May holds significance at BMC as a month of celebration and introspection, marked by Asian American and Pacific Islander (AAPI) Heritage Month and Mental Health Awareness Month. As active participants in the BMC API employee resource group (ERG), we are acutely aware of the unique hurdles our global communities face, particularly within high-pressure environments like the tech industry. Moreover, staying connected to cultural roots serves as a vital pillar for better mental health among API professionals.

By engaging in traditions, community events, and conversations with family members, we fortify our resilience and sense of belonging. In this blog, we aim to provide practical strategies for managing daily stress, promoting mental well-being, and nurturing a supportive workplace culture.

Effective stress management is not only vital for our mental health but also crucial for enhancing productivity and job satisfaction. Here are our top five tips for effectively managing daily stress:

1. Prioritize regular physical activity: Incorporating at least 30 minutes of exercise into your daily routine can work wonders for your mood and energy levels. Whether it’s a brisk morning jog, a leisurely lunchtime walk, or a post-work cycling session, physical activity has been scientifically proven to uplift spirits. Even short bursts of exercise can yield significant benefits. For the benefits of consistent physical activity, check out Eating Well’s guide.
2. Maintain a balanced diet and hydration: Fueling your body with well-balanced meals and staying adequately hydrated throughout the day can significantly improve focus and energy levels. It’s essential to be mindful of your consumption of caffeine and alcohol, as they can impact stress levels and overall mood. For more details on maintaining a healthy diet, the National Institute of Mental Health provides useful resources and tips here.
3. Prioritize quality sleep: Quality sleep plays a pivotal role in maintaining good mental health. Strive to establish a consistent sleep schedule and cultivate a bedtime routine that minimizes exposure to blue light from electronic devices. This helps regulate your body’s internal clock and enhances sleep quality, leaving you more alert and resilient during the day. Learn more about managing blue light for better sleep at the Sleep Foundation.
4. Embrace mindfulness and relaxation techniques: Set aside dedicated time for relaxation and mindfulness practices such as yoga, meditation, or deep-breathing exercises. These techniques not only alleviate stress but also bolster emotional resilience. Integrating these activities into your daily routine can offer a much-needed reprieve and help you manage work-related stress more effectively. Start exploring these techniques at Headspace.
5. Set realistic goals and boundaries: In the fast-paced environment of the tech industry, it’s imperative to set achievable goals and establish clear boundaries. Learn to gracefully decline additional responsibilities when feeling overwhelmed, and prioritize taking regular breaks to prevent burnout. Celebrate even the smallest achievements, and remember that it’s okay to ask for help when you need it. For tips on maintaining healthy boundaries at work, visit LinkedIn Learning.

By practicing these stress management strategies, you can take proactive steps towards improving your mental well-being. It’s equally crucial to foster connections with colleagues and seek support, when necessary, to reinforce a workplace culture characterized by openness and care.

As we commemorate the rich heritage of the API community this May, let’s recommit ourselves to prioritizing mental health. By implementing these top tips, our aspiration is to cultivate a healthier, more productive, and more inclusive work environment. Let’s endeavor to make a positive impact, one day at a time.

In the intricate and fast-paced world of mainframe development, onboarding junior developers represents a formidable challenge. The landscape is littered with extensive and diverse codebases, crafted over the years with varying styles and documentation levels. The challenge of getting new developers up to speed is multifaceted. For instance, there’s a growing expectation among junior developers for access to tool sets that mirror those used in other environments. To meet those demands and ease onboarding, organizations must be able to give them a familiar, modern integrated development environment (IDE).

Moreover, the departure of original application authors and the impending retirement of seasoned developers only compound these challenges. Senior developers find themselves investing significant time in guiding their newer counterparts, conducting exhaustive code reviews, and enforcing standards, especially in distributed teams. From a managerial perspective, this process not only consumes valuable resources but also impacts senior developer productivity and team velocity. Furthermore, there’s a looming concern about the potential introduction of regressions and bugs by less-experienced team members.

Thankfully, amidst these challenges, there emerges a transformative solution: generative artificial intelligence (GenAI).

Generative AI presents a groundbreaking and transformative alternative in the mainframe development process, offering contextual code analysis, automated explanations, and seamless integration of comments and documentation into the codebase. Imagine a world where deciphering complex code becomes streamlined, and the need for extensive manual documentation diminishes. This is the promise that GenAI holds.

Revolutionizing mainframe development

Generative AI revolutionizes mainframe development by enabling comprehensive code analysis and automating explanations. Gone are the days of laborious code comprehension; with GenAI, understanding complex code becomes intuitive and efficient. By providing contextual insights and explanations, the technology empowers developers to navigate intricate codebases with ease, accelerating development cycles and reducing the risk of errors. With its ability to adapt to evolving codebases and provide real-time insights, GenAI becomes an invaluable ally in the development process, ensuring that mainframe applications remain robust and scalable in the face of changing requirements. Below are several examples of how generative AI can change the game for mainframe development.

Enhancing team productivity and velocity

Leveraging generative AI for code understanding and documentation enhances team productivity and velocity. By automating repetitive tasks and providing real-time insights, it frees up valuable time for developers to focus on innovation and problem-solving. Teams can iterate faster, deliver higher-quality code, and adapt to evolving requirements with agility. The increased efficiency and effectiveness translate into tangible benefits for development teams, enabling them to deliver value to stakeholders more rapidly and consistently.

Cost and time savings

The streamlined onboarding of junior developers through generative AI translates into significant time and cost savings while also reducing the burden on senior developers and minimizing the risk of errors introduced by inexperienced team members. Additionally, the automated generation of documentation ensures consistency and accuracy, further enhancing productivity and reducing overhead costs associated with manual documentation efforts. By optimizing resource allocation and minimizing waste, GenAI enables organizations to maximize their return on investment and achieve greater efficiency in their mainframe development processes.

Seamless integration of comments and documentation

Generative AI facilitates the seamless integration of comments and code documentation into the development process. Say goodbye to outdated or inconsistent documentation; with GenAI, documentation becomes dynamic and effortlessly aligned with the codebase. Developers can focus on writing code, confident that the accompanying documentation will be generated automatically, ensuring clarity and maintainability. By providing a centralized repository of up-to-date documentation, GenAI enables teams to collaborate more effectively and ensures that knowledge is preserved and accessible to all stakeholders.

Enhancing code quality

One of the most significant advantages of incorporating generative AI into mainframe development is the enhancement of code quality. Traditionally, organizations have implemented informal standards to make the code easier to work with. New developers who are unaware of those standards can apply changes that are noncompliant with those standards, making future changes more difficult. By providing automated explanations and suggestions, generative AI helps ensure that code adheres to best practices and standards, improving the readability and maintainability of the code while also reducing the likelihood of introducing bugs or vulnerabilities. By promoting consistency and adherence to established coding standards, GenAI helps organizations maintain code quality and reliability over time.

Facilitating knowledge transfer

In addition to streamlining the onboarding process for junior developers, generative AI can also facilitate knowledge transfer within development teams. By automatically generating explanations and documentation, it helps capture and codify the collective knowledge of senior developers. This knowledge is then made accessible to all team members, ensuring continuity and reducing reliance on individual expertise.

Promoting collaboration and communication

Alongside that knowledge sharing, generative AI also fosters collaboration and communication among team members by providing a common understanding of the codebase. By generating consistent and accurate documentation, it ensures that all team members have access to the same information, reducing misunderstandings and miscommunications., promoting a collaborative development environment where team members can easily share ideas, provide feedback, and work together towards common goals. By breaking down silos and facilitating open communication, GenAI helps organizations harness the collective intelligence of their development teams and drive innovation and excellence in mainframe development.

Driving innovation

By automating routine, repetitive tasks such as code explanations and documentation and providing valuable insights, generative AI frees up developers’ time and mental bandwidth so they can dedicate more time to exploring new ideas, experimenting with different approaches, iterating quickly, and pushing the boundaries of what is possible. This ultimately fosters innovation and creativity within the organization, helping it stay ahead of the curve and deliver cutting-edge solutions to its customers.

Conclusion: Generative AI represents a paradigm shift in mainframe application development

In today’s fast-paced digital landscape, the ability to adapt to changing requirements is crucial for success. Generative AI equips development teams with the flexibility and agility to respond quickly to evolving business needs. By automating code analysis and documentation, it enables developers to iterate rapidly, experiment with new features, and pivot as necessary. This ensures that mainframe applications remain responsive to changing market demands and business priorities. With GenAI, organizations can embrace change with confidence, knowing that their development processes are agile and adaptable to whatever the future may hold.

In conclusion, generative AI represents a paradigm shift in mainframe development, offering a solution to the longstanding challenges associated with onboarding junior developers and maintaining codebase integrity. By harnessing its power, organizations can streamline their development processes, enhance team productivity, and stay ahead. It’s not just about adopting a new technology; it’s also about unlocking the full potential of mainframe development and driving innovation in the digital era.

In a landscape where the pace of digital transformation is relentless, and the demand for operational excellence is ever-present, ServiceOps emerges not just as a buzzword, but also as a critical enabler for businesses to thrive in the digital age. Building upon the foundational principles outlined in previous blogs, let’s dig into why ServiceOps, a unified approach to IT service management (ITSM) and IT operations (ITOps), matters and why the time to embrace it is now more urgent than ever.

The urgency of ServiceOps

1. Rapidly evolving business landscape: The digital landscape is evolving at an unprecedented pace, with technological advancements, changing customer expectations, and market disruptions reshaping industries overnight. In this dynamic environment, organizations must adapt quickly to stay competitive, making the need for agile and efficient IT management practices more urgent than ever.
2. Increasing complexity and scale: As organizations scale and diversify their operations, the complexity of managing IT services and operations grows exponentially. Traditional siloed approaches to IT management are no longer sufficient to meet the demands of modern enterprises, necessitating a more integrated and holistic approach like ServiceOps.
3. Growing customer expectations: We are now in an era where the customer experience reigns supreme, so organizations must deliver seamless and reliable services to attract, delight, and retain customers and exceed their expectations. Service disruptions and downtime can have far-reaching consequences, leading to lost revenue, damaged reputation, and customer churn. ServiceOps offers a proactive and collaborative approach to managing IT services, ensuring high availability and reliability to meet customer demands.

Why you should care about ServiceOps

1. Driving business outcomes: ServiceOps is not just about IT—it’s about driving tangible business outcomes. By optimizing processes, automating routine tasks, and fostering collaboration, ServiceOps enables organizations to enhance productivity, improve service quality, and accelerate innovation, ultimately driving sustainable growth and competitive advantage.
2. Enhancing customer satisfaction: In today’s customer-centric world, delivering exceptional customer experiences is paramount. ServiceOps enables organizations to proactively monitor and manage IT services, minimizing downtime and service disruptions to ensure high levels of customer satisfaction and loyalty.
3. Staying ahead of the curve: As digital disruption reshapes industries, organizations that adopt ServiceOps gain a strategic advantage over their competitors by taking a proactive and integrated approach to IT management, quickly adapting to changing market dynamics, and capitalizing on emerging opportunities.

The time is now

1. Seizing the competitive advantage: By implementing ServiceOps early and optimizing IT operations, organizations can gain a competitive advantage in their respective markets, enhancing collaboration and driving innovation to differentiate themselves from competitors and position themselves for long-term success.
2. Minimizing risks and downtime: Service disruptions and downtime can have significant financial and reputational implications for organizations. By adopting ServiceOps practices, organizations can minimize the risk of service disruptions, proactively addressing issues before they escalate and ensuring uninterrupted service delivery.
3. Unlocking growth opportunities: ServiceOps enables organizations to optimize their existing operations and foster a culture of continuous improvement and collaboration to drive innovation, develop new products and services, and capitalize on emerging market trends.

Adopting ServiceOps is not just about staying ahead of the curve—it’s about future-proofing your organization for success in an increasingly digital world through operational excellence, enhanced customer satisfaction, and new opportunities for growth and innovation. The time to act is now. Embrace ServiceOps and embark on a journey towards transformative outcomes and sustainable success in the digital age.

Read more about the adoption of ServiceOps and how you can accelerate and maximize the impact of a converged ITSM/ITOps transformation in the latest e-book from Enterprise Strategy Group.

Today’s complex and dynamic architecture makes it increasingly difficult to trace an error back to the root cause of the problem. This results in slow incident response time, which can lead to service degradation and expensive downtime. We’re excited to announce new capabilities in the BMC Helix observability and AIOps solution portfolio that help teams improve service reliability and mean time to repair (MTTR) while streamlining incident management.

The BMC Helix ITOM 24.2 release introduces OpenTelemetry support for distributed tracing, BMC HelixGPT-powered log insights in the context of a situation, new service blueprints, and enhanced integration for correlation of incidents to situations. Other enhancements include expanded BMC Helix Discovery Technology Knowledge Updates (TKUs) and improved usability through advanced event filtering.

These key enhancements make cloud-native application data collection easier for IT operations and site reliability engineering (SRE) teams and reduce the time teams must spend manually troubleshooting problems or poring over complicated information.

Troubleshoot more efficiently with OpenTelemetry tracing

OpenTelemetry (OTel) is the open-source standard for instrumenting, generating, collecting, and exporting telemetry data to gain observability over microservices. Through the OTel collector, IT teams that include DevOps, IT operations (ITOps), SRE, and platform engineering, can get traces into BMC Helix ITOM, our observability and AIOps solution, without needing to use a third-party monitoring tool.

Our solution derives the rate, errors, and duration (RED) metrics from all the spans ingested to provide a highly accurate picture of an application’s behavior and give users a standardized starting point for troubleshooting microservices or any request-driven applications. By viewing a trace, you can track the complete flow of a request from one service to another and identify which part of the application is causing issues such as errors or latency concerns.

Figure 1. Visualizing a trace.

BMC Helix ITOM enriches OpenTelemetry trace data with a powerful topology mapping and visualization that gives IT teams a better understanding of the overall service and its dependencies and isolates root causes by correlating traces with topology, metrics, and events in the context of the problem. If an application issue occurs, you can identify the impacted application quickly. From that impacted node, you can launch the dashboard that shows the problem’s contextual RED metrics. Clicking on any error status will direct you to the relevant traces to pinpoint the issue.

In addition, the topology derived from those traces get reconciled and added to the solution’s network infrastructure topology mapping and visualization, giving IT teams end-to-end service visibility. This helps identify issues that stem from errors within the microservices or changes to the infrastructure or network.

Resolve incidents faster with AI-powered log insights

BMC Helix ITOM continues to invest in artificial intelligence and machine learning (AI/ML) capabilities to improve MTTR and team productivity. In January, we introduced the Best Action Recommendation (BAR) feature to help IT practitioners resolve issues and eliminate days of troubleshooting. Trained on past incidents, situations, and remediation actions, BAR uses generative AI to recommend potential solutions to resolve problems. IT administrators, network operations center (NOC) operators and SREs can use the “Ask BMC HelixGPT” tool to ask questions and get more details to questions such as, “What’s the impact of the issue?” and “Has this situation happened in the past?”

Figure 2. Log insights leveraging BMC HelixGPT.

Now, with log insights for situations, teams gain additional insights with important trends and patterns found in the context of the problem. Using generative AI for log data further enhances the functionality of BAR by reducing the noise and the time it takes to identify unusual behaviors in log data. Log insights provides a summary of the issue and a breakdown of the different occurrences so teams can make informed decisions with a clear explanation of the problem.

Automate incident management through a single interface

When you’re under pressure to respond to production incidents, nothing kills productivity more than context switching. The BMC Helix observability and AIOps solution portfolio eliminates context switching by enabling cross-launching between the situation view (where a generative AI-based summary and the root cause of the issue is shown) to your IT service management (ITSM) tool. By creating a single incident for many correlated events along with the impacted service and configuration item (CI) details, the solution significantly reduces the number of tickets created, as well as their corresponding costs and incident noise. ITSM and ITOps teams can now focus and collaborate more efficiently on solving critical issues.

Get better control of events

The new advanced event filter capability helps users view only the events they want to focus on to prioritize critical issues and diagnose them early by:

• Creating new filters and viewing events that match the specified priorities.
• Saving hundreds of filters and switching between them within the intuitive user interface (UI).
• Grouping devices and services based on technology, role, and geography.

Figure 3. Advanced event filter in BMC Helix Operations Management.

Other enhancements

New service blueprints for service modeling 

Speed the time to value (TTV) for customers creating service models with new out-of-the-box service blueprints for application performance monitoring, networks, cloud infrastructure, and mainframe that reduce contextual noise and enable root cause analysis.

New BMC Helix Discovery TKUs

Get expanded BMC Helix Discovery TKU content with new cloud, Kubernetes, storage, and network coverage that improves visibility into your IT environment. For the full list, see our documentation.

Want to see it working together? We’ll be happy to show you the latest features; request a demo today.

As we advance through 2024, IT service management (ITSM) remains a pivotal cornerstone for organizations aiming to streamline their services and manage IT operations more effectively. With approximately 48 percent of organizations rating their ITSM capabilities as “great” or “good,” there’s a burgeoning confidence in current ITSM strategies. However, this sentiment is balanced by an equal number acknowledging the necessity for significant enhancements.

This post delves into the nuances of ITSM adoption, the implications of the remote work era, the transformative role of artificial intelligence (AI) and automation, the intensifying investment in cloud infrastructure, and the evolving focus on the employee experience.

1. ITSM adoption and perception:

• Large organizations typically have more complex infrastructures and have been leading the adoption of ITSM, necessitating robust solutions for their extensive requirements. (Imarc Group, 2024)
• Small- and medium-sized enterprises are adopting ITSM solutions at the highest growth rate, seeking cost savings and flexibility to compete with larger businesses. (Research and Markets, 2024)
• A significant growth in the ITSM market is anticipated, with a forecasted size of $22.1 billion by 2028, growing at a CAGR of 15.9%. (MarketsandMarkets, 2024)
• North America is expected to hold a major share of the cloud ITSM market, with an increasing number of businesses adopting hybrid cloud and on-premises solutions. (Mordor Intelligence, 2024)
• The integration of AI in ITSM solutions is not just a trend, it is also starting to make a real impact, with predictions that 70% of organizations will implement structured automation by 2025, significantly up from 20% in 2021. (TOPdesk, 2024)
• Challenges in ITSM adoption often include a lack of senior management buy-in, inadequate tools, and the need for strategic alignment at higher levels. (AXELOS, 2022)

Check out our cloud-native ITSM software ›

2. Impact of remote work on ITSM:

• There’s a notable shift towards AI-driven automation, with reports suggesting that companies implementing AI-powered knowledge base tools have improved first-contact resolution by five to seven percentage points and reduced handling time by 20 to 30 %. (SDI, 2024)
• Industries with unique remote work challenges, like the entertainment industry, are expected to increasingly embrace remote work as technological constraints are overcome. (Cybersecurity Intelligence, 2023)
• With the proliferation of devices—29 billion connected to the internet—IT helpdesks face escalating challenges, causing 80 percent of IT leaders to work longer hours. (Cybersecurity Intelligence, 2023)
• The management complexity for IT teams has risen, with 35 percent finding the variety of devices and locations challenging. (Cybersecurity Intelligence, 2023)
• The overloading of VPNs is causing performance issues, leading to a pivot away from traditional VPNs to more modern solutions. (Cybersecurity Intelligence, 2023)
• The transition to remote work has led to a surge in ITSM ticket volumes, exceeding the historical annual growth rate and placing added pressure on IT staff. (InvGate, 2022)

3. AI and automation in ITSM:

• Organizations using generative AI for ticket resolution have seen a remarkable 75% reduction in resolution times, leading to increased productivity and happier employees. (SDI, 2024)
• Around 60% of companies are now using AI-based ITSM tools to enhance their service desk functions. (Rezolve, 2023)
• AI-driven automation in ITSM can potentially reduce incident resolution times by up to 50%. (Rezolve, 2023)
• About 75% of ITSM professionals believe that AI will significantly impact ITSM by 2025. (Rezolve, 2023)

Find out how AI is influencing service management ›

4. Cloud infrastructure spend:

• Cloud-native platforms are on the rise, with predictions that by 2025, 95% of new digital workloads will be on such platforms, a significant increase from 30% in 2021. (SDI, 2024)
• Investment in cloud infrastructure services has increased remarkably, with a 29% growth from the previous year, indicating accelerated cloud adoption. (Rezolve, 2023)
• The leading players in the cloud market are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, which collectively hold about 66% of the global market share. (Rezolve, 2023)
• Multi-cloud strategies are becoming more prevalent, with 42% of companies using four or more public cloud services for their workloads. (Tech Target, 2023)

5. Employee experience:

• Organizations using AI and automation report that employees believe they can increase their workload by 30% due to the efficiency gains provided by these technologies. (SDI, 2024)
• Enhancing employee experience is a growing focus in ITSM, with 67% of organizations acknowledging the importance of better employee experiences. (Halo ITSM, 2023)
• There’s an increasing trend to improve employee engagement and retention through enhanced ITSM-driven experiences. (Rezolve, 2023)
• The adoption of AI and intelligent automation in ITSM is set to play a significant role in improving employee experiences by reducing incident resolution times and increasing the availability of IT support. (AXELOS, 2022)

The ITSM landscape continues to evolve. Organizations are adapting to the demands of remote work, exploring the advantages of AI and automation, investing heavily in cloud services, and placing a renewed emphasis on the employee experience. By embracing these trends, companies can stay resilient and agile in the face of ongoing digital transformation.

Explore enterprise ITSM solutions ›

Do you trust what you’re eating? I can rest easy; in the U.S., I have the Food and Drug Administration regulating my food supply. Do you trust the product you just purchased? If I I’m in the EU, I have confidence because of the CE mark. Do you trust your bank/supermarket/airline is secure? I shouldn’t need to because they follow established security frameworks. This is a long-winded way to say that throughout our lives, we trust regulations and standards to ensure our safety, and cybersecurity is no different. In this blog we are going to look at what security frameworks are, why we need them, how to choose a framework, and, finally, balance things out by looking at the potential downsides of security frameworks.

Why do companies need cybersecurity frameworks?

Short answer: To improve the organization’s security posture in the following ways.

• Standardization: A common language and set of measures ensures that requirements are well-understood and consistent. Frameworks provide best practices and guidelines that are platform-agnostic to implement cybersecurity measures.
• Risk management: A framework provides a structured approach to risk management. It will usually begin with a discovery phase followed by an assessment of the finding. This in turn cascades down into resource (be that time or money) allocation to ensure the most significant threats are mitigated.
• Compliance requirements: While many industries have specific regulatory requirements for cybersecurity, non-industry-specific frameworks will align with standards of the regulatory bodies to ensure compliance.
• Resources/Skills gaps: Implementing effective cybersecurity controls is no small task. When you add skills shortages (or a complete lack of skills) to the mix, you may wonder where to start. Frameworks offer guidance based on risk, helping to ensure your implementation plan is as effective as possible.
• Continuous improvements: The threat landscape is always changing—the next exploit is currently in development somewhere. Frameworks provide the structure to ensure you stay secure and are continuously updated to keep pace with the latest threats for maximum effectiveness.
• Interoperability: Similar to standardization but slightly different, cybersecurity measures span the wider digital ecosystem to ensure that collaboration and communication can be facilitated between internal stakeholders and with vendors and auditors.
• Awareness/Visibility: The frameworks themselves raise awareness about cybersecurity and promote best practices.

How do you choose a framework?

Depending on your business sector or organizational status, which framework you follow may already be mandated. For example, a company that is publicly traded will need to comply with the Sarbanes-Oxley Act (SOX), so it may use the Control Objectives for Information and Related Technologies (COBIT) framework to achieve this. For US government agencies, the National Institute of Standards and Technology (NIST) regulations must be followed. What if you are not mandated to follow a certain framework? Then you focus on a framework that can help you address one of more of the following:

• Risk:
  • Encompasses risk identification, analysis, evaluation, treatment, and monitoring.
  • Facilitates ongoing monitoring and reporting of compliance efforts.
  • Helps the organization prioritize effort based on risk.
• Control:
  • Provides a high-level strategy for the cybersecurity team.
  • Platform-agnostic set of security controls.
  • Easy-to-digest current state of the organization.
• Program:
  •  Covers the whole organization.
  • Assesses the organization’s current state in a single place.
  • Measurable.
  • Brings the whole organization into a common language from technical team to executives.

Are there downsides to using a framework?

If a piece of work is improving the security posture, ultimately there is no downside to it, however that doesn’t mean it can’t be critiqued. There is a common misconception that if you have a followed a security framework, then you are completely secure. While you are better protected than you were before, your security posture must still be validated with assessments and penetration tests (pentests).

Organizations are complex places, and a one-size-fits-all approach can lead to both over-investment and under-investment in certain areas if they have not been properly risk assessed. While it is expensive to implement a framework, it’s also costly to maintain it going forward, which can leave an organization vulnerable if they do not invest in both implementation and continuous maintenance.

Examples of popular frameworks

• NIST CSF (Cyber Security Framework)
• CIS Controls
• ISO/IEC 27001
• Payment Card Industry Data Security Standards (PCI DSS)
• General Data Protection Regulation (GDPR)

Summary

Security frameworks provide organizations with structured guidelines and methodologies that align with industry standards, best practices, and regulatory requirements. Choosing which framework to implement is no small task and requires a significant financial investment and time commitment. Ultimately, adhering to a framework will improve the security posture of the organization, but do not become a victim of a false sense of security. Following the framework alone does not make you secure—you must also conduct security assessments and penetration testing to ensure agility in the face of continuously evolving threats.

In today’s continuously evolving IT environment, staying ahead sometimes means embracing both the tried and true and the cutting cutting-edge For organizations deeply invested in TrueSight Operations Management, the prospect of integrating advanced artificial intelligence (AI) capabilities might seem like a leap into the unknown. However, the future of operational excellence lies in blending existing strengths with the latest innovations. This is where BMC Helix AIOps and observability come into play, offering a bridge to the future. BMC Helix AIOPs is called as BMC Helix Service Monitoring Tile in the Helix Portal Landing Page.

I have had several inquiries regarding the feasibility of using existing TrueSight Operations Management and connecting it to the award-winning functionality of BMC Helix AIOps and observability. Customers are asking if they can adopt the latest BMC Helix AIOps and observability tools without replacing TrueSight Operations Management with BMC Helix Operations Management.

This blog previews the best practice steps  and use cases for customers to connect TrueSight Operations Management to BMC Helix AIOps using BMC Helix Intelligent Integrations, and guidance to helpcustomers using TrueSight Operations Management who want to adopt both BMC Helix Operations Management and BMC Helix AIOps and observability to improve operational efficiency, reduce costs, and yield the benefits of a modern, highly available, cloud-native platform.

Working with available use cases

Use case 1— TrueSight Operations Management and BMC Helix Operations Management event data workflow using BMC Helix Intelligent Integrations

• Download, install, and configure the TrueSight Operations Management connector.
• Once the connection and integration are successful, send events to it.
• Through the connector, the events are propagated to BMC Helix Operations Management.

• As a next step, when we do any event operation like a closing event in TrueSight Operations Management, we will see the corresponding event closing in the BMC Helix Operations Management
• Please note there is no back propagation, as of now, of event status from BMC Helix Operations Management to TrueSight Operations Management.

Figure 1. Use case 1 flow (TSPS is TrueSight Presentation Server, a component of TrueSight Operations Management).

Figure 2. Use case 1 flow with pros and cons.

Use case 2—TrueSight Operations Management and BMC Helix Operations Management event data topology using BMC Helix Intelligent Integrations.

• Download, install, and configure the TrueSight Operation Management connector.
• Configuration Items (CIs) created manually in TrueSight Operation Management will get ingested into BMC Helix Operations Management.
• In BMC Helix Operations Management, we will observe the CIs getting ingested. In BMC Helix Discovery, we will see these CIs as generic elements.
• In BMC Helix Service Monitoring, we will not see these CIs as these are created manually in TrueSight Operations Management.
• Please note there is no back propagation, as of now, of event status from BMC Helix Operations Management to TrueSight Operations Management.

Figure 3: Use case 2 flow.

Use case 3—TrueSight Operations Management with a configuration management database (CMDB) and service models integrated into BMC Helix Operations Management topology using BMC Helix Intelligent Integrations

• Download, install, and configure the TrueSight Operation Management connector.
• This is like use case 2, the only difference here is the service models are created in the CMDB and published from the CMDB to TrueSight Operations Management.
• BMC Helix Discovery will show the same topology.
• In BMC Helix Service Monitoring (BMC Helix AIOps), we will see the business service models.

Figure 4. Use case 3 flow.

Use case 4—TrueSight Operations Management monitoring vCenter

• Add monitoring policy for vCenter KM in TrueSight Operations Management.
• vCenter hierarchy builds up in TrueSight Operations Management.
• We can see the vCenter CI in BMC Helix Discovery.
• We observe that the hierarchy does not have multilevel topology as in TrueSight Operations Management.

There is no use case diagram for this.

This completes the best practice steps you can use when trying to integrate TrueSight Operations Management with BMC Helix Service Monitoring (AIOps) using BMC Helix Intelligent Integrations.

For each of the four use cases, we have recorded a five-minute video demonstration, which can be obtained by request by emailing sayan_banerjee@bmc.com

Conclusion

These use cases help IT teams that want to stay with TrueSight Operations Management while adopting BMC Helix Operations Management with AIOps and using BMC Helix Service Monitoring.

Enhancing the BMC Helix Observability and AIOps solution with application and network management capabilities

It is my pleasure to welcome Netreo team to BMC. Today, BMC officially announced our intent to acquire Netreo. Netreo is a software company that helps organizations deliver digital experiences through real-time visibility into performance and availability of applications deployed in the cloud, on-premises, and within hybrid networks and infrastructure. The deal is expected to close in the first half of 2024, subject to customary closing conditions.

Full-stack open Observability and AIOps on the BMC Helix platform

The acquisition marks a strategic leap forward for BMC as Netreo products will accelerate our OpenTelemetry-based observability capabilities and enhance the full-stack, open BMC Helix Observability and AIOps solutions with Network Performance Monitoring and Diagnostics and application performance management capabilities. The combined power of the BMC Helix platform and Netreo solutions provides IT Operations, DevOps practitioners, developers, and network teams with comprehensive insights spanning applications, infrastructure, and networks, helping them increase the overall reliability and performance.

Netreo’s strong presence within developer and DevOps teams, coupled with standards-based OpenTelemetry tracing (including logs and metrics), positions BMC to effectively lead the observability segment, facilitating deeper visibility into modern microservices-based applications. In addition, Netreo’s approach to network performance and diagnostics is noteworthy, extending beyond conventional methods such as SNMP, NetFlow, and sFlow to incorporate API-based insights from network elements alongside SD-WAN monitoring. As a result, networking teams can deliver new network services, increase customer satisfaction, and maximize overall network performance.

Expanded Observability and AIOps add even more value to BMC ServiceOps, which includes industry-leading service management capabilities. It enables teams to predict incidents and disruptions from changes and continuous releases, helping customers reduce risk while accelerating innovation.

We are excited to offer our customers full-stack, open observability and AIOps to increase reliability and performance of their entire IT environments.  Building on top of our ServiceOps, innovative AI and ML technologies such as the BMC HelixGPT solution, BMC is uniquely positioned to help enterprises transform the way they deliver critical services, helping them prevent incidents, and accelerating their journey to becoming an Autonomous Digital Enterprise.

Jasmin Young, CEO of Netreo, is looking forward to joining forces with me at BMC. We share excitement about seeing Netreo’s observability vision come to fruition within the BMC family as both organizations have bold plans for observability, AIOps and applying machine learning across the portfolio.

With this acquisition, at BMC, we continue to accelerate our focus on investing in strategic innovation and technologies that drive IT and digital transformation, furthering the adoption of machine learning and observability across the enterprise.

Netreo offers both on-premises and SaaS solutions. To learn more about Netreo, follow www.netreo.com, and to learn about the BMC Helix Observability and AIOps solution, follow www.bmc.com/it-solutions/bmc-helix.html.

In the dynamic landscape of software development, where mainframe systems continue to play a critical role, the adoption of artificial intelligence (AI) is revolutionizing traditional practices. Specifically, the emergence of generative AI presents a paradigm shift in how developers interact with mainframe applications within the DevOps ecosystem. This article delves into the fundamental disparities between using generative AI and conventional AI methodologies in mainframe DevOps and explores how the former augments the developer experience.

The overarching goal of employing generative AI in mainframe DevOps is to enhance the application developer journey. Generative AI operates on the premise of explaining, guiding, and testing mainframe application changes and enhancements. Unlike traditional AI and machine learning (ML) techniques, which predominantly focus on optimizing system performance or detecting anomalies, generative AI takes a proactive stance in empowering developers throughout the software development lifecycle.

Understanding the scope of AI capabilities

Before delving into the nuances of generative AI, it’s important to delineate the capabilities of AI in the mainframe DevOps domain. AI encompasses a spectrum of techniques, ranging from anomaly detection and predictive maintenance to optimization and automation. These methodologies excel in augmenting system monitoring, maintenance, and performance. However, traditional AI falls short in addressing the intricate nuances of the developer journey, such as code comprehension, adherence to coding standards, and efficient testing strategies.

Why generative AI is the optimal solution

Generative AI emerges as the quintessential solution for augmenting the application developer journey in mainframe DevOps due to its innate capacity to comprehend, guide, and enhance code-related tasks. Unlike traditional AI methodologies, which primarily focus on system optimization and anomaly detection, generative AI transcends these boundaries by actively participating in the software development lifecycle. For instance, in code explanation tasks, generative AI not only provides comprehensive insights into code snippets but also automatically integrates explanatory comments, fostering better understanding and collaboration among developers.

Similarly, in code-review scenarios, generative AI offers real-time guidance on adhering to enterprise coding standards and industry best practices, thereby ensuring consistency and compliance across applications. These examples underscore the multifaceted capabilities of generative AI in enhancing developer productivity and facilitating seamless collaboration within the DevOps ecosystem.

Let’s explore five key use cases that demonstrate the efficacy of generative AI in this realm:

1. Explanation: Understand what the code does

Generative AI offers in-depth explanations of code snippets and programs. Leveraging sophisticated natural language processing (NLP) methods, it seamlessly integrates comments into code, improving readability and fostering collaboration among developers. This automated process enhances understanding and streamlines the development workflow, ensuring that developers can grasp the intricacies of the codebase more efficiently, which helps accelerate the development cycle.

2. Review: Receive immediate feedback on code standards

Generative AI plays a pivotal role in code reviews, assisting developers with real-time guidance and remediation suggestions that ensure adherence to predefined standards to promote consistency and compliance across applications. Developers benefit by receiving immediate feedback on their code, which helps them identify and rectify potential issues early in the development process. Consequently, it leads to higher-quality codebases, improved software reliability, and increased efficiency in development workflows.

3. Improve the delivery of your changes

In an era characterized by increasingly complex toolchains, generative AI can significantly streamline the code delivery process. By facilitating quicker root cause isolation and enhancing the resiliency and performance of continuous integration and continuous delivery (CI/CD) toolchains, it expedites mean time to resolution (MTTR). Reduced downtime and faster issue resolution lead to increased productivity and smoother workflows for developers, while businesses experience improved software delivery efficiency, enhanced competitiveness, and greater customer satisfaction.

4. Give every developer a personal assistant

Generative AI functions as an indispensable virtual coding assistant, equipped with extensive knowledge of best practices, design patterns, and syntax. By seamlessly integrating into developer workflows, it provides contextual recommendations and promotes adherence to industry and organizational standards, enhancing developer productivity and code quality. Developers gain real-time guidance, enabling them to make informed decisions and produce high-quality code efficiently . As a result, businesses get accelerated development cycles, reduced error rates, and enhanced software reliability, ultimately leading to improved customer satisfaction and competitive advantage in the market .

5. Efficient testing with minimal viable data

Generative AI revolutionizes the testing paradigm by intelligently analyzing source code and devising optimal testing strategies. By leveraging sophisticated algorithms, it generates test case scripts and tailors datasets to the specific requirements of the application, ensuring thorough coverage while maintaining efficiency. Automation of the testing process saves developers time and effort previously spent creating comprehensive test suites so they can focus more on developing new features and addressing critical issues. For businesses, the adoption of generative AI for test case generation results in improved software quality and reliability . With thorough testing coverage, organizations can mitigate the risk of bugs and performance issues, leading to higher customer satisfaction and reduced maintenance costs in the long run. Additionally, it facilitates faster release cycles, enabling businesses to stay competitive in the market.

Considerations for adoption

When considering the adoption of generative AI in mainframe DevOps, organizations should carefully evaluate several key factors to ensure successful implementation:

• Scalability and compatibility with existing mainframe infrastructure and development processes
• Potential benefits, such as improved code quality, faster development cycles, and enhanced developer collaboration, etc.
• Cost-effectiveness and return on investment, encompassing initial investments in training and infrastructure and the long-term benefits of increased efficiency and productivity
• Alignment with strategic objectives and overall digital transformation initiatives

By carefully weighing these considerations, organizations can make informed decisions about whether and how to incorporate generative AI into their mainframe DevOps workflows.

Conclusion: Generative AI fosters a culture of continuous improvement

In essence, the integration and adoption of generative AI into mainframe DevOps signifies a transformative leap in the developer journey. With enhanced code comprehension and improved testing and adherence to standards, developers are empowered to innovate with unparalleled confidence and agility. This not only accelerates development cycles but also fosters a culture of continuous improvement within organizations.

Embracing generative AI paves the way for enhanced efficiency, reliability, and scalability in mainframe application development. Businesses can expect streamlined processes, reduced time to market, and better software quality. Moreover, the adoption of generative AI sets the stage for future advancements in technology and reinforces the organization’s position at the forefront of innovation in the competitive landscape. Overall, it signifies a strategic investment in harnessing the full potential of AI to drive sustainable growth and success in the digital era.

The impact of data and artificial intelligence (AI) on technology is expanding and deepening rapidly, lending incredible benefits and potential consequences to business. The past year’s AI frenzy had every industry chomping at the bit with the potential of learning language models (LLMs) and natural language processing (NLP) to drive business and customer outcomes faster, more efficiently, and more comprehensively. Let’s face it—AI is now mainstream. In fact, a recent survey showed that nearly two-thirds (64 percent) of business owners believe AI will improve customer relationships and increase productivity. With a majority of large enterprises now investing in and embracing generative AI for uses ranging from customer service to cybersecurity, these capabilities will soon cease to be differentiators but mere table stakes.

So how does business stay ahead of the curve? AI on its own doesn’t deliver value—in fact, it adds complexity and involves substantial risk. Sure, it connects users to data, but only via useful applications supported in secure, cloud-based infrastructure. It’s the strategic use of data afforded by carefully managed AI that leads to innovation—and that’s where the magic happens.

The word innovation itself has become so commonplace that we hardly hear its meaning anymore. Delivering fresh methodologies and ideas to market faster than anyone else is now core to business success—and operationalization is the secret sauce: making innovation easier.

Innovation won’t wait

AI is expanding so rapidly; it feels like when the internet was born. Easier isn’t the only goal. How do you also operationalize innovation fast? By connecting and optimizing all your operations across your landscape to create a seamless path for people, process, and technology to navigate, leading to the next Big Idea. That’s the promise of the Autonomous Digital Enterprise, BMC’s vision for the future of business—agile, resilient, digitally transformed, and ready to continuously innovate and win. And the way we get there is via Connected Digital Ops.

With Connected Digital Ops, you can connect across your entire technology landscape, from on-premises to cloud to edge, to automate and transform your IT infrastructure. You can turn systems of record into systems of action, enabling automated workflows that give you the real-time insights you need to take action. And you can make operations (the Ops) the catalyst for competitive advantage as the innovation differentiator that’s always connected to customer demands and essential to driving business outcomes.

The power of Connected Digital Ops is in its ability to deliver transformation and growth with the breakthrough capabilities of AI and five distinct Ops.

It’s AIOps, combining data, analytics, and machine learning (ML) to enable enterprise observability, automated issue remediation, and proactive problem-solving.

It’s DataOps, uncovering new opportunities and unlocking greater business value, rapidly, continuously, and at scale.

And ServiceOps, removing the barrier between service (ITSM) and operations (ITOM) to enable collaboration between teams for amazing user experiences.

Along with DevOps, where organizations can develop and deliver in-demand apps continuously and efficiently.

And finally, AutonomousOps, where integrating, automating, and orchestrating complex application and data workflows ensures business outcomes at enterprise speed and scale.

Deliver on the promise of AI with BMC

Today’s IT landscape comprises multiple platforms spanning mainframe, public, private, and hybrid cloud, and on-premises datacenters, all the way to the edge. BMC has nimbly mastered every paradigm shift for more than 40 years, and we are uniquely positioned to help connect your business-critical infrastructure across this vast and complex hybrid IT ecosystem with the power of AI.

Elevate Ops to be the most mission critical part of your business, a foundation for continuous innovation. In the AI-dominated future, business must move faster than humanly possible—and BMC can help you get there.

Learn more about Connected Digital Ops.