One question has loomed in the minds of CIOs and enterprise technology leaders for years: “Is our investment in technology impacting the success of the enterprise?”
As technology has advanced, this question has matured over the years. As the potential impact of technology has increased, organizations have sought to better understand that impact and how the evolution of technology ultimately affects the customer experience. Enter Application Performance Monitoring (APM).
APM, as a technology, was designed to prevent application failure and to ensure uptime, performance and an overarching understanding of the “application” – versus just the performance of the hardware that it operated upon. This was revolutionary as mean time to resolution (MTTR), the average time from when an incident is reported until it is resolved, has always plagued the performance and profitability of even the largest enterprises.
The ability to quickly identify points of failure means companies can reduce the downtime of applications, while simultaneously reducing the amount of frustration, cursing and otherwise unsavory finger-pointing behavior by development, database and infrastructure teams worldwide. The collective database and software engineering community breathed a long overdue sigh of relief.
As app failure war rooms started to become more efficient and effective, more questions began to boil up: “What kind of insight can we gain from APM so that we stop making these mistakes altogether?” and even “How can we get smarter about catching failures before they happen?” Enter AppDynamics.
AppDynamics, taking a revolutionary approach to APM by leveraging machine learning from the ground up, created a smarter, faster way to begin to sense problems. By capturing business transactions at every point across the customer experience, AppDynamics was able to use machine learning to understand what “normal” was at all points in time. This eliminated alert storms and began to help organizations more intelligently increase application uptime and performance. War rooms across enterprise DevOps organizations become, dare I say, civil again.
As APM continued to advance, we began to take note of a revolutionary way to use APM to move application intelligence out of the war room and into the board room.
Our software engineering and research division forged a Titan partnership with AppDynamics. As a company that both designs data center-grade technology infrastructure and develops enterprise applications, we understood that there is incredibly valuable intelligence between software and hardware. WWT went to work.
Using AppDynamics’ newest suite of analytics — Business iQ — and our internal management consultancy, the partnership marries quantitative application performance analytics and traditional business performance data. For the first time, APM started supplying complex, relevant data that helps businesses understand exactly how changes in code, connectivity and hardware affect the performance of the business. Upgraded a data center? How did it increase the total dollars spent? Changes to the codebase? How did it decrease the abandonment rate of the shopping cart?
Changes in hardware, software and communication technology affect every aspect of the business, but one thing remains the same: the customer experience affects the bottom line. Delivering consistent, responsive applications that help customers enjoy interactions with businesses are what it’s all about. Being able to understand how the complex underlying technologies that run our businesses touch each individual customer makes us more than successful — it makes us revolutionary