Observability 101

If you look up “observability” you find a very academic definition on the “measure of how well internal states of a system can be inferred from knowledge of its external outputs.” And while the concept originated from electrical engineering and control theory, it has become a buzzword in software engineering and product development in recent years.

From the halls of MIT to the command lines of Silicon Valley, observability has morphed to include tools and techniques that take system monitoring, such as how long a process takes to complete, exceptions, errors and failures, to a new level that creates a single source of truth and instrumentation to provide actionable insights and business intelligence.

A few years ago, I was at a technology conference and an executive from Etsy told the audience, “If it moves, we track it. Sometimes we'll draw a graph of something that isn't moving yet, just in case it decides to make a run for it.” In other words, they are monitoring everything and making business and operating decisions on how critical business infrastructure is working.

So Big Tech has utilized observability within its infrastructure to make business decisions bridging engineering, operations and business management.

But how can a printing business use these strategies? Here are few examples:

Customer Experience 

How long does it take to provide a customer an estimate? How long does it take to order a product in a web-to-print store front? What’s the abandonment rate in your digital storefront?

Sales

You can measure what products are selling or monitor how sales have been changing for different products and adjust marketing and other resources accordingly.

Workflow

How fast can files be processed? What are common errors that break file processing? How long does it take to go from customer submission to printed output?

Print Production

What is the productivity of a piece of equipment today, this week, this month?

And with measurement in place, here’s a scenario enabled by system data coupled with observability and automation.

A customer needs a quick turnaround on a job, and places an order from your storefront. Your workflow software automatically catches a common file issue, because your developer had data insights to add a feature to catch that error, and queues up the print job. The print job goes to press and is sent to the bindery for cutting, but along the way the job cart gets misplaced. Your MIS/workflow system is tracking the status and raises an exception alerting staff but also requeues the print job so that it can be reprinted and finished, so it makes the approaching shipping deadline.

When you combine system monitoring data with instrumentation providing data analysis, machine learning and artificial intelligence, you open the door to a new level of automation.

From digital storefronts, to workflow, to hardware, modern print production systems now support the ability to monitor the state of system. We now have a tremendous amount of data at our fingertips. Using modern observability techniques, we can use data from software and hardware to make business and operation decisions that create better buying experiences for our customers and make production efficient and more profitable.