When just minutes of digital downtime could lead to millions in lost revenue, organizations need an accurate way to assess and progress the maturity of their operations. An accurate measure of digital maturity can help benchmark teams against best practices, revealing key areas for improvement. This can allow technical leaders to better identify the metrics with which to build more ambitious but still achievable strategic goals.
This is the logic that drives the digital operations maturity model. But times have changed. Rising customer expectations and the emergence of automation, machine learning and artificial intelligence.
What is digital operations maturity?
When the first maturity model for digital operations was devised in 2018, organizations needed a more accurate way to assess their maturity and how they could get better. As digital transformation came of age, it was clear that IT organizations had to transition from a queued and reactive IT service management (ITSM) model to a more agile DevOps approach. The journey includes culture, technology and people changes, but one thing remains constant: how to handle unplanned incidents.
In the past, most organizations were simply not capable of dealing with real-time work in a streamlined, seamless manner. Processes were designed around queued work, and employees tasked with responding to incidents had neither the tools nor the knowledge to do so effectively. For every minute teams spent troubleshooting, there were fewer opportunities to innovate. Just one hour of downtime caused by such incidents can cost between $1 and $5 million for one-third of organizations.
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My company, PagerDuty, originally identified just four stages of digital operations maturity based on our industry experience and observations: reactive, responsive, proactive and preventative. We saw that some companies were still in reactive mode, discovering issues only when customers reported them. Compounding the impact, employees lacked the skills, knowledge or authority to solve them.
Today, we classify most organizations as responsive. Thanks to new processes along with instrumentation and monitoring, most teams can see when things are going wrong. But although employees today are more capable, they often still lack the right information and authority to make quick and effective decisions.
We think that about one-third of organizations are in the more mature proactive mode, surfacing and resolving most issues before customers notice, empowering employees with knowledge and authority and automatically documenting learnings from past incidents. Only about a tenth of organizations belong to our top-tier preventative group, characterized by a culture of continuous learning and highly automated real-time response.
Pressure is building.
Then came the events of 2020. The advent of Covid-19 accelerated digital adoption in many organizations by years, permanently cementing the pre-eminence of operations teams at the heart of the modern enterprise. Today, nearly every business is a digital business.
Consumer demands have also evolved as their exposure to and dependence on online services increased. In fact, business executives who responded to an October 2020 McKinsey survey said they are “three times likelier now than before the crisis to say that at least 80 percent of their customer interactions are digital in nature.”
More than ever, organizations are under pressure to enhance their digital operations — to meet escalating customer expectations, resolve incidents proactively and free up time for important development and innovation projects. But although the pressure is greater than before, there’s also more help at hand if organizations can work out how best to leverage new technology. These developments require a redefined maturity model.
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Sometimes, to move forward, you must first take a step back. Our new digital operations maturity model adds a first stage: manual. This was necessary to define those organizations that haven’t yet leveraged automation to their advantage.
Many organizations have operational processes designed mainly for legacy environments, with incidents — almost always spotted first by customers — resolved manually and the response team using queued workflows to do so, such as support tickets. Urgent issues are also manually escalated and there are usually just a few ways to reach experts when escalating unplanned work.
The model has also evolved with more nuance to reflect the reality of modern operations. Many organizations classed as reactive have invested in tech for visibility and real-time mobilization as they mature and migrate to the cloud. They’re shifting from centralized to distributed technical teams and have documented processes for alerts, but major incidents are still managed ad hoc.
Responsive organizations are now starting to use machine learning to identify issues and reduce false positives and noise. Teams have more visibility into these issues, which can be automatically identified and then actioned by experts.
Meanwhile, proactive organizations are advancing machine learning into “AIOps” (machine learning tightly integrated with automation), finding issues before their customers and seamlessly — potentially automatically — responding to them. When a human is needed, the right information can be delivered to the right people in a timely manner. And those people can be armed with automated routines that provide them with access and authority to act without risk.
A high degree of automation can reduce responder toil and escalations and usually saves time. This, in turn, can help create a more agile, resilient organization capable of predicting and mitigating the impact of incidents through continuous learning, leading to becoming a preventative company.
Take the first steps.
AIOps is positioned to change the world. In digital operations, it can help teams reduce manual tasks and human error, increase knowledge sharing and drive continuous improvement. But the road to maturity is ever-changing as technology advances and market dynamics rapidly evolve. Those organizations moving through the gears to reach digital operations maturity today haven’t just transformed their technology investments. They’ve modernized their entire enterprise by reinventing their culture and processes.
This may seem like a long way away for those in the early stages of manual operations maturity. But every journey begins with the first step. And with digital operations powering our business world, there’s no time to delay.
Enough explanation from me and hopefully useful for all of us.