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.

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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