Is your service assurance really service assurance?? (Part 2)

In yesterday’s article, we asked whether what many know as service assurance can rightfully be called service assurance. Yesterday’s, like today’s, post was inspired by an interesting white paper from the Netrounds team titled, “Reimagining Service Assurance in the Digital Service Provider Era.”

Below are three insightful tables from the Netrounds white paper:

Table 1 looks at the typical components (systems) that service assurance is comprised of. But more interestingly, it looks at the types of questions / challenges each traditional system is designed to resolve. You’ll have noticed that none of them directly answer any service quality questions (except perhaps inventory systems, which can be prone to having sketchy associations between services and the resources they utilise).

Table 2 takes a more data-centric approach. This becomes important when we look at the big picture here – ensuring reliable and effective delivery of customer services. Infrastructure failures are a fact of life, so improved service assurance models of the future will depend on automated and predictive methods… which rely on algorithms that need data. Again, we notice an absence of service-related data sets here (apart from Inventory again). You can see the constraints of the traditional data collection approach can’t you?

Table 3 instead looks at the goals of an ideal service-centric assurance solution. The traditional systems / data are convenient but clearly don’t align well to those goals. They’re constrained by what has been presented in tables 1 and 2. Even the highly touted panaceas of AI and ML are likely to struggle against those constraints.

What if we instead start with Table 3’s assurance of customer services in mind and work our way back? Or even more precisely, what if we start with an objective of perfect availability and performance of every customer service?

That might imply self-healing (automated resolution) and resolution prior to failure (prediction) as well as resilience. But let’s first give our algorithms (and dare I say it, AI/ML techniques) a better chance of success.

Working back – What must the data look like? What should the systems look like? What questions should these new systems be answering?

More tomorrow.

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