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Digital Transformation Needs Intentionality

Larry Dragich

With the convergence of technology finding its way from the corporate world to our personal devices and home appliances, meeting the expectations of a quality customer experience is a formidable challenge.

Digital Transformation seems to be on everyone's radar but if there is no intentionality from the IT Executive who is sponsoring the program it becomes more of a loose correlation of technology initiatives under an IT strategy banner.

Consider some of the initiatives: Data/Analytics, Mobile Technology, Private Cloud, Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). All have their own unique role to play that is intrinsic to a Digital Transformation program. Although, when you step back and consider how to measure the success for such a program, things can get a little murky.

Digital Transformation requires more than just the latest technology, it's a mindset that iterative change is on the way and should be embraced. This also requires us to factor in the people and process parts of the equation and find ways to measure the end-user-experience (EUE).

One way to do this is to sponsor an Application Performance Monitoring (APM) initiative that can provide visibility to the business, help communicate the progress, and highlight the impacts to the organization.

Meaningful metrics can be difficult to obtain without a specific focus on business impact (transactions) and a concise way to collect them. Consider that a strong APM solution opens the door for better clarity on how each technology initiative affects the EUE, providing key metrics for a Digital Transformation program.

I recommend including all three monitoring factions within an APM strategy, Wire Data Analytics, Synthetic Transactions, and Agent Code Instrumentation.

1. Wire Data Analytics- Discover and decipher application performance data as it traverses the network.

2. Synthetic Transactions- Web robots that execute specific transactions for location-based availability and act as a barometer for measuring application performance.

3. Agent Code Instrumentation- Lightweight agents monitoring the application code as it executes from the Web and App servers. To gain visibility at the edge, script injection is often used for client render time.

Utilizing these overarching delivery mechanisms to provide input into a Machine Learning and/or AI solution has the potential to dramatically improve application delivery and performance across a variety of IT disciplines. This also lays the ground work to support a DevOps culture, providing an amplified feedback loop that is so desperately needed.

Once you develop a strategy on the best way to bring together the 3 monitoring factions, APM becomes "table stakes" on the digital transformation front because you can't improve what you don't measure.

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Digital Transformation Needs Intentionality

Larry Dragich

With the convergence of technology finding its way from the corporate world to our personal devices and home appliances, meeting the expectations of a quality customer experience is a formidable challenge.

Digital Transformation seems to be on everyone's radar but if there is no intentionality from the IT Executive who is sponsoring the program it becomes more of a loose correlation of technology initiatives under an IT strategy banner.

Consider some of the initiatives: Data/Analytics, Mobile Technology, Private Cloud, Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). All have their own unique role to play that is intrinsic to a Digital Transformation program. Although, when you step back and consider how to measure the success for such a program, things can get a little murky.

Digital Transformation requires more than just the latest technology, it's a mindset that iterative change is on the way and should be embraced. This also requires us to factor in the people and process parts of the equation and find ways to measure the end-user-experience (EUE).

One way to do this is to sponsor an Application Performance Monitoring (APM) initiative that can provide visibility to the business, help communicate the progress, and highlight the impacts to the organization.

Meaningful metrics can be difficult to obtain without a specific focus on business impact (transactions) and a concise way to collect them. Consider that a strong APM solution opens the door for better clarity on how each technology initiative affects the EUE, providing key metrics for a Digital Transformation program.

I recommend including all three monitoring factions within an APM strategy, Wire Data Analytics, Synthetic Transactions, and Agent Code Instrumentation.

1. Wire Data Analytics- Discover and decipher application performance data as it traverses the network.

2. Synthetic Transactions- Web robots that execute specific transactions for location-based availability and act as a barometer for measuring application performance.

3. Agent Code Instrumentation- Lightweight agents monitoring the application code as it executes from the Web and App servers. To gain visibility at the edge, script injection is often used for client render time.

Utilizing these overarching delivery mechanisms to provide input into a Machine Learning and/or AI solution has the potential to dramatically improve application delivery and performance across a variety of IT disciplines. This also lays the ground work to support a DevOps culture, providing an amplified feedback loop that is so desperately needed.

Once you develop a strategy on the best way to bring together the 3 monitoring factions, APM becomes "table stakes" on the digital transformation front because you can't improve what you don't measure.

The Latest

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...