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3 Essentials for Agile Operations

Pete Waterhouse

For decades IT operations has been viewed as something of a back-office technology function; the IT engine room. That’s not wrong since the applications under control have generally been large monolithic systems of record designed to automate internal business processes. These systems have been inherently complex and tightly-coupled, so changing them has been difficult, time consuming and costly. As such, our operational mindset has remained firmly focused on maintaining reliability and avoiding risk at all costs – even if that means holding back releases and ticking off our colleagues in development.

Not anymore.

Now, business success is not only dependent on assuring the reliability of internal business processes, but also by leveraging mobile computing and the cloud for more ‘experience-centric’ customer engagement. Now, software has the potential to forge new business models and disrupt markets, meaning change intolerance and aversion makes way to experimentation and innovation. The net-net of course is faster more iterative application development, newer highly scalable architectures, and a far more diverse set of applications – some of which will be standalone, while others will be integrated within our existing business process fabric.

So against this backdrop, what are main reasons the IT operations discipline must adapt to support the application-driven imperatives of the business and what characterizes a successful agile operations transformation? It boils down to delivering on three essential requirements – speed, quality and scale.

In Pursuit of Quality

Since the applications now being developed are far more customer-centric, service quality will be assessed at many more points of engagement. Consider a web/mobile airline booking system for example. This might be comprised of ten or more discrete services (e.g. booking flight, checking baggage, choosing seats, ordering meal, scanning boarding pass, car rental etc.). Every one of which constitutes a point of interaction where quality in the form of an optimum experience has a direct correlation to customer retention and business gain.

The traditional approach would be to wait for each of these applications to reach production and then initiate monitoring. Yet this is problematic since any problems now have a significantly greater impact on the business and in all likelihood will require developers to fix them. The result – greater conflict, more technical debt and lost business.

A far more agile approach is to incorporate monitoring earlier in the software development lifecycle. This involves collaborating with development to replicate the deep analytics and traceability insights normally gained in production and applying them in a development and testing context. Through this, the immediate gain is stopping defects leaking into production, but there are more lasting benefits too.

Firstly, development has much earlier visibility into quality requirements before the system goes live and can enact any necessary refactoring strategies.

Secondly, key insights gained through transaction monitoring can be shared with the operations team, so they can immediately establish metrics and performance KPI’s against which the production environment can be measured.

Finally, and in true DevOps fashion, this approach creates tighter feedback loops so that everyone knows who the app is serving, what experience is expected, and where changes impact performance – continuously.

The Need for Speed

With business now being driven by a complex mix of highly experiential software services, it’s essential that any problems are detected and resolved at a pace that matches the speed of delivery. This however is difficult because monitoring systems generally lack the ability to provide uninterrupted transaction level visibility.

Some toolsets for example provide rich mobile analytics (usage, behavior, crashes) which is all great. But what happens when the success of a new national mobile app based sales promotion depends on the successful recording against a backend database and requires no network latency at peak times. Without end-to-end visibility that can follow transactions across all apps and infrastructure and that provides insight into the underlying causes for failure, no realistic service levels can be established with the business.

Scaling the Summit

Embracing newer horizontally scalable architectures is the way leading innovators are future proofing their businesses. Combined with microservice style development these architectures facilitate more rapid deployment of independent business services. Services that truly harness the cloud by dynamically scaling resources.

Taking advantage of this means monitoring must become equally future proof by scaling in tandem. However attractive from an architectural perspective, these applications will introduce greater complexity, more interdependencies, newer tech like NoSQL and document-based data stores (e.g. Cassandra and MongoDB) and initiate complex system behaviors due to their highly distributed nature.

The old approach of teams maintaining their own sets of specialized diagnostic tools over infrastructure that falls within their own silo is no longer sustainable. Now, more unified monitoring approaches must provide cross-functional teams with fast visual comprehension to reveal what matters versus what can and should be ignored. Additionally, change analysis to more rapidly isolate problems increases in importance, since the resources underpinning cloud-native applications will unexpectedly shift based on demand, cost and lifecycle.

New applications and architectures supporting more transformative digital business demand IT operations must become as agile as development.

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3 Essentials for Agile Operations

Pete Waterhouse

For decades IT operations has been viewed as something of a back-office technology function; the IT engine room. That’s not wrong since the applications under control have generally been large monolithic systems of record designed to automate internal business processes. These systems have been inherently complex and tightly-coupled, so changing them has been difficult, time consuming and costly. As such, our operational mindset has remained firmly focused on maintaining reliability and avoiding risk at all costs – even if that means holding back releases and ticking off our colleagues in development.

Not anymore.

Now, business success is not only dependent on assuring the reliability of internal business processes, but also by leveraging mobile computing and the cloud for more ‘experience-centric’ customer engagement. Now, software has the potential to forge new business models and disrupt markets, meaning change intolerance and aversion makes way to experimentation and innovation. The net-net of course is faster more iterative application development, newer highly scalable architectures, and a far more diverse set of applications – some of which will be standalone, while others will be integrated within our existing business process fabric.

So against this backdrop, what are main reasons the IT operations discipline must adapt to support the application-driven imperatives of the business and what characterizes a successful agile operations transformation? It boils down to delivering on three essential requirements – speed, quality and scale.

In Pursuit of Quality

Since the applications now being developed are far more customer-centric, service quality will be assessed at many more points of engagement. Consider a web/mobile airline booking system for example. This might be comprised of ten or more discrete services (e.g. booking flight, checking baggage, choosing seats, ordering meal, scanning boarding pass, car rental etc.). Every one of which constitutes a point of interaction where quality in the form of an optimum experience has a direct correlation to customer retention and business gain.

The traditional approach would be to wait for each of these applications to reach production and then initiate monitoring. Yet this is problematic since any problems now have a significantly greater impact on the business and in all likelihood will require developers to fix them. The result – greater conflict, more technical debt and lost business.

A far more agile approach is to incorporate monitoring earlier in the software development lifecycle. This involves collaborating with development to replicate the deep analytics and traceability insights normally gained in production and applying them in a development and testing context. Through this, the immediate gain is stopping defects leaking into production, but there are more lasting benefits too.

Firstly, development has much earlier visibility into quality requirements before the system goes live and can enact any necessary refactoring strategies.

Secondly, key insights gained through transaction monitoring can be shared with the operations team, so they can immediately establish metrics and performance KPI’s against which the production environment can be measured.

Finally, and in true DevOps fashion, this approach creates tighter feedback loops so that everyone knows who the app is serving, what experience is expected, and where changes impact performance – continuously.

The Need for Speed

With business now being driven by a complex mix of highly experiential software services, it’s essential that any problems are detected and resolved at a pace that matches the speed of delivery. This however is difficult because monitoring systems generally lack the ability to provide uninterrupted transaction level visibility.

Some toolsets for example provide rich mobile analytics (usage, behavior, crashes) which is all great. But what happens when the success of a new national mobile app based sales promotion depends on the successful recording against a backend database and requires no network latency at peak times. Without end-to-end visibility that can follow transactions across all apps and infrastructure and that provides insight into the underlying causes for failure, no realistic service levels can be established with the business.

Scaling the Summit

Embracing newer horizontally scalable architectures is the way leading innovators are future proofing their businesses. Combined with microservice style development these architectures facilitate more rapid deployment of independent business services. Services that truly harness the cloud by dynamically scaling resources.

Taking advantage of this means monitoring must become equally future proof by scaling in tandem. However attractive from an architectural perspective, these applications will introduce greater complexity, more interdependencies, newer tech like NoSQL and document-based data stores (e.g. Cassandra and MongoDB) and initiate complex system behaviors due to their highly distributed nature.

The old approach of teams maintaining their own sets of specialized diagnostic tools over infrastructure that falls within their own silo is no longer sustainable. Now, more unified monitoring approaches must provide cross-functional teams with fast visual comprehension to reveal what matters versus what can and should be ignored. Additionally, change analysis to more rapidly isolate problems increases in importance, since the resources underpinning cloud-native applications will unexpectedly shift based on demand, cost and lifecycle.

New applications and architectures supporting more transformative digital business demand IT operations must become as agile as development.

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 ...