Building operational use cases on your modern data platform

Over the last couple of years at Altis Consulting, we have been seeing a significant shift in the type of use cases that organisations are seeking to deliver on their modern data platform ecosystems.

Traditionally, most workloads running on enterprise data platforms have focused on decision support capabilities such as management reporting, KPI dashboards, operational reporting and self-service analytics. These workloads were largely analytical in nature, with the primary objective being to provide insights that support decision-making.

However, organisations are increasingly looking to modern cloud data platforms such as Snowflake and Databricks to support mission-critical operational workloads that directly influence or execute business processes in real time.

These operational workloads are typically:

• Data and compute intensive
• Time-sensitive or near real-time
• Integrated across multiple enterprise systems
• Embedded directly into operational business processes
• Expected to meet higher availability and reliability standards

Operational use cases delivered by Altis

Whether it’s optimising pricing, forecasting demand, personalising customer interactions or supporting Agentic AI, we are seeing modern data platforms increasingly becoming part of the enterprise operational fabric across various clients and industry verticals. Some examples of operational use cases that we have delivered include:

• A pricing engine for a waste management company that receives customer quote requests in real time from a CRM platform, processes pricing logic through a cloud data platform and returns pricing recommendations back to customer service representatives during live customer interactions.
• A production optimisation solution for a major oil & gas producer, integrating operational telemetry from SCADA systems into a cloud data platform in real time, alongside data from other enterprise systems, to feed a third-party Integrated Production Modelling (IPM) solution used by production engineering teams.
• A customer engagement solution for a large ticketing and entertainment organisation, integrating real-time customer, event and transactional data into their cloud data platform to enable personalised marketing campaigns. This allowed marketing teams to trigger timely, behaviour-driven communications and apply advanced customer segmentation to improve engagement, customer experience and event outcomes.
• Demand forecasting solutions for retail organisations, enabling optimisation of inbound logistics, warehouse inventory levels and store replenishment strategies using machine learning models operating on large-scale transactional and supply chain datasets.

As organisations increasingly operationalise their modern data platforms, there are several important architectural and operating model considerations that need to be addressed.

Real-Time Integration Architecture

The latency requirements for operational workloads are typically a lot lower than for traditional reporting workloads.

From an inbound integration perspective, this often means moving beyond scheduled batch ingestion towards patterns such as:

• Change Data Capture (CDC)
• Event-driven architectures
• Streaming ingestion
• Real-time APIs

These approaches are critical when operational decisions depend on the latest transactional or telemetry data.

Outbound integrations also become significantly more important. Unlike traditional analytics workloads where data primarily stays within reporting environments, operational workloads often need to push decisions or insights back into operational systems and SaaS platforms in real time.

This drives the need for:

• API-first integration patterns
• Event publishing frameworks
• Reverse ETL capabilities
• Reliable retry and failure handling mechanisms

Data Application Frameworks

Many operational use cases require more than pipelines and dashboards.

Users may need interactive applications to enter operational inputs, run scenarios, review recommendations and trigger workflows. As a result, modern data platform ecosystems such as Snowflake and Databricks are increasingly providing native application and AI capabilities alongside their core data engineering functionality.

However, organisations should not assume that all operational use cases can or should be delivered purely using native capabilities within the data platform ecosystem itself. In many cases, operational solutions may still require applications to be developed using traditional software engineering stacks and frameworks such as React, Node.js and enterprise API layers.

This is blurring the traditional boundaries between data platforms, application platforms and AI platforms, requiring organisations to think carefully about where operational application logic should reside.

Support Considerations

A very different level of support is required for operational workloads compared to traditional decision support solutions.

If a management report is unavailable for several hours, the business impact may be manageable. However, if a mission-critical solution becomes unavailable, the consequences can include revenue loss, operational disruption or customer experience impacts.

As a result, building operational use cases requires stronger engineering and operational support practices around:

• High availability and disaster recovery
• Monitoring and alerting
• Performance and concurrency testing
• Incident response processes and SLAs

This also requires closer collaboration between data engineering, platform engineering, integration and operational support teams.

Agentic AI and Intelligent Automation

The rise of AI is accelerating the evolution of operational use cases.

Several of our clients are exploring agentic AI workflows that can orchestrate business processes, retrieve enterprise context, generate recommendations and trigger operational actions.

Modern data platforms are becoming foundational to these architectures because they centralise enterprise data, governance, semantic models and AI capabilities.

However, operational AI introduces additional complexity around latency, reliability, explainability and governance. Organisations should therefore introduce these capabilities incrementally and align them to clearly defined operational outcomes.

Governance and Data Quality

Operational workloads significantly increase the importance of trusted and governed data.

Poor data quality in a reporting environment may lead to incorrect decisions over time. In operational workloads, it can immediately impact pricing, forecasting, automation or operational execution.

This increases the need for:

• Data quality monitoring
• Master data management
• Strong governance controls
• Data lineage and observability

Operational AI workloads also require governance around explainability, model drift and human oversight.

Operating Model Considerations

Historically, data teams owned analytics platforms while application teams owned operational systems. Modern operational workloads blur these boundaries significantly.

To succeed, organisations typically require:

• Cross-functional product-aligned teams
• Strong platform engineering capabilities
• Shared accountability models
• DevOps, DataOps and MLOps practices

The modern data platform is no longer simply an analytics environment, it is increasingly becoming part of the enterprise operational backbone.

Conclusion

Modern cloud data platforms are evolving well beyond their traditional role as repositories for reporting and analytics.

They are increasingly being used to power operational, real-time and AI-driven business capabilities that directly influence core enterprise day-to-day operations.

While this creates significant opportunities for organisations to simplify architectures and accelerate innovation, it also introduces new architectural, operational and governance challenges that need to be carefully considered.

The organisations that will succeed in this space will be those that treat their modern data platform not simply as a reporting environment, but as a strategic operational platform capable of supporting intelligent, data-driven enterprise execution at scale.