Operational Intelligence & AIOps
How can operational data lead to better decisions?
IN BRIEF
Operational intelligence is the use of live operational data from applications, machines, event streams and IT systems to make decisions while operations are running. It includes real-time analytics, process mining and AIOps, the use of AI in IT operations to detect and classify incidents. It depends on a sound data platform and reliable data quality. AIOps uses AI to analyse operational data, for example to detect anomalies, correlate events and support root-cause analysis.
When to involve us.
When operational data is spread across many systems but decisions rely on late reports, incidents are detected too late or it is unclear where processes actually stall.
We start with the decisions made in operations and derive which data is needed, when and in what quality. Using process mining, event analysis or AIOps, we make workflows and incident patterns visible and test on a limited use case whether decisions become faster or better. We define the data platform, data ownership and quality rules so that the use case holds up over time.
What the work produces.
We agree on the scope around your goals, starting point and available capacity. The following elements illustrate what an engagement can include.
WHAT WE ADDRESS
- Map of operational decisions and data
- Process mining or AIOps use case
- Target design for data platform and event processing
- Data quality rules and data ownership
POTENTIAL OUTPUTS
A map of operational decisions and the data they require, together with an evaluated use case. The findings show whether available operational data improves decisions and which gaps in data quality or event processing still need to be addressed.
A focused starting point.
A specific decision or process that should be better managed, and an overview of the systems involved. Sample data or event logs and the responsible people from operations and IT help to assess the data situation realistically.
The question first. Then the scope.
The first conversation clarifies the objective, deliverables and people involved. We agree whether the engagement covers design, pilot support or technical implementation, who takes on each task and how the deliverables will be assessed.
Discuss a starting point by emailDo we need a complete data platform first?
Not a complete one, but the data foundation has to support the first use case. A broad platform program without concrete use often shows no visible value for a long time. Conversely, analyses frequently fail because of incomplete data, missing timestamps or unclear ownership. It therefore makes sense to start with a specific decision, build the data foundation it needs and shape the platform around the next use cases. That way the platform grows with demand rather than in advance of it. Quality rules and data ownership are defined from the start.
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