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Change Management Part 3: Optimizing Change Management for Cloud, Agile, and Mobile - How the Technologies Are Evolving

Dennis Drogseth

This is is Part 3 of a three-part series on change management. In Part 1, I examined both the processes and the use cases associated with managing change. In In Part 2, I looked at metrics, best practices, and pitfalls. In this third blog, I’ll look at how the technologies for service modeling, automation, visualization, and self-service are evolving to address the more dynamic demands of trends such as cloud, agile, and mobile.

Start with Change Management Part 1

Start with Change Management Part 2

Service Modeling

The place to start with any discussion of service modeling is still the configuration management database (CMDB) or configuration management system (CMS). However, service modeling is also important in service catalog capabilities for self-service, as well as application discovery and dependency mapping (ADDM) solutions.

In EMA research and consulting, we have seen increasingly close ties between service modeling and automation, as well as between service modeling and analytics. EMA has also documented a growing number of agile and DevOps initiatives leveraging CMS backbones for more timely provisioning and governance. Examples can be found in our book, CMDB Systems: Making Change Work in the Age of Cloud and Agile.

Image removed.All this may seem to go against the common wisdom, especially since the notion of service modeling, and the idea of deploying CMDBs in particular, is not infrequently thought of as “old hat.” But the truth is the CMDB/CMS/ADDM landscape is changing dramatically to support higher levels of dynamic currency, more advanced levels of automation, and more effective use of advanced analytics. EMA’s recent report What is the Future of IT Service Management? those respondents with CMDBs were twice as likely to be successful in their ITSM initiatives as those without, and those with ADDM capabilities were eight times more empowered. Similar trends emerged when EMA did research targeting effective service delivery over cloud in Q2 2013 and when we evaluated advanced operations analytics in Q4 2014.

One reason that CMBDs and ADDM tools can make such a difference is that while cloud and virtualization make capturing service interdependencies more difficult, they actually require more not less attention to optimizing service-to-infrastructure relationships across a broader and more eclectic mosaic of choices. Optimizing cloud, in all of its iterations (public/ private, SaaS, IaaS) is ultimately about visibility and control, not a cloudy faith in blind speed.

Automation

Not surprisingly, cloud also raises the bar for automation. In that same recent research on ITSM futures, we saw that “cloud ups the ante for increasing investments in automation in provisioning services” as a top choice (just after viewing cloud as a resource for growth). This choice was tied, not surprisingly, with “cloud is forcing us to pay more attention to DevOps.”

In our ITSM futures research, the top priorities for change-related automation were the following:

1. IT process automation (ITPA) – Also known as “runbook,” ITPA provides policy-based capabilities for integrating and unifying automation across everything from patch management to onboarding to service provisioning using a common set of automated workflows.

2. Systems configuration – This included automation in virtualized as well as non-virtualized environments.

3. Workflow between the service desk and operations – This priority reflects the pervasive need for better integration between service desk/operations that was seen in EMA’s ITSM futures research.

4. Storage and network configuration – Natural extensions of both cloud and traditional infrastructure, storage configuration and network configuration were each noted as automation priorities by 36% of respondents.

5. Automation for lifecycle asset management – This type of automation is made visibly more difficult in hybrid cloud environments. Although ranked fifth, this area was still a high priority overall and was selected by nearly one-third of respondents.

Visualization and Reporting

This may not sound new, but good visualization and reporting are more critical than ever for managing change. One way to address visualization is to ask which roles and use cases are supported. For more general change management, some of the roles to consider are domain specific (e.g., network, systems, desktop, mobile, security, service desk, applications, development). Cross-domain roles are also important, including engineering, architecture, service delivery, change management, asset management, financial planning, and user-experience management. Finally, to be truly aligned with business priorities, it’s often critical to include non-IT stakeholders, such as financial planning, non-IT executives, marketing, online operations, and partner management.

Service Catalog

Ideally, a service catalog, or even a service portal, should be a natural extension of the CMDB system, with consistent modeling and insight into relevant interdependencies. This is just as true for those catalogs that include cloud-based services as it is for those including in-house services as it’s becoming increasingly important to understand dependencies outside IT’s physical walls. Incorporating public-cloud, private-cloud, and non-cloud services in an integrated service catalog is more and more in demand, as is support for mobile access to corporate and other services.

Mobile and Self-Service

Managing mobile devices and support for self service, while separate, are linked in how they both support escalating consumer expectations surrounding the choice, availability and performance of IT services. Our ITSM futures research showed that mobile adoption has already become heterogeneous in most environments — including iPhones, Android, tablets and other devices. Moreover, 65% of our respondents offered mobile end consumers some level of access for corporate applications.

The top priority for self-service, the most consumer-driven form of change management, was, not surprisingly, automation for end-user access to services. Near the top were also self-service via a service catalog, and mobile access. Other prioritized self-service objectives, somewhat less directly related to change management, were self-service enabled knowledge management and automation for resolving end-user issues.

Changing Technologies in a Changing World

These are not the only technologies relevant to managing change. For instance, lifecycle asset management, usage analysis, and onboarding are all part of the picture. Moreover, analytics relevant to change management is increasingly part of the story as well — a topic I’ll address in October. But if there’s a net takeaway, it’s that change management technologies are themselves changing to address a changing world in the so-called “Digital Age.” This brave new world is neither “stable nor precise” according to the traditional IT canon. But it’s one of greater opportunity, greater dynamism, and greater dependency on IT services and their digital content than ever before.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Change Management Part 3: Optimizing Change Management for Cloud, Agile, and Mobile - How the Technologies Are Evolving

Dennis Drogseth

This is is Part 3 of a three-part series on change management. In Part 1, I examined both the processes and the use cases associated with managing change. In In Part 2, I looked at metrics, best practices, and pitfalls. In this third blog, I’ll look at how the technologies for service modeling, automation, visualization, and self-service are evolving to address the more dynamic demands of trends such as cloud, agile, and mobile.

Start with Change Management Part 1

Start with Change Management Part 2

Service Modeling

The place to start with any discussion of service modeling is still the configuration management database (CMDB) or configuration management system (CMS). However, service modeling is also important in service catalog capabilities for self-service, as well as application discovery and dependency mapping (ADDM) solutions.

In EMA research and consulting, we have seen increasingly close ties between service modeling and automation, as well as between service modeling and analytics. EMA has also documented a growing number of agile and DevOps initiatives leveraging CMS backbones for more timely provisioning and governance. Examples can be found in our book, CMDB Systems: Making Change Work in the Age of Cloud and Agile.

Image removed.All this may seem to go against the common wisdom, especially since the notion of service modeling, and the idea of deploying CMDBs in particular, is not infrequently thought of as “old hat.” But the truth is the CMDB/CMS/ADDM landscape is changing dramatically to support higher levels of dynamic currency, more advanced levels of automation, and more effective use of advanced analytics. EMA’s recent report What is the Future of IT Service Management? those respondents with CMDBs were twice as likely to be successful in their ITSM initiatives as those without, and those with ADDM capabilities were eight times more empowered. Similar trends emerged when EMA did research targeting effective service delivery over cloud in Q2 2013 and when we evaluated advanced operations analytics in Q4 2014.

One reason that CMBDs and ADDM tools can make such a difference is that while cloud and virtualization make capturing service interdependencies more difficult, they actually require more not less attention to optimizing service-to-infrastructure relationships across a broader and more eclectic mosaic of choices. Optimizing cloud, in all of its iterations (public/ private, SaaS, IaaS) is ultimately about visibility and control, not a cloudy faith in blind speed.

Automation

Not surprisingly, cloud also raises the bar for automation. In that same recent research on ITSM futures, we saw that “cloud ups the ante for increasing investments in automation in provisioning services” as a top choice (just after viewing cloud as a resource for growth). This choice was tied, not surprisingly, with “cloud is forcing us to pay more attention to DevOps.”

In our ITSM futures research, the top priorities for change-related automation were the following:

1. IT process automation (ITPA) – Also known as “runbook,” ITPA provides policy-based capabilities for integrating and unifying automation across everything from patch management to onboarding to service provisioning using a common set of automated workflows.

2. Systems configuration – This included automation in virtualized as well as non-virtualized environments.

3. Workflow between the service desk and operations – This priority reflects the pervasive need for better integration between service desk/operations that was seen in EMA’s ITSM futures research.

4. Storage and network configuration – Natural extensions of both cloud and traditional infrastructure, storage configuration and network configuration were each noted as automation priorities by 36% of respondents.

5. Automation for lifecycle asset management – This type of automation is made visibly more difficult in hybrid cloud environments. Although ranked fifth, this area was still a high priority overall and was selected by nearly one-third of respondents.

Visualization and Reporting

This may not sound new, but good visualization and reporting are more critical than ever for managing change. One way to address visualization is to ask which roles and use cases are supported. For more general change management, some of the roles to consider are domain specific (e.g., network, systems, desktop, mobile, security, service desk, applications, development). Cross-domain roles are also important, including engineering, architecture, service delivery, change management, asset management, financial planning, and user-experience management. Finally, to be truly aligned with business priorities, it’s often critical to include non-IT stakeholders, such as financial planning, non-IT executives, marketing, online operations, and partner management.

Service Catalog

Ideally, a service catalog, or even a service portal, should be a natural extension of the CMDB system, with consistent modeling and insight into relevant interdependencies. This is just as true for those catalogs that include cloud-based services as it is for those including in-house services as it’s becoming increasingly important to understand dependencies outside IT’s physical walls. Incorporating public-cloud, private-cloud, and non-cloud services in an integrated service catalog is more and more in demand, as is support for mobile access to corporate and other services.

Mobile and Self-Service

Managing mobile devices and support for self service, while separate, are linked in how they both support escalating consumer expectations surrounding the choice, availability and performance of IT services. Our ITSM futures research showed that mobile adoption has already become heterogeneous in most environments — including iPhones, Android, tablets and other devices. Moreover, 65% of our respondents offered mobile end consumers some level of access for corporate applications.

The top priority for self-service, the most consumer-driven form of change management, was, not surprisingly, automation for end-user access to services. Near the top were also self-service via a service catalog, and mobile access. Other prioritized self-service objectives, somewhat less directly related to change management, were self-service enabled knowledge management and automation for resolving end-user issues.

Changing Technologies in a Changing World

These are not the only technologies relevant to managing change. For instance, lifecycle asset management, usage analysis, and onboarding are all part of the picture. Moreover, analytics relevant to change management is increasingly part of the story as well — a topic I’ll address in October. But if there’s a net takeaway, it’s that change management technologies are themselves changing to address a changing world in the so-called “Digital Age.” This brave new world is neither “stable nor precise” according to the traditional IT canon. But it’s one of greater opportunity, greater dynamism, and greater dependency on IT services and their digital content than ever before.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...