Once upon a time, you could be an expert generalist in managing IT infrastructure. Today? Not so much. Modern digital environments encompass so many constituent services and specializations that it’s impossible for any individual to truly master them all.
At the same time, the importance of IT infrastructure, and the extent to which organizations rely on it for basic business continuity, has never been greater.
As compute costs trend lower, and natural language queries and machine learning (ML) usability advance, we’re witnessing what we call the “democratization of AI.” This trend has many facets, but they boil down to a basic reality: The power of algorithmic analysis is no longer locked away where only data scientists can access it. Suddenly, anyone can do so.
This change will have profound effects on IT operations for every organization. And if you’re not taking steps to capitalize on it, you should be.
To support today’s sprawling business infrastructures, most organizations maintain multiple dedicated teams. Each focuses on a specialized domain (applications, networking, servers, cloud), typically using domain-specific tools, and working with domain-specific data.
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This level of specialization is needed to diagnose problems and keep each part of the infrastructure healthy. Yet inevitably, it creates data silos that fragment information across the end-to-end environment, making it more difficult to support.
The AI advantage
But what happens when more people can access AI-enabled data and insights? They can:
Make better decisions
Each day in the life of an IT engineer involves dozens, sometimes hundreds of decisions. (Does this alert require further investigation? How should I remediate this failure? Which software version should I deploy? Which configuration is optimal for my environment?)
With siloed data and visibility, people often make those decisions based on limited information. By applying algorithmic analysis broadly, more people can make data-driven choices that collectively produce more reliable, better-performing infrastructures.
Provide better experiences
With more data and deeper insights, operations teams get better at ensuring high-quality experiences. For example, the ability to correlate signals across data silos and quickly isolate root causes reduces Mean Time to Detect and Mean Time to Repair (MTTD/MTTR)–two of the most important metrics affecting user experience. Algorithmic analysis can also work proactively, surfacing hidden systemic issues and identifying degradations before they become customer-impacting failures.
As more teams in an organization adopt ML tools, the walls between isolated data silos break down. It becomes easier for specialists in different domains to speak the same language and address systemic issues spanning organizational boundaries.
The net result is a virtuous circle, where infrastructure teams get progressively better informed, faster, and more effective. Organizations can start with narrow use cases or easily solvable issues to augment individual domain-specific tasks. Over time, the aggregate impact of those successes grows. More people adopt ML techniques, and data-driven decision-making expands to new use cases, fueling ongoing innovation and efficiencies across the business.
[ Related reading: How the democratization of AI impacts enterprise IT ]
Start your transformation
If AI democratization sounds like a trend you’d like to get behind, you’re not alone. Here are some basic steps to prepare for data-driven operations in your organization.
Break down data silos
Initially, many in the AI industry focused on building ever more sophisticated ML models. What those making the most progress have found, however, is that data quality matters as much as the model itself. If infrastructure data is fragmented and incomplete, even the most advanced tools will deliver poor results.
To get better outcomes more quickly, focus on making sure that data is clean, properly contextualized, and ready for analysis. Systems across your infrastructure should bring all data, from all sources together–not just storing it in one place, but applying proper context to enable a holistic view.
Expand data literacy
Achieving widespread AI adoption requires an evolution in culture and mindset as much as technology. Everyone from the CIO to low-level engineers should be working to achieve at least a basic understanding of ML, so they can envision how it can impact their work. If data scientists are the only ones who understand data and algorithms, frontline network engineers will never recognize opportunities to solve day-to-day problems using these techniques.
At the same time, the value data scientists provide will be limited, because they don’t have the domain expertise to understand how to best apply algorithmic analysis. When everyone has basic fluency with data and ML tools, new opportunities to save time, optimize quality, and improve decision-making will arise organically.
Choose the right tools
Look for AIOps solutions designed with extensive domain knowledge that can connect and contextualize data across domains and vendors in your infrastructure. Effective solutions should also natively integrate with existing communications channels (ticketing systems, Teams, Slack, etc.) so that insights can be quickly shared across teams.
By taking steps now, you can put your organization in a position to keep pace with the relentless growth of complexity in IT infrastructure. You can empower frontline engineers with data-driven insights to work smarter and faster while more highly skilled staff focus on critical issues and strategic projects. You can adopt a healthier growth model for your business, where everyone becomes progressively more data-savvy and effective, and continually finds new ways to use AI to achieve better outcomes.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]