How do you ensure data fidelity to build trust in AI automation?

IT teams are responsible for the performance of digital experiences, even when they don’t own the cloud and Internet networks they run on. With the Internet as the new corporate backbone, it’s easy to get overwhelmed by the vastness of this unowned environment and its importance for core business operations.

With billions of data points from various sources, the task of determining and harnessing relevant insight goes beyond human scale. Let alone collating it in a manner that’s contextually relevant to the different teams that have a stake in, and influence over, how a digital experience is delivered and assured.

This inability to see and manage external cloud and Internet networks like you own them has implications for the frontend. Users who experience inconsistent application or website performance may grow to avoid these digital services or withdraw from using them altogether.

While the challenge for these teams is to gain control of an uncontrolled or semi-controlled space in order to achieve digital delivery excellence – it isn’t the impossible task it may seem.

It’s a question of data

Digital delivery excellence is quintessentially becoming a question of data. 

IT teams are inundated with alerts and noisy data from their owned infrastructure, and a growing estate of unowned – third-party – infrastructure as well. The volume and variation make it nearly impossible to determine what might be a red herring, how to prioritize, and where to apply resources most effectively to achieve the biggest noticeable uplift in digital experience.

The way data-intensive problems of this magnitude are solved today is through the use of AI.

Digital Experience Assurance (DXA) is an emerging IT management discipline that leverages AI-driven intelligence for IT teams to quickly correlate and identify patterns to triangulate the source of outages and disruptions. With faster prioritization of which incidents require attention and which do not, mean-time-to-resolve (MTTR) for digital performance problems can be reduced from hours to minutes.

But there is much more that DXA is capable of, both now and in the future.

While getting to grips with data and extracting actionable insights is valuable, the other side of DXA is

enabling closed-loop workflows that automate and accelerate critical actions. This is powering a radical shift in IT operations from monitoring to intelligent automated action. The intent is for the AI to correlate, analyze, diagnose, predict, optimize, and remediate performance issues with little or no manual intervention. This is the opportunity at hand that leading IT teams are beginning to seize.

But in IT, there is no automation without trust.

To trust the signal of an AI-powered recommendation enough to allow it to trigger an automated action, there needs to be trust in the fidelity of the data feeding that AI engine. Fidelity in this context is about having data from all corners of an organization’s internal environment and from across the Internet landscape as well; and for that data to be extracted and brought to the AI in a clean and standardized format.

CIOs and IT teams that leverage these kinds of AI capabilities as part of their digital experience delivery will find their experiences perform smarter and faster, and that they have a greater ability to capitalize on the digital opportunities ahead.

To learn more, visit us here.