For many infrastructure and operations teams, incident management remains a reactive process. Tickets are reviewed, prioritised and resolved one at a time, often under pressure and with limited context. While most organisations have mature ticketing systems in place, they still struggle to consistently identify what truly matters.
The challenge is not a lack of data but how it is used. Prioritisation is typically driven by static fields such as category, impact, or urgency. In practice, these fields are often applied inconsistently or used as placeholders, leading to unreliable reporting. At the same time, the most valuable information, what actually happened and why, is captured in free-text fields like summaries, descriptions, and resolution notes but rarely analysed at scale.
This disconnect makes it difficult to see the full picture, increasing the risk of focusing on lower-impact issues while overlooking broader patterns and systemic risks.
A shift in perspective: Treating incident data as a strategic asset
We often see organisations approach incident management one ticket at a time rather than as a connected system of signals. This leads to short-term fixes instead of long-term improvements. In addition, structured ticket categorisation is often applied inconsistently, and the most informative data is captured in unstructured text that is rarely analysed at scale. This creates a disconnect where reporting may suggest one set of priorities while underlying issues tell a different story.
A more effective model is to treat incident data as a strategic asset. When structured data and unstructured free-text content are analysed together, it becomes possible to identify recurring issues, uncover root causes, and prioritise based on overall impact rather than individual ticket attributes. Multiple incidents initially categorised differently may in fact share a common root cause, indicating a broader infrastructure issue.
Recent advances in data and AI platforms are making this model more practical to adopt at scale. By consolidating ticket data into a centralised analytics environment and applying AI at scale, organisations can achieve consistent and explainable classification without the need for complex machine learning pipelines.
With these insights in place, teams can move beyond isolated ticket resolution and begin addressing underlying issues. In practice, this model often reveals patterns that traditional reporting fails to capture.
In one example, a leading U.S.-based energy company explored this model through a proof of concept. By analysing both structured and unstructured ticket data together, the team was able to surface hidden patterns and improve how incidents could be classified and prioritised, highlighting the potential for more proactive operations.
From data to decisions: Enabling faster, smarter prioritisation
Once incident data is consistently classified and accessible, the next challenge is enabling teams to act on it quickly.
Built on the Databricks Data Intelligence Platform, the AI Ticket Classification accelerator consolidates ticket data into a centralised analytics environment and applies AI at scale by evaluating ticket content against defined rules and returning a category, reason, and confidence score. This enables consistent and explainable classification at scale without the need for complex machine learning pipelines.
A materialised view continuously maintains an analytics-ready dataset within the Lakehouse, providing near real-time visibility into incident trends and patterns.
This is where conversational analytics plays an important role. Using capabilities such as Databricks AI/BI Genie, teams can interact with their data using natural language without needing to understand the data model or write queries.
For example:
- What is the root cause of this ticket and how has it been resolved in similar cases?
- Which tickets should we prioritise first and why?
In practice, this allows users to quickly surface related incidents, explore trends across tickets, and gain insight into prioritisation factors. This reduces time to insight from hours to minutes and improves confidence in decision-making.
Moving beyond dashboards: Why conversational analytics matters
Traditional reporting tools provide visibility into what has already happened but often fall short when teams need to explore and validate trends across incidents.
Conversational analytics provides a more accessible way to work with ticket data. Instead of relying on predefined dashboards or queries, users can ask questions in natural language and quickly surface trends, relationships, and potential root causes.
In the context of incident management, this makes it easier to identify which issues require attention, understand how similar incidents have been handled, and prioritise actions more effectively.
Where to start: Practical steps to modernise incident management
Organisations looking to improve prioritisation and reduce escalations can begin with these focused steps:
- Standardise classification using AI within a centralised analytics environment
Rather than relying solely on manual categorisation or separate machine learning pipelines, organisations can apply AI to consistently classify tickets based on their full context based on free-text fields.- Enable self-service conversational access to insights
Even with better data, access to insights remains a barrier. Many teams still depend on specialised skills to query data or build reports, which slows down decision-making. Conversational interfaces provide a more accessible way to interact with data and accelerate decision-making across teams.- Leverage a unified data platform for governance and scale
Centralised platforms support secure and governed access to data.
These steps help bridge the gap between data and decision-making, allowing teams to act on insights rather than assumptions.
The impact: moving from reactive to proactive operations
Taking this approach can deliver measurable improvements across infrastructure and operations:
- More accurate and consistent incident classification
- Earlier detection of systemic risks, helping reduce escalations
- Smarter prioritisation focused on high-impact and recurring issues
- Faster root cause analysis and resolution
- Improved SLA performance and overall system stability
Collectively, these outcomes support a shift from reactive incident response to more proactive and insight-driven operations.
Looking ahead
As environments grow more complex, the ability to interpret and act on incident data becomes increasingly important. Organisations that continue to rely on manual analysis and inconsistent categorisation risk missing the signals that matter most.
By leveraging modern data and AI capabilities and enabling more intuitive access to insights, organisations can take a more strategic approach to incident management, one that prioritises not only what needs to be fixed but what should be prevented.
If you are exploring how to improve prioritisation, reduce escalations, or better understand the root causes behind incidents, this represents a practical starting point for modernising incident management.
