes, identify defects more quickly and improve overall software quality. Let’s examine some of them:

    Artificial intelligence/machine learning (AI/ML). AI and ML refer to the field of computer science that deals with developing intelligent machines that can simulate human intelligence and learn from data.

    One of the main benefits of AI and ML in continuous testing is the ability to automate test case generation, which reduces the need for manual testing and frees up time for other tasks. This enables testers to test applications more comprehensively and frequently, ensuring defects or issues are identified and addressed quickly. AI and ML increase testing accuracy by identifying patterns and anomalies in the testing data.

    AI and ML techniques can efficiently and rapidly analyze extensive amounts of data, allowing them to recognize patterns and irregularities in testing data that human testers might overlook.

    Automation: Automation in continuous testing refers to using automated tools and processes to execute software tests repeatedly and consistently. It involves automating various stages of the software testing life cycle, such as test planning, test case design, test execution and test reporting.

    Automation can efficiently perform regression testing, ensuring that changes do not introduce new defects or issues in the software. Tests can be executed 24/7 without human intervention, reducing the time and cost required for testing.

    DevOps: DevOps in continuous testing is the application of DevOps principles to continuous testing, focusing on integrating testing into the software development life cycle and enabling faster and more reliable delivery of software.

    In the context of continuous testing, DevOps emphasizes testing integration into the development process, enabling developers to detect and fix defects early in the cycle. DevOps also promotes automation, collaboration and communication between development and operations teams, allowing faster and more efficient testing and deployment.

    Blockchain: Blockchain technology has been gaining increasing attention in the software development and testing industry due to its ability to provide secure and tamper-proof data storage.

    In continuous testing, blockchain stores testing results and data in a decentralized and transparent way, making it virtually impossible for anyone to modify or manipulate the data. This enhances the credibility of the testing process and outcomes and provides an added layer of security to ensure the integrity of the data.

    Blockchain technology enables efficient and seamless data sharing between stakeholders involved in the testing process, enabling faster and more accurate decision-making.

    Internet-of-Things (IoT): In continuous testing, IoT simulates many IoT devices and platforms, enabling testers to test their applications across multiple devices and platforms. This ensures that applications are compatible with various IoT devices and platforms and perform reliably and consistently in various environments.

    For instance, when creating an application that needs to run on multiple devices, it is possible to use an emulator to test the app on your laptop without setting up a development environment for each type of device.

    IoT-enabled continuous testing provides real-time feedback on the performance and functionality of applications, enabling organizations to identify and address issues quickly and effectively.

    Trends in Continuous Testing

    Continuous testing is rapidly evolving to meet the needs of modern software development practices, with new trends emerging to address the challenges development teams face. Three key trends currently gaining traction in continuous testing are cloud-based testing, shift-left testing and security testing. These trends are driven by the need to increase efficiency and speed in software development while ensuring the highest quality and security levels. Let’s take a closer look at these trends.

    Cloud-Based Testing: Continuous testing is deployed through cloud-based computing, which provides multiple benefits like ease of deployment, mobile accessibility and quick setup time. Businesses are now adopting cloud-based services due to their availability, flexibility and cost-effectiveness. Cloud-based testing doesn’t require coding skills or setup time, which makes it a popular choice for businesses.

    AI-Driven Testing: AI-driven continuous testing is often integrated into cloud-based testing platforms, providing end-to-end automation for web, mobile and API testing. AI and cloud-based testing allows for access to testing environments anywhere, anytime, and provides for regular automated testing using platforms like Selenium.

    Shift-Left Testing: Shift-left testing is software testing that involves testing earlier in the development cycle rather than waiting until later stages, such as system or acceptance testing. The goal of shift-left testing is to identify and address defects as early as possible.

    Delaying testing until the end of development often results in more complicated bug fixes since the software is already fully built. This can cause an increase in cost and time to market. Identifying and fixing bugs earlier in the software development life cycle is generally more cost-effective.

    As IBM’s Systems Sciences Institute claimed, repairing a bug discovered during implementation costs roughly six times more than one found during design. If a mistake is detected after product release, the cost of fixing it is four to five times greater than one found during design and up to 100 times greater than one found during the maintenance phase. This suggests that a bug’s cost increases exponentially as the software progresses through the SDLC.

    Low-Code/No-Code: Low- or no-code in continuous testing refers to using visual interfaces and pre-built elements or modules to create and execute automated tests instead of writing complex code. Low-code/no-code testing tools often provide drag-and-drop interfaces and pre-built components that can be combined to create tests, reducing the time and effort required to develop and maintain test scripts.

    This type of testing provides several advantages, such as allowing non-technical business experts to run testing, speeding up test production and execution and enhancing product quality by enabling more thorough and efficient testing.

    Generally, according to statistics, by 2025, around 70% of new business applications are predicted to use low-code or no-code technologies.

    Security Testing: Due to the rising incidence of cybersecurity threats and data breaches, security testing has become a crucial aspect of software quality assurance. In the year 2023, the tech world anticipates that there will be a stronger focus on security testing, with the emergence of more advanced tools and methodologies aimed at detecting and resolving security weaknesses in software applications.

  • Logz.io Taps AI to Surface Incident Response Recommendations

    Logz.io Taps AI to Surface Incident Response Recommendations

    Logz.io this week added a supervised machine learning capability to its observability platform that reduces mean-time-to-remediation by surfacing recommendations for resolving incidents.

    Asaf Yigal, vice president of product for Logz.io, said the Alert Recommendation capability added to the Logz.io Open360 platform uses artificial intelligence (AI) to model the steps a DevOps team needs to complete to resolve an incident.

    The goal is to reduce the amount of time required to resolve incidents at a time when IT environments are becoming increasingly complex, he added.

    In fact, a recent Logz.io survey found 75% of respondents said it currently takes them hours to resolve production issues, with only 14% satisfied with their current mean-time-to-resolution (MTTR). A total of 41% specifically cited monitoring and observability of Kubernetes environments as a primary challenge.

    Alert Recommendation is the latest in a series of investments in AI that Logz.io has made. Previously, Logz.io integrated the ChatGPT generative artificial intelligence (AI) platform to surface links to related information and best practices for resolving IT issues.

    In general, AI tools should make it possible to manage IT at a level of scale that eliminates many of the low-level data engineering and analytics tasks that previously required manual effort from a DevOps engineering team. If, for example, the observability platform is generating recommendations to address issues, there may be less of a need to create runbooks that DevOps teams typically create to address a wide range of known issues.

    Along with making it simpler to discern the root cause of an issue, AI also makes it possible for less experienced members of a DevOps team to resolve an issue using guidance generated by the observability platform, noted Yidal.

    In effect, AI technologies are reducing the cognitive load required to be an effective member of a DevOps team, he added.

    One way or another, it’s not so much a question of whether AI will be applied to DevOps as much as it is to the degree. Many of the manual tasks that often conspire to create DevOps bottlenecks should be significantly reduced in the months ahead as more advances are made. The challenge now is determining how best to reallocate DevOps expertise in anticipation of those advances.

    Of course, there’s always going to be some sense of trepidation when it comes to AI. However, many of the tasks that are about to become automated tend to be tedious. Many DevOps professionals would just as soon see those tasks become automated in the expectation that more time will become available to take on more complex challenges.

    Regardless of the motivation, the way IT is managed is about to change. There may be plenty of instances where AI does not live up to its initial hype, but as AI models are exposed to more data, they will become more accurate. That doesn’t mean, however, there won’t always be a need for a DevOps engineer to ensure those algorithms are behaving as expected.

  • Boomi Brings Generative AI to Integration Platform

    Boomi Brings Generative AI to Integration Platform

    Boomi announced today it is applying generative artificial intelligence (AI) to enable organizations to automate integrations via a natural language interface.

    Boomi CEO Steve Lucas said Boomi AI makes it much simpler for professional and citizen developers alike to create integrations at scale using generative AI platforms to invoke the low-code engine that Boomi already provides to create integrations between thousands of applications.

    Those generative AI capabilities will complement a wide range of AI capabilities based on machine learning algorithms that are already embedded in the Boomi platform, he added.

    Generative AI’s Impact on Integration and Automation – Steve Lucas, Boomi from Techstrong.TV on Vimeo.

    Boomi has been widely used to drive business process integration via an integration platform-as-a-service (iPaaS) environment it delivers as a managed service. The low-code platform the company created significantly reduces the need to rely on professional developers to write procedural code using Java, for example, to create those integrations.

    Generative AI capabilities make it possible for almost anyone of any skill level to create those integrations across multiple types of application programming interfaces (APIs), noted Lucas.

    That capability also makes it possible for Boomi to notify end users when, for example, a data attribute in an application has changed and ask them if they would like to apply that change across their entire application environment.

    In addition, Boomi AI can also interpret the intent of a prompt to better orchestrate processes in addition to providing an audit history in natural language that explains how decisions were made and what outcomes occurred as a result. As part of that effort, Boomi algorithms are also trained to avoid biases and unfairness with established ethical guidelines.

    Boomi’s AI engine is trained using anonymized data collected from approximately 20,000 customers that have created more than 200 million integrations using the Boomi platform. Those integrations have been exposed to third-party large language models (LLMs) that the company is using to train Boomi AI, said Lucas. Boomi is not storing any customer data to train its AI models.
    DevOps teams, of course, spend hours creating integrations that should be much simpler to create, test and validate with the rise of generative AI. In fact, the number of integrations being created to drive a wide range of digital business transformation initiatives should substantially increase.

    In the meantime, DevOps teams would be well-advised to start evaluating which tasks they manually perform today that could instead be automated by a generative AI platform. Many of the routine integrations that DevOps teams create today using low-level APIs will be increasingly automated by end users. The ultimate goal should be to identify more complex tasks that previously DevOps teams would not have attempted simply because there wasn’t enough time.

    It’s still too early to determine what impact generative AI will have on the way IT is managed, but DevOps teams that are typically committed to automating as many processes as possible should be at the forefront of adoption. The challenge is going to be simply keeping track of the rate of change that might soon be enabled by these platforms.

  • 7 Benefits of Self-Contained Apps for BPM

    7 Benefits of Self-Contained Apps for BPM

    According to Gartner, “As business needs change, organizations must be able to deliver innovation quickly and adapt applications dynamically—reassembling capabilities from inside and outside the enterprise. To do this, organizations must understand and implement the ‘composable enterprise.’”

    This is an appealing concept for companies looking for a good balance between highly centralized and highly distributed technologies and information systems.

    For enterprise IT, the use of composable infrastructure – built up of independent components that can be swapped out when needed – is already widely applied. Another place to look for the advantages of composability: Business process management  (BPM) automation. Why? Because IT teams, DevOps teams and leadership are facing a number of pressures:

    • Business teams need responsiveness and speed from IT as everyone continues to look for ways to maximize go-to-market.
    • Current business process automation platforms deploy applications that are all dependent on shared resources. The DevOps team must take interoperability and integration among a group of applications using shared resources into consideration for new developments, new deployments and maintenance. This can be complex, time consuming and can require extensive testing before updates can be deployed.
    • Similarly, whenever the process application platform is upgraded to a new version to take advantage of new features, or when maintenance on a single application is needed, all applications are affected and the need to offline and upgrade everything can impose a high cost in downtime.
    • Scaling grouped business process applications can add unnecessary cost as it is applied across the board even if not all of those applications need to be scaled.
    • The approach of building business applications from interoperable elements promises to address some of these key issues.

    Modularity and component-based systems have already been successfully applied for many years in IT and software development. Infrastructures are composed; we no longer buy machines but CPU, memory, disk space, bandwidth and so on. In software development we see composability applied through containerization, an approach that is already widely used and becoming the standard for DevOps teams, as shown for example by the growing use of platforms such as Docker and Kubernetes.

    Self-contained applications is setting the same trend for composable BPM process-based applications. BPM technologies already fit perfectly into the approach of composable architectures. Applications themselves are also already internally made up of composable elements: Processes, sub-processes, connectors, services, reusable pieces of code, business rules and UI fragments, just to name a few.

    Self-contained apps combine the benefits of containerization approaches and technologies with the power of its BPM application platform technologies for application development. The BPM base offers wide extensibility to integrate with enterprise IT, with good auditability, traceability and compliance; deployment of individual applications using containers offers faster delivery, portability and easier management.

    DevOps teams can deploy complex applications more easily, deliver continuously, and thus support innovation and faster time to market. Each self-contained app can be deployed individually, independently and quickly to deliver increased value through improvements as quickly as the business requires.

    DevOps teams, IT leadership and CIOs will be happy to see self-contained apps because:

    • The containerized approach included with self-contained apps is already familiar to CIOs and their technical teams.
    • The less complex, independent development and deployment of self-contained applications means faster application delivery to production overall. As it’s not necessary to take interoperability and integration among a group of applications into account during development, development is faster, testing is easier and the resulting application is easier to maintain.
    • Scaling an independent application is simpler and it can be applied to each application individually.
    • Application maintenance costs are reduced, as smaller and more regular updates are now possible.
    • Because it’s easier to implement multiple projects, development teams’ efficiency and productivity, and therefore ROI, increase.

    Business will be happy to see self-contained apps because:

    • Automation and business application projects are more easily adapted to business changes; the IT team can respond more quickly and with more confidence due to the smaller scope and less interdependence among other applications.
    • Faster innovation and competitive advantage offers a faster response to new business needs.
  • Optimizing SaaS With Automation and Zero-Touch IT

    Optimizing SaaS With Automation and Zero-Touch IT

    For the first time in 10 years, businesses have slowed their frenetic pace of SaaS adoption. Based on recent BetterCloud research, the average enterprise today uses 130 SaaS applications. While that represents an 18% increase from last year, it’s worth noting that it’s the slowest annual growth rate since we began tracking SaaS usage in 2012—and it will likely continue to trend downward. Let’s examine why.

    The pace of companies adopting new SaaS applications has been fast and furious in the last couple of years. But this trend created a number of challenges. One, SaaS sprawl burdened many employees who had to learn and constantly switch between new apps. Two, the spike in the number of applications brought about serious governance and security challenges for IT. Lastly, it created waste; many applications were never fully deployed, rolled out and adopted by users.

    Fast forward to today; these challenges are compounded by the current macroeconomic headwinds that are driving greater scrutiny on SaaS spend. And the decelerated growth in SaaS adoption is just an early sign. We’re entering into a more deliberate phase of SaaS adoption and optimization as an industry.

    In the coming years, we are going to see the total number of applications used in organizations plateau. The priority in 2023 and beyond will be to better integrate, manage, govern and extract maximum value from the applications that are in place today. We are going to see more organizations continue to embrace automation with the goal of achieving zero-touch IT — the peak point at which the number of manual “touchpoints” by IT support is as close to zero as possible.

    2023 IT Mandate: Automation

    Automation of processes is hardly a new concept, and IT pros believe it will be a big part of their future. In our research, an overwhelming 86% see automation as critical to overcoming SaaS operations (SaaSOps) and security challenges.

    Many have started their journey and are already reporting efficiencies. Of those we surveyed, 71% already automated at least one help desk process, like onboarding or password resets. Additionally, 43% already have a dedicated SaaSOps automation role or team, and another 23% plan to establish one.

    But if the ultimate goal is to reach peak automation, there’s still much work to do.

    Zero-Touch IT: Managing SaaS Sprawl

    At most organizations, many workflows can be automated, replacing all their manual IT touchpoints. Many in the industry call this approach to automation “zero-touch IT” and agree that it’s a top priority. Our findings show 72% believe zero-touch automation is the future of SaaSOps, and 69% are working toward it.

    While it may seem daunting, the journey to achieving zero-touch IT is not out of reach. It does require investment in time, technology and people, however. And once you get there the efficiencies will be apparent. Let’s break these benefits down by category.

    Zero-touch IT helps companies manage their software applications much more effectively. IT groups have historically gotten bogged down in the manual execution of tasks that are complicated and tedious, despite being basic and common. Two processes cited as top concerns for IT professionals, onboarding new employees and offboarding departing employees, are concrete examples.

    But managing the user life cycle of an employee doesn’t just start at onboarding and stop at offboarding. Many changes take place during an employee’s time at the organization—promotions, changes in departments, password resets, new project assignments, etc. And every single time an event like this occurs, some type of action, like giving or revoking access to new files, elevating access rights or taking security steps to prevent unauthorized access is required. This is a lot to keep track of, especially once you multiply these steps across your entire SaaS stack for multiple users.

    Automating all of the necessary steps—or at least some of them—will make the user life cycle management experience much more streamlined and efficient. Not only that, but you’ll reduce the chances of making an error from repeatedly retyping employee details.

    Additionally, going zero-touch with onboarding means that new employees will contribute value on day one. They’ll have almost immediate access to all the devices, apps, data and groups they need to be fully productive. Automating repetitive onboarding steps also does away with scads of request tickets and frees up IT for more strategic work.

    Zero-Touch IT: Governing SaaS Sprawl

    Once upon a time, SaaS security would have solely fallen on security teams. Not so today. Based on our research, 81% of IT professionals now say they are responsible for protecting SaaS and sensitive data. Further, 43% added a new SaaS app that stores sensitive data in the last 12 months.

    This is why zero-touch principles have become so critical to IT; it’s the key to securing and protecting applications and the data in them.

    Here’s a real-world scenario. Imagine you’re offboarding 100+ departing employees. Given this task is most often done manually, a handful of those employees are overlooked and therefore retain access to the company’s systems. By automating the offboarding process, you can ensure you quickly cut off access to every employee leaving the company with zero room for error and reducing the risk of a data breach or other potentially destructive action.

    Or imagine an employee externally sharing a file that contains sensitive business information. This immediately exposes said data. By automating the data detection process, IT now receives an alert every time such a vulnerability is identified. IT can then automate intervention to immediately stop any further data exfiltration and/or restrict file access.

    As more applications are deployed, more high-value data gets spread across them, creating new opportunities for bad actors to breach systems and exfiltrate (steal) information. IT groups recognize that automating SaaSOps strengthened their cybersecurity, with 26% citing better compliance or data security as an outcome.

    The bottom line is that zero-touch IT can provide a more comprehensive and up-to-date understanding of what employees are doing with their corporate and shadow applications and their data in order to uphold security standards.

    Zero-Touch IT: Boosting Operational Efficiency

    Nearly a third (32%) of IT professionals cited improved operational efficiency as the top benefit of automating SaaS management. Zero-touch IT helps organizations do more with less and ultimately save money.

    For example, automation can help IT extract insights into the utilization of apps to determine which resources are needed and which are expendable. IT can then find functionality overlaps between different apps, as well as orphan and ghost apps. This often allows IT to consolidate similar apps. It becomes simple to identify underused applications that do not deliver sufficient value and then discontinue them.

    Shockingly, two-thirds of SaaS apps are unsanctioned at organizations. Automation can help curb shadow IT by catching unauthorized applications in use. But shadow IT isn’t always a bad thing if it’s under control. With the right insights, IT can better understand what applications its employees need to be productive.

    The Future is Brighter With Zero-Touch IT

    SaaS growth may be slowing, but at most enterprises, the SaaS stack remains large, with many IT pros (59%, to be exact) citing management challenges.

    But achieving zero-touch IT won’t happen overnight. It’s a journey.

    Every application has its own idiosyncrasies, protocols and processes. In many cases, applications hold or share sensitive business data. Most organizations are tasked with carrying out all tasks related to the SaaS stack manually, a seemingly impossible task.

    But there is light at the end of the tunnel. This coming year we will consolidate, control, optimize and protect the SaaS stack at new levels — with a little help from zero-touch IT.

    At the end of the day, it’s organizations that prioritize automation that will be able to deliver more time savings, cost efficiencies, value and benefits.

  • Start Automating Now

    Start Automating Now

    A bit of feedback I received from last week’s post is that I didn’t provide any background, I just jumped right in with examples. So let’s take a step back and talk about the bigger picture.

    It is always a surprise to me when I talk to enterprise friends and associates and hear that automation has not taken over their DevOps processes. Yes, we still (and might always) want the ability to have a person review what is being done and make course corrections if the automation has something wrong, but most of the automation that is available for DevOps is no longer new and should be a no-brainer.

    The ability for a build to block based upon any given criteria and notify Jira or ServiceNow—or whatever issue tracking system you use—while alerting the people responsible for the build is simply obvious … and yet, some organizations aren’t even that far yet. The ability to stop a build, create a ticket, assign it to the appropriate person and give it a priority is built into most of these products. It should be looked at to see if it’s suitable to the organization.

    This goes on and on. We’re now to the point where everything in the toolchain can be automated. IaC and GitOps are tackling the broad operations end, and if the application is 100% public cloud-based, CloudOps simply solves the problem. Automation from check-in to production is possible, with very few places where intervention from humans is needed.

    Given that most of this technology is mature—even though some of it has not been around as long but has broad enough adoption that it can be considered mature (Flux and Argo come to mind here)—it is time for enterprises to start another round of automation. Just like AI won’t eliminate most development jobs this year, automation won’t eliminate jobs, either. But we’ve been so strapped for time for so long that this may be a surprise. Automation might reduce the need to scramble for new team members while asking current employees to give a bit more … over and over.

    So get started. The math that made DevOps appealing in the first place—streamlining to reduce time to market and free up IT hours—is the same math that applies to the current round of automation. The more that can be automated, the more time team members will have to perform more productive work.

    Having mentioned AI in passing, I’ll use that as a segue to include AI in the “automation” discussion. At this time, nearly all of the tools we call AI are simply advanced automation tools and should be considered in the process of increasing IT automation. The ability for an app to run, learn your systems and improve them (be that improvement in development, security or ops) is just advanced automation. In the places where it works well in a given environment, it should be adopted. It is highly unlikely that AI will get worse over time, so even marginally acceptable AI results make it worth implementing, counting on technology advancements and continued training to improve that benefit moving forward.

    So, start automating now. Free your time, take a holiday at the beach, do the same job in less time or, to quote a previous generation, “Work smarter, not harder.” You’re doing the job that keeps the servers humming and users coming, now do it with less effort. And keep rocking it.

  • PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty this week made generally available an artificial intelligence for IT operations (AIOps) platform that leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates.

    Jonathan Rende, senior vice president and general manager for PagerDuty, said that approach enables the PagerDuty AIOps platform to begin surfacing valid recommendations sooner than other AIOps platforms. Other platforms first need to be integrated into IT environments by an expensive team of consultants and integrators, he added. The PagerDuty approach makes the benefits of AIOps immediately available to both IT operations teams and application development teams alike, he said.

    The arrival of PagerDuty AIOps comes on the heels of the launch of PagerDuty Process Automation, a platform for orchestrating automation across multiple platforms that is integrated within PagerDuty Operations Cloud. That capability makes it possible for organizations to centralize the management of islands of automation that have emerged across the enterprise without having to replace any framework that has already been deployed, said Rende. There are also more than 700 integrations between PagerDuty AIOps and the PagerDuty Operations Cloud platform.

    Like other platforms, PagerDuty AIOps relies on machine learning algorithms and an AI model created by PagerDuty to correlate events so that the platform can put them in context. Incident response recommendations are then made, which an IT team can accept and automatically implement or reject. The platform then uses those inputs to surface more accurate recommendations over time, said Rende.

    The platform will also automatically group apply defined rules coupled with its machine learning capabilities to aggregate alerts. That capability reduces the overall alert noise that might otherwise be generated, noted Rende.

    Organizations are looking to invest in AI and automation more heavily at a time when many of them are reducing the overall size of their IT staff, said Rende. The challenge with AI, however, is that it can take six months or more for a rival AIOps platform to learn enough about an IT environment to begin making recommendations. The PagerDuty approach takes advantage of the data within the PagerDuty incident management platform to train an AI model more quickly, in a way that reduces the time to generate valid recommendations to a matter of days, noted Rende.

    PagerDuty has been building that AI model for the last four and a half years with that specific goal in mind, he added.

    It’s not clear how quickly organizations are embracing AIOps, but as IT environments continue to evolve, many once-manual tasks are being automated. There is a natural fear of the impact those advances might have on specific roles within an IT organization, but as IT environments get more complex, there will always be a need for IT personnel that can focus on higher-order tasks, said Rende.

    Overall, the rate of IT staff turnover should also decline as much of the toil that contributes to burnout is also eliminated.

    It may be a while yet before AI becomes pervasively applied to manage IT, but the proverbial genie is not going back into the bottle. The challenge is determining precisely where the line between the man-machine interface with IT environments is today versus where it might be tomorrow.