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Category: ITIL

  • DevOps and ITIL Integration: Driving Collaborative Agility in ITSM

    DevOps and ITIL Integration: Driving Collaborative Agility in ITSM

    Businesses face continuous pressure to deliver high-quality IT services efficiently. One emerging integration to meet these goals is between DevOps and IT service management (ITSM), more commonly known as DevOps+.

    DevOps+ is becoming more than a trend and has emerged as a powerful collaborative approach to meet these challenges. By combining the strengths of DevOps’ agile software development practices and ITSM’s structured service management approach, organizations can foster better collaboration, enhance agility and improve overall service delivery efficiency.

    DevOps and ITSM Symbiosis

    DevOps and ITSM traditionally operated as separate frameworks with distinct focuses. DevOps emphasizes rapid software delivery and collaboration between development and operations teams. On the other hand, ITSM-based ITIL (information technology infrastructure library) provides best practices for ensuring service stability, reliability and adherence to governance and compliance.

    Integrating DevOps and ITIL creates a symbiotic relationship, allowing organizations to leverage the best of both worlds. DevOps+ encourages a cultural shift, breaking down silos and promoting cross-functional communication. This collaboration enhances the entire software development lifecycle, leading to faster feedback loops, accelerated innovation, and increased responsiveness to business needs.

    Enhancing Agility and Speed

    DevOps+ integration brings greater agility and speed to IT service management. Organizations can streamline the release process by automating deployment pipelines and implementing continuous integration and continuous delivery (CI/CD) practices, reducing time-to-market for new features and updates. Agile development methodologies enhance the adaptability of ITSM processes to meet evolving business requirements effectively.

    Ensuring Stability and Reliability

    While agility is crucial, it must not compromise service stability and reliability. ITIL’s best practices provide a strong foundation for maintaining service quality. By incorporating ITIL processes such as incident management, change management and problem management, organizations can effectively manage risks, reduce service disruptions and ensure the stability of critical IT services.

    Leveraging Automation and Orchestration

    Automation is central to both DevOps and ITIL. Through integration, organizations can leverage automation and orchestration tools effectively. Automated testing, infrastructure provisioning and configuration management improve efficiency and consistency across the ITSM lifecycle. This enables teams to focus on higher-value tasks, reducing manual errors and operational overhead.

    Emphasizing Customer-Centricity

    DevOps+ puts the end-user experience at the forefront of service delivery. By continuously seeking customer feedback and measuring service performance, organizations can better align their offerings with user expectations. A customer-centric approach fosters higher satisfaction, loyalty, and competitive advantage.

    Breaking Down Barriers to Change

    Change management is vital in ITIL, ensuring that modifications are planned and controlled. In the context of DevOps+, change management becomes more adaptive and responsive, facilitating a culture that embraces change as an integral part of the development process. This promotes experimentation, reduces resistance to change, and accelerates innovation.

    Challenges of DevOps and ITIL Integration

    Despite the numerous benefits of DevOps+ integration, organizations may encounter challenges during implementation:

    Cultural Shift: Merging DevOps and ITIL requires a cultural shift. Overcoming resistance to change and fostering collaboration between traditionally separate teams can be challenging but is crucial for success.

    Process Alignment: DevOps and ITIL have different processes and terminologies. Aligning these methodologies while preserving their individual strengths can be complex.

    Skill Sets: Integrating DevOps and ITIL necessitates upskilling teams in both domains to operate effectively in the integrated environment.

    Tooling and Automation: Adopting suitable tools and technologies that support DevOps+ practices and integration is vital but may require significant investments.

    Metrics and Performance Measurement

    Measuring the success of DevOps+ integration is essential to understand its impact and effectiveness. Key performance indicators (KPIs) can help monitor progress and identify areas for improvement.

    Deployment Frequency: The frequency of software deployments indicates the efficiency of the integrated approach. Higher deployment frequency implies faster and more reliable releases.

    Mean Time to Resolve Incidents (MTTR): MTTR measures how quickly teams can resolve incidents and restore services after disruptions occur. A reduced MTTR demonstrates improved service reliability and faster incident resolution.

    Customer Satisfaction Scores: Measuring customer satisfaction provides insights into how well the integrated approach meets end-users’ needs and expectations.

    Continuous Improvement and Feedback Loops

    Continuous improvement is at the core of DevOps+. Establishing feedback loops and conducting regular retrospectives empowers teams to identify areas for improvement and implement iterative enhancements to processes and practices.

    Implementing DevOps+ integration requires a well-structured roadmap. For example, assessment and planning help leaders evaluate the organization’s current ITSM practices to understand how DevOps and ITIL principles align and define integration vision and goals.

    Additionally, your continuous optimization efforts allow you to continuously monitor performance metrics, gather feedback, and conduct retrospectives to identify improvement areas, leading to better team collaboration and communication. Fostering cross-functional collaboration between development, operations and other relevant teams is easily accomplished. To meet these goals, encourage regular meetings, schedule joint planning sessions and enable shared documentation to ensure everyone is aligned on goals and progress.

    When this happens, leverage automation tools to streamline manual processes and increase efficiency. Implement continuous integration and continuous delivery pipelines to automate software deployment and testing. Automation enhances consistency, reduces the risk of human errors and adds a new layer for measuring and evaluating to continuously measure the performance of the integrated approach using defined KPIs. In this vein, you can regularly review progress against the initial vision and goals to ensure the integration aligns with organizational objectives.

    Next, as the DevOps+ integration proves successful in pilot projects, gradually scale and expand it to other teams and departments. Share the success stories and best practices to encourage broader organizational adoption.

    Conclusion

    In conclusion, the integration of DevOps and ITIL, known as DevOps+, marks a transformative approach to IT service management. By combining the agility and rapid deployment of DevOps with the stability and reliability of ITIL, organizations can drive collaborative agility and achieve efficient service delivery.

    DevOps+ integration requires overcoming cultural barriers, aligning processes and upskilling teams, but the rewards are immense. With clear metrics to measure success, organizations can continually optimize their DevOps+ approach to meet customer needs and adapt to evolving market demands.

    As technology evolves, DevOps+ integration will remain at the forefront of cutting-edge IT service management, empowering organizations to embark on a journey of innovation, efficiency and excellence.

    By embracing DevOps+ as a guiding philosophy, organizations can cultivate a culture of collaboration, continuous improvement and customer-centricity, laying the groundwork for a prosperous future in the dynamic world of IT service management.

  • SolarWinds Adds Service Management Platform to Portfolio

    SolarWinds Adds Service Management Platform to Portfolio

    SolarWinds has added a service management capability to its portfolio as part of an effort to make it simpler for IT teams to align support services with specific functions.

    At the same time, SolarWinds has updated its SQL Sentry observability tool for databases and DataOps workflows to add query-level wait statistics along with a revamped web portal and dashboard for tracking the overall health of a database environment.

    SolarWinds Enterprise Service Management (ESM) makes it possible for IT teams to manage IT services based on the ticketing systems, knowledge bases, service portals and request catalogs available for specific departments such as marketing or human resources.

    Kevin Kline, senior staff technical marketing manager for SolarWinds, said that as IT management continues to evolve, there is a greater need to streamline and optimize workflows based on the insight from observability platforms. As a result, there is now a need to apply observability more broadly to include database platforms alongside DevOps and other IT workflows, he noted.

    The overall goal is to eliminate the blame game that results when IT platforms are managed in a more siloed fashion, added Kline.

    Most IT teams are still relying on monitoring tools to track a set of predefined metrics in the hope that an alert will enable them to proactively address an issue before it leads to a major disruption. However, as IT environments become more complex, there is a growing need for observability platforms so IT teams can launch queries and surface issues that predefined metrics aren’t likely to identify. Initially, observability platforms were embraced by DevOps teams, but it’s rapidly becoming apparent that this capability needs to be applied to every IT process. In effect, organizations are on the cusp of democratizing observability, noted Kline.

    Achieving that goal will also become simpler thanks to the rise of machine learning algorithms capable of predicting issues and generative artificial intelligence (AI) tools that will make it simpler to craft queries.

    In the meantime, there is already no shortage of observability platforms, but the SolarWinds approach is different in that, in addition to DevOps workflows, the company has integrated its platform with multiple database platforms to provide a more comprehensive approach, said Kline.

    It’s not clear just yet how observability platforms might impact the way IT teams are structured, but the need for specialists such as database administrators (DBAs) isn’t likely to go away anytime soon, noted Kline. Instead, the level of collaboration across various IT teams should substantially improve.

    In the meantime, IT teams might be well-advised to define an observability strategy today before multiple teams start implementing approaches that might be optimized for a relatively narrow range of use cases. After all, trying to reconcile insights generated by multiple observability platforms may very well defeat the purpose of investing in observability in the first place.

  • Cloud Orchestration and its Impact on DevOps Teams

    Cloud Orchestration and its Impact on DevOps Teams

    Cloud orchestration refers to the coordination and management of various interconnected cloud services and resources to automate and streamline complex IT processes. In simple terms, it is the process of integrating, synchronizing, and automating various cloud-based components to create a seamless and unified workflow. 

    Cloud orchestration aims to optimize the deployment, management, and use of resources in a cloud environment, improving efficiency, reducing costs, and ensuring that cloud services are delivered in a timely and effective manner. It is related to the concept of CloudOps (cloud operations), which uses cloud orchestration concepts to allow more efficient management of cloud resources. Cloud orchestration is a critical aspect of modern IT infrastructure management, as it enables organizations to manage and control their cloud resources more effectively.

    Difference between Cloud Orchestration and Cloud Automation

    Though the terms cloud orchestration and cloud automation are often used interchangeably, they have distinct differences. 

    Cloud automation refers to the process of automating individual tasks and processes within the cloud environment. It involves the use of scripts, templates and other tools to automate repetitive tasks, such as provisioning resources, deploying applications and configuring environments. The primary goal of cloud automation is to reduce the time and effort needed to manage cloud resources.

    Cloud orchestration is a broader concept that encompasses not only the automation of tasks but also the coordination and management of the entire cloud ecosystem. It involves the integration of various cloud services, tools and resources to create a unified, streamlined workflow. Cloud orchestration ensures that all components in the cloud environment work together seamlessly, automating complex processes and managing dependencies between tasks.

    In essence, cloud automation is a subset of cloud orchestration. While cloud automation focuses on automating individual tasks, cloud orchestration takes a more holistic approach, orchestrating multiple tasks and resources to achieve a higher level of automation and efficiency.

    How Cloud Orchestration Enhances DevOps Practices

    Increased Efficiency and Speed

    One of the main benefits of cloud orchestration in a DevOps context is the ability to increase efficiency and speed in the software development and deployment process. By automating and streamlining various tasks, cloud orchestration eliminates manual processes, reduces the potential for human error and accelerates the overall development cycle. 

    Improved Collaboration and Communication

    Cloud orchestration fosters better collaboration and communication between development and operations teams by providing a unified platform for managing cloud resources. This shared platform enables teams to work together more effectively, breaking down traditional silos and ensuring that all stakeholders have visibility into the development and deployment process. 

    Enhanced Infrastructure and Application Management

    Cloud orchestration simplifies the management of complex cloud infrastructure by providing a single, unified platform for cloud optimization—improving the management of resources, applications and services in the cloud.

     This centralized management approach enables organizations to more easily monitor, control and optimize their cloud environment, ensuring that resources are used effectively and that applications are running smoothly. With cloud orchestration, organizations can more easily scale their infrastructure to meet changing demands, ensuring that they have the resources they need when they need them.

    Improved Monitoring and Troubleshooting

    Cloud orchestration provides improved monitoring and troubleshooting capabilities, enabling organizations to more effectively identify and address issues in their cloud environment. With comprehensive monitoring tools and dashboards, teams can gain insight into the performance of their applications, identify bottlenecks and pinpoint potential issues before they become critical.

    Challenges of Integrating Cloud Orchestration With DevOps

    Despite the numerous benefits of cloud orchestration, integrating it into DevOps practices can present several challenges. Some of the common challenges include:

    • Complexity: Cloud orchestration involves the integration of various cloud services, tools and resources, which can be complex and time-consuming. Organizations may struggle with complexity, particularly if they lack the necessary expertise or resources to effectively implement and manage the solution.
    • Resistance to change: Implementing cloud orchestration requires a change in mindset and approach to software development and deployment. As your organization migrates more systems to the cloud, some team members may be resistant to adopting new processes and tools, making it difficult to fully embrace and realize the benefits of cloud orchestration.
    • Security concerns: As with any cloud-based solution, security is a critical concern when integrating cloud orchestration into DevOps practices. Organizations must ensure that their solution is secure and that they have the necessary policies and procedures in place to protect their sensitive data and resources.
    • Cost and resource constraints: Implementing and managing a Cloud Orchestration solution can be resource-intensive, requiring significant investment in infrastructure, tools, and personnel. Organizations must carefully consider the costs and resources required to implement cloud orchestration and determine whether the benefits outweigh the investment.

    Best Practices to Integrate Cloud Orchestration Into DevOps

    To successfully integrate cloud orchestration into DevOps practices and overcome the challenges mentioned above, organizations should consider the following best practices:

    • Assess your organization’s readiness: Before implementation, evaluate your organization’s readiness for change. This includes assessing your existing infrastructure, processes and tools, as well as your team’s willingness and ability to adopt new practices and technologies.
    • Develop a clear strategy: Develop a clear strategy for integrating cloud orchestration into your DevOps practices. This should include defining your goals and objectives, identifying the tools and resources you will need and establishing a timeline for implementation.
    • Choose the right tools and technologies: Select the appropriate tools and technologies that best meet your organization’s needs and requirements. Consider factors such as ease of integration, scalability, security and cost when evaluating potential solutions.
    • Train and educate your team: Ensure that your team has the necessary knowledge and skills to effectively implement and manage cloud orchestration. This may involve providing training and education on new tools, processes and best practices, as well as fostering a culture of continuous learning and improvement.
    • Monitor and measure success: Continuously monitor and measure the success of your implementation. This includes tracking key performance indicators (KPIs), such as deployment speed, resource utilization and application performance, to ensure that your solution is delivering the desired results.

    Conclusion

    Cloud orchestration is an essential tool for organizations looking to streamline and optimize their IT infrastructure, particularly in the context of DevOps practices. By automating and coordinating complex cloud processes, organizations can improve efficiency, speed, collaboration and overall performance. 

    However, successfully integrating with DevOps can be challenging, requiring a clear strategy, the right tools and technologies and a commitment to continuous improvement. By following the best practices outlined in this guide, organizations can overcome these challenges and fully realize the benefits.

     

     

  • Survey Sees Value But Limited Understanding of Platform Engineering

    Survey Sees Value But Limited Understanding of Platform Engineering

    Platform engineering is emerging as method for managing DevOps at scale, but a survey suggested that many organizations are still not clear on the concept.

    A survey of 100 DevOps engineers, platform engineers, developers, site reliability engineers and IT managers conducted by Civo, a cloud service provider, found 85% of respondents work for organizations that have partially or fully embraced a DevOps culture, with nearly two-thirds (65%) identifying platform engineering as the next evolution of DevOps. Nearly, a third (30%) of respondents work for organizations that are already currently evaluating whether they should adopt platform engineering.

    Overall, the survey found 50% of the respondents at organizations that have adopted platform engineering reported improved standardization, with 43% also seeing faster product delivery.

    However, only 41% of the respondents in DevOps roles that were familiar with platform engineering said they had a moderate to high level of familiarity with the concept.

    Saiyam Pathak, director of technical evangelism at Civo, said the survey makes it clear that, in terms of understanding how platform engineering best practices should be implemented, there is need for more clarity. Many organizations initially adopted DevOps to spur innovation, so any approach to centralization shouldn’t come at the expense of developer productivity, he noted.

    Historically, DevOps adoption of DevOps workflows in most organizations has been driven from the bottom up. As a result, it’s fairly common for multiple application development teams within an organization to have adopted multiple DevOps platforms to manage workflows. Advocates for platform engineering are making a case for eliminating many of those redundant platforms in favor of, for example, a single continuous integration/continuous delivery (CI/CD) platform that serves as a corporate standard.

    At the core of those efforts is the assumption that centralization will reduce costs by eliminating redundancy. At a time when more organizations than ever are sensitive to the cost of IT, there is always going to be a natural tendency to rationalize platforms. Challenges arise, however, when centralization results in a loss of flexibility. Many of the teams adopt DevOps because they want to be able to add and replace tools as they see fit. If there is a corporate standard that narrows their tool options, those teams may start to look for ways to do an end-run around the corporate standard.

    The philosophical IT battle between advocates for centralization and proponents of decentralization has been around since the first PCs showed up in enterprise IT environments. As always, organizations need to strike a balance between both extremes, said Pathak.

    It’s not clear how much platform engineering might accelerate the pace at which applications are built and deployed, but the total cost of DevOps should decline as various tools and platforms are consolidated. The challenge, of course, is that centralizing DevOps workflows is likely to disrupt the application development workflows on platforms that may about to be rationalized.

    Regardless of what platforms become the corporate standard, the one thing that is certain is the cultural challenges associated with successfully making that transition will loom large within most organizations.

  • BMC Adds Generative AI Capabilities to Helix Management Platform

    BMC Adds Generative AI Capabilities to Helix Management Platform

    BMC today revealed it is embedding generative artificial intelligence (AI) capabilities across its BMC Helix service and operations management platforms.

    BMC CTO Ram Chakravarti said the company is incorporating the open source GPT-J large language models (LLMS) to build generative AI models. Those models are optimized for specific domains using data collected by BMC and imported from other data sources, like the Jira project management software from Atlassian. That persistent layer of storage is designed to aggregate multiple types of data within what amounts to a Helix data lake.

    The generative AI capabilities added to BMC’s portfolio include natural language summaries of issues and events captured by BMC Helix Operations Management, along with an ability to create a historical analysis of a user’s specific environment to identify recurring issues more easily.

    BMC Service Management, meanwhile, can now correlate incidents and provide resolution summaries that can be stored. It’s also now possible to run clustering analysis on chat archives to find new or improved utterances that help virtual agents answer queries better and execute workflows.

    BMC initially opted to build its own LLMs because general-purpose LLMs—such as the one created by OpenAI—are subject to “hallucinations.” Hallucinations are created when incorrect data is included in a model. BMC is training its LLMs on a narrow range of trusted data to ensure the recommendations and subsequent automations it creates are valid.

    Over time, BMC will add additional LLMs; the company hasn’t yet decided if there are use cases where it makes sense to rely on an existing general-purpose LLM to add additional capabilities, noted Chakravarti.

    Organizations can also opt to use a containerized version of the BMC LLM if they prefer, but BMC will encourage most organizations to invoke generative AI via its Helix SaaS platform, he added.

    BMC Helix is an IT services management (ITSM) platform that integrates with a range of DevOps platforms. Helix is part of a larger existing BMC Connected Digital Ops initiative that seeks to unify ServiceOps and DevOps with AIOps, DataOps and AutonomousOps. Generative AI will automate a range of tasks from within Helix that can then be extended to DevOps platforms such as Jenkins.

    It’s still early days as far as applying generative AI to processes, so organizations should focus on use cases where it will add some immediate value, said Chakravarti. Once they develop a level of trust in the automation created and executed, they can then pragmatically expand the number of generative AI use cases where they make sense, he added.

    One way or another, generative AI is going to be applied to the management of IT. Many routine tasks that conspire to limit the productivity of IT teams today will be eliminated, but there will always be a need for a human to remain in the loop to ensure the right outcomes are achieved. However, it will also soon become possible for existing teams to manage IT at levels of scale that were previously unattainable.

    Image source: Photo_Credite_Indira-Tjokorda via Unsplash

  • Dell Looks to Expand Pool of Available DevOps Expertise

    Dell Looks to Expand Pool of Available DevOps Expertise

    Dell Technologies has quietly launched a training and education initiative to significantly expand the number of IT professionals that have DevOps skills.

    Brad Maltz, senior director for DevOps and developer relations ecosystems for Dell, said the next era of IT will require IT professionals that understand how to automate the management of application development and deployment. Given its long infrastructure history, Dell is especially interested in helping IT teams to use a mix of graphical tools and command line interfaces (CLIs) to programmatically deploy software at much faster rates and at scale, he added.

    Dell has already hired more than 200 specialists to provide that training as part of a DevOps Transformation Services consulting services engagement, said Maltz. The primary area of focus will be working with IT organizations that are embracing platform engineering to centralize the management of DevOps and GitOps workflows, he noted. Many of those teams are now relying on declarative automation tools that ultimately make DevOps more accessible to a wider range of IT professionals, added Maltz.

    The number of IT professionals that employ graphical tools based on IT management frameworks such as ITIL still far outnumber the number of IT professionals that have DevOps skills. The ongoing debate between these two camps essentially comes down to the need to be able to consistently manage IT at scale versus being able to adroitly keep pace with the needs of organizations that are increasingly dependent on software. In the digital transformation era, it’s become clear that more of the organizations that hire IT professionals are placing a higher value on agility, said Maltz.

    Bridging the divide between ITIL and DevOps to address that requirement is largely a psychological challenge that requires IT teams to address a range of cultural issues in addition to mastering different types of tooling, he added.

    Regardless of whether organizations continue to manage IT infrastructure internally or rely more heavily on managed services provided by Dell, the need to continuously deliver secure software faster has become a business issue. The trouble is the level of DevOps expertise within organizations tends to vary widely. A recent survey published by the Continuous Delivery (CD) Foundation found only 22% of respondents are using both continuous integration (CI) and continuous delivery (CD) to automate all building, testing and deployment of code in a production environment. Most organizations that have adopted DevOps make extensive use of CI to build applications, but CD has proven to be more challenging to master. In fact, the CD Foundation survey found only 7% of respondents described themselves as DevOps engineers or specialists.

    The bulk of the managed services that Dell provides are focused on managing infrastructure rather than deploying software, but the company does have a managed DevOps offering based on the application development and deployment platform that an internal Dell DevOps team built to construct and deploy applications. Naturally, Dell has a vested interest in accelerating the rate at which software can be deployed on IT infrastructure it provides and manages.

    It may be a while before the overall size of the DevOps community expands, but the training initiatives such as the one launched by Dell will help. However, it may become a lot easier to find and retain that talent in the months and years ahead. In the meantime, organizations would be well-advised to invest in upskilling their own IT teams. After all, it’s less expensive to train someone that already works within an IT organization than it is to compete against thousands of other organizations willing to pay top dollar for the best DevOps expertise money can buy.

  • Device42 Brings Power and Carbon Emission Tracking to IT Asset Management

    Device42 Brings Power and Carbon Emission Tracking to IT Asset Management

    Device42 today made available a power and CO2 Summary Dashboard to its agentless platform that employs machine learning algorithms to discover and track IT assets.

    Raj Jalan, Device42 CEO, said the dashboard makes it possible for IT teams to combine the tracking of assets and associated dependencies with a capability that enables them to reduce power consumption costs and levels of carbon emissions.

    It’s not clear yet how carbon emission tracking will be integrating into IT management frameworks, but as sustainability becomes a more pressing issue, it’s only a matter of time before IT teams will be required to monitor power consumption and associated carbon emissions more closely, said Jalan.

    The Device42 dashboard simplifies that task by identifying carbon emissions by location, business unit, vendor or application. Armed with that data, it then becomes possible to consolidate servers and applications or move workloads to more efficient platforms, added Jalan.

    Arguably, the biggest challenge when it comes to managing IT infrastructure is the number of organizations that are still using spreadsheets to do so. The Device42 approach to discovery and assessment uses the application programming interfaces (APIs) exposed by cloud services providers and IT vendors. Once mapped, it then becomes possible for IT teams to use a search function to uncover, for example, dependencies between multiple services.

    In theory, it should be possible for DevOps teams to also programmatically implement policies based on the Device 42 data to move workloads in way that reduce IT infrastructure costs. Over the years, many organizations have lost track of how workloads have been deployed in, for example, cloud computing environments—only to discover a surprise expense at the end of the month.

    In fact, one of the hottest segments of IT these days is the emergence of financial operations (FinOps) as a discipline to control cloud computing costs. Fundamentally, FinOps is the discipline that promotes a shared responsibility for an organization’s cloud computing infrastructure and costs. Rather than having disparate procurement teams working in silos to identify and approve costs, business, financial and IT leaders establish policies and best practices for usage that are programmatically enforced.

    It’s still early days as far as the adoption of FinOps is concerned, but adding that capability first requires a deep appreciation for what IT assets are being used to run which workloads. From there, it then becomes possible to not only optimize usage but also reduce both electricity costs and carbon emissions.

    Of course, the tracking of carbon emissions right now is a bigger issue in Europe and elsewhere than it is in the U.S. However, a global framework for trading carbon credits is now impacting IT teams as organizations look to minimize their carbon footprints. Sustainability has become an economic issue.

    In the meantime, DevOps teams should expect that soon, power consumption and carbon emissions will be added to the list of metrics they are measured on.

  • Cisco Streamlines Licensing Options for IT Operations Platform

    Cisco Streamlines Licensing Options for IT Operations Platform

    Cisco has reduced the number of licensing options for its Intersight operations platform to two as part of an effort to make the software-as-a-service (SaaS) platform for managing on-premises and cloud computing environments more accessible.

    In addition, Cisco has added a Cisco UCS X-series Modular System to its portfolio based on a two-socket server configured with 4th Gen Intel Xeon Scalable processors and an instance of Intersight that has been extended to make it possible to apply policies that limit energy consumption.

    Mahesh Natarajan, senior director of product management of cloud and compute for Cisco, said Intersight is a complement to the management platform the company already bundles with its Unified Compute Systems (UCS) platforms for on-premises IT environments.

    Essentials enables management of server profiles/policies, managing firmware updates and proactive health monitoring and security advisories along with many other capabilities. The Advantage option adds low-code/no-code orchestration, more advanced server operations, multi-domain visibility and third-party integration with, for example, third-party IT service management (ITSM) platforms.

    In the wake of the COVID-19 pandemic, there’s been a shift toward cloud-based platforms that make it simpler for IT teams to centrally manage multiple IT environments from anywhere. Cisco Intersight, in addition to supporting Cisco servers, has been integrated with cloud services from Amazon Web Services (AWS), Google and Microsoft to achieve that goal.

    In fact, as the management of IT continues to converge across multiple platforms, the concept of a data center has become something of a misnomer, said Natarajan. Integrated application workloads are now routinely distributed across on-premises and cloud computing platforms that are simpler to manage using a consistent set of policies, he added.

    A SaaS platform also provides the added benefit of reducing the total cost of IT because the need to deploy management platforms for each environment is sharply reduced, noted Natarajan.

    Very few organizations are running all their application workloads in one environment, so the need to reduce costs by finding more efficient ways to manage IT operations is becoming a more pressing issue. Most IT teams are trying to limit the number of full-time employees they might otherwise hire by trying to improve the overall productivity of the teams they do have.

    Of course, centralizing the management of IT is as much a cultural challenge as it is technical. Each IT team that deploys a workload on a platform has a natural bias toward using the management framework made available by the platform provider. Each of those platform providers is now extending its reach as part of an effort to become the one control plane that rules them all. Cloud service providers, for example, are extending the reach of their management frameworks into both local data centers and edge computing environments.

    Regardless of preferences, the cost of licensing multiple management platforms can quickly become prohibitive, especially if they provide redundant capabilities. Organizations looking to reduce the total cost of IT would much rather reduce licensing costs than lay off the IT staff that drive a wide range of digital processes. The challenge, as always, is getting everyone in the IT organization to agree on what that one management platform should be.

  • 3 Performance Challenges as Chatbot Adoption Grows

    3 Performance Challenges as Chatbot Adoption Grows

    Today, many organizations are starting to use voice or text-enabled chatbots for the first time or already have chatbot systems in place. In fact, Gartner said 25% of organizations are predicted to make chatbots their primary customer communication channel by 2027. This is because consumer behavior has shifted to primarily digital interactions and away from traditional customer support channels.

    As a result of today’s digital landscape, consumers are now accustomed to immediate gratification and expect instant responses and resolution from customer support, which makes chatbots an ideal solution for brands to rapidly serve customers. Due to this rising demand for instant customer service, chatbot systems have become more sophisticated and consumers’ reliance on them is increasing. This increase in adoption has led to higher performance expectations for bots and if those expectations are not met, customer satisfaction and brand loyalty will suffer.

    Below, we’ll look at three major challenges tied to the growing popularity of chatbots as they handle larger and more diverse groups of customers, and how business leaders developers and DevOps and customer experience (CX) teams can tackle these common challenges with a continuous testing approach.

    The Pain Points of Adoption: 3 Key Challenges as Chatbot Usage Grows

    As the popularity of chatbots explodes for customers around the world, the underlying systems that handle natural language processing (NLP), latency, data security and other functions need to be stronger. This is due to a growing and increasingly diverse demographic of users, many of whom are sharing more of their personal information to address a wider range of customer needs. Common chatbot pain points can be summarized by three overarching challenges.

    Challenge #1: Chatbots must contend with a larger and more diverse base of customers

    Chatbots that are voice or text-enabled must be smart enough to operate securely while navigating countless variations in spelling, a wide range of spoken accents and vernacular, background noise, static from bad connections and more. Even in a single language, there may typically be only four or five “intents” from most customers in engaging a chatbot, but those intents may be phrased in hundreds of different ways. Now, the global growth in adoption raises the chance of poor connections and exponentially increases the number of languages and variations in phrasing that a chatbot must understand and interpret.

    Challenge #2: Customers are trusting chatbots with more personal and sensitive data

    As chatbots become smarter and more trusted by customers to meet their needs, people are getting more comfortable giving their personal data to chatbots. That increases the number of situations where chatbots are working with personally identifiable information (PII) or other highly sensitive data. Especially given GDPR and other data privacy regulations being enacted worldwide, there’s added pressure to ensure a chatbot system’s handling of this data remains secure and compliant across all use cases and geographic regions.

    Challenge #3: Modern chatbots require 24/7 availability and reliability

    In an era of flexible work and millennial and Gen Z audiences interacting with bots after business hours and on weekends, chatbots can’t afford to work in shifts. Users may be spread across many time zones or around the world, meaning there’s always a sizable portion of customers awake, online and looking for fast and efficient service. Because of this, chatbots must be able to handle spikes and heavy traffic 24/7.

    The executive takeaway from these challenges is that modern chatbot investments must be built around dynamic systems capable of adjusting to shifting customer usage and performance requirements.

    A Continuous Testing Mindset is Required for Top Chatbot Performance

    Fortunately, by honoring a few best practices and making the right strategic technology investments in automated artificial intelligence (AI) and machine learning (ML)-driven systems, C-suite leaders responsible for CX and the teams they manage can deploy chatbots capable of keeping up with the above challenges. Perhaps the most central element for success is to embrace a continuous testing approach.

    For example, when a company is undergoing digital transformation and shifting its contact centers and supporting systems to the cloud, they are moving a significant amount of data, reimagining complex integrations and rearchitecting processes. In order to maintain application stability and existing integrations in the contact center, continuous testing is critical to ensure that an organization’s systems still operate effectively throughout the cloud migration process. With continuous testing, development and testing aren’t separate processes. The testing is automated and quality assurance is involved as developers submit code.

    The most effective approach to continuous testing includes end-to-end analysis across all channels. Testing chatbot behaviors should mimic real-time customer interactions. This should include challenging scenarios such as unexpected user inputs and usage spikes at random times of the day and night. Automation is critical, especially in peak demand scenarios such as Black Friday, Cyber Monday, open enrollment, etc. Load testing up to 1,000-100,000 bot requests per second, or more, can help ensure companies will be able to handle the increased traffic. Ideally, the testing approach should be comprehensive to include automated NLP score testing, conversational flow testing and security testing and performance testing and monitoring.

    By promoting the continuous testing approach across contact center operations, executives can empower their teams to build and maintain chatbots that remain optimized on an ongoing basis and that form an essential part of providing exceptional CX. This will make a huge difference in performance and reliability as systems scale and adapt to the evolving demands of a growing customer base.

    Continuous Testing Elevates Chatbot Reliability and Value

    High-performing chatbots and the continuous testing that keeps them optimized at those performance levels can benefit any company by lowering risks and enhancing revenue. Organizations that become adept at applying continuous testing avoid 90% of the downtime from high-severity (SEV 1) errors, which can result in saving $2.2 million in costs over a three-year period. When continuous testing touches every stage of the development process, it identifies issues earlier and helps strengthen an organization’s ability to innovate faster and service its customers effectively and profitably.