Skip to main content

Innovation Explosion Sets Stage for New Wave of Hybrid Applications

Julie Craig

In the past, technology evolution was quantified as generational. So the first generation of computing was based on the vacuum tube, followed by the transistor, integrated circuits, and microprocessors. Even today, multi-generational software and hardware systems are very much alive in many modern data centers.

The interesting thing about the generational approach is that it has always been associated with linear progression and evolutionary growth. However the rapid technology evolution we are seeing today is more revolutionary than evolutionary, and IT is bearing the brunt of its impact.

Emerging from the financial doldrums of the 2008 – 2012 “downturn”, business and IT are confronted with an explosion of technology which has never been equaled in recorded history. Driverless cars, sensor-driven “swarms”, alternative energy sources, and “smart” electric grids are the tip of the iceberg. And in contrast to the generational, evolutionary innovation waves of the past, everything is happening at once.

Businesses are confronted with multiple concurrent streams of innovation encompassing cloud, sensor networks, Internets of Things (IoT), social media, and ubiquitous mobile. Each of these trends becomes, in turn, a foundation for the next wave of innovation. Each also provides business opportunities for creating the “next big idea” driving revenue from new products and services.

I have heavily covered this evolution with multiple research pieces over the past several years and am currently analyzing the results of my latest survey on the topic of the Extended Enterprise. This is an ecosystem-wide view of the trends currently impacting IT, summarized in a paper entitled Public Cloud Comes of Age: Application Performance Management (APM) Strategies & Products for a Production-Ready Cloud Ecosystem. With the paper scheduled to be published by December 15, I will be presenting a webinar on the topic on December 16. Click here to register .

The research focuses on the increasingly hybrid nature of today’s applications, particularly in view of the fact that this “explosion of innovation” is, essentially, being driven by software. Enterprise IT is already dealing with hybrid on-premise/public Cloud applications, and future hybrid deployments will encompass Internet of Things, mobile, and social media interactions and data as well.

A growing “API Economy” means that everything is connected. While interconnected apps take a toll on performance, particularly when they are connected via APIs, users expect the same levels of performance regardless of their device or location. And as data siphoned from sensor networks and related “Internet of Things” technologies increasingly becomes fodder for business-critical data stores and reporting, real time interoperability becomes increasingly important — as well as increasingly difficult to achieve.

In short, the challenges of monitoring and managing “hybrid” environments which I anticipated three years ago have now come to pass — in spades. Why is this important? Because all of these technologies are, or soon will be, leveraged by component-based applications and services. So in addition to “hybrid cloud”, we will also have “hybrid API-driven apps” (transactions executing via multiple API calls), “hybrid mobile” (mobile to mobile applications), and other combinations of “hybrids”.

Although it is an exciting and interesting time to be part of this evolution, one should also note the age-old curse that says: May you live in interesting times. IT professionals at all levels — including CIOs, Directors, and managers — are living in interesting times. With applications running “in the cloud”, accessing partner and supplier data centers, executing as mobile “apps”, driving down the road, and exchanging real-time data, the illusion of control has dissipated.

So the “big picture” question is: How can IT manage application performance and availability in an environment it can’t control? For enterprise IT, the question of the day then becomes, “Is YOUR organization ready to support these technologies”?

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 ...

Innovation Explosion Sets Stage for New Wave of Hybrid Applications

Julie Craig

In the past, technology evolution was quantified as generational. So the first generation of computing was based on the vacuum tube, followed by the transistor, integrated circuits, and microprocessors. Even today, multi-generational software and hardware systems are very much alive in many modern data centers.

The interesting thing about the generational approach is that it has always been associated with linear progression and evolutionary growth. However the rapid technology evolution we are seeing today is more revolutionary than evolutionary, and IT is bearing the brunt of its impact.

Emerging from the financial doldrums of the 2008 – 2012 “downturn”, business and IT are confronted with an explosion of technology which has never been equaled in recorded history. Driverless cars, sensor-driven “swarms”, alternative energy sources, and “smart” electric grids are the tip of the iceberg. And in contrast to the generational, evolutionary innovation waves of the past, everything is happening at once.

Businesses are confronted with multiple concurrent streams of innovation encompassing cloud, sensor networks, Internets of Things (IoT), social media, and ubiquitous mobile. Each of these trends becomes, in turn, a foundation for the next wave of innovation. Each also provides business opportunities for creating the “next big idea” driving revenue from new products and services.

I have heavily covered this evolution with multiple research pieces over the past several years and am currently analyzing the results of my latest survey on the topic of the Extended Enterprise. This is an ecosystem-wide view of the trends currently impacting IT, summarized in a paper entitled Public Cloud Comes of Age: Application Performance Management (APM) Strategies & Products for a Production-Ready Cloud Ecosystem. With the paper scheduled to be published by December 15, I will be presenting a webinar on the topic on December 16. Click here to register .

The research focuses on the increasingly hybrid nature of today’s applications, particularly in view of the fact that this “explosion of innovation” is, essentially, being driven by software. Enterprise IT is already dealing with hybrid on-premise/public Cloud applications, and future hybrid deployments will encompass Internet of Things, mobile, and social media interactions and data as well.

A growing “API Economy” means that everything is connected. While interconnected apps take a toll on performance, particularly when they are connected via APIs, users expect the same levels of performance regardless of their device or location. And as data siphoned from sensor networks and related “Internet of Things” technologies increasingly becomes fodder for business-critical data stores and reporting, real time interoperability becomes increasingly important — as well as increasingly difficult to achieve.

In short, the challenges of monitoring and managing “hybrid” environments which I anticipated three years ago have now come to pass — in spades. Why is this important? Because all of these technologies are, or soon will be, leveraged by component-based applications and services. So in addition to “hybrid cloud”, we will also have “hybrid API-driven apps” (transactions executing via multiple API calls), “hybrid mobile” (mobile to mobile applications), and other combinations of “hybrids”.

Although it is an exciting and interesting time to be part of this evolution, one should also note the age-old curse that says: May you live in interesting times. IT professionals at all levels — including CIOs, Directors, and managers — are living in interesting times. With applications running “in the cloud”, accessing partner and supplier data centers, executing as mobile “apps”, driving down the road, and exchanging real-time data, the illusion of control has dissipated.

So the “big picture” question is: How can IT manage application performance and availability in an environment it can’t control? For enterprise IT, the question of the day then becomes, “Is YOUR organization ready to support these technologies”?

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 ...