PBIMCR presents 'A Case Study on PBI & Security Analytics in Fabric & PBI'

Join us for June's PBIMCR meet-up

When: Thursday 25th of June 2026
Time: arrive for 530pm with talks starting from 6pm start prompt
Location: Slalom, 17 Marble Steet, M2 3AW
Complimentary drinks & pizza provided by our hosts Slalom & sponsors Robert Walters

Duncan Boyne
“Can Someone Turn the Heating On? An Accidental Case Study in Power BI”

A manufacturing company had a problem.

Their internal testing failure rate sat comfortably below 1%. Their customers, however, were seeing replacement rates as high as 15%.

The strange part? It wasn’t consistent.

Some customers barely experienced issues at all. Others saw failures come in waves. Summer spikes. Winter spikes. Complaints would rise, disappear, then return again months later.

• The data looked good.
• The engineers were smart.
• The dashboards were working.
• So why was the real-world experience telling a completely different story?

In this interactive session, I’ll walk through the accidental investigation that uncovered the issue, not through advanced AI, complex modelling, or some magical DAX formula, but through curiosity, context, and asking what felt like very dumb questions.

Questions like:

• What’s different between testing and storage?
• What happens after the product leaves the workshop floor?
• Why are certain customers affected more than others?
• And eventually:
• Why is nobody putting the heating on?

This session is part Power BI story, part consultancy lesson, and part reminder that data professionals are not just report builders, we are storytellers. Sometimes the numbers only make sense when you step away from the dashboard and start understanding the humans, processes, and assumptions behind them.

You’ll leave with a practical framework of questions every consultant and analyst should ask, along with a different perspective on how to approach problem solving, stakeholder conversations, and the stories hidden inside your data.

Darlington Chigozie Okeke
'Security Analytics with Microsoft Fabric & Power BI'

Machine-learning-based malware detection systems generate large volumes of structured and semi-structured data: behavioural features, anomaly scores, classification outputs, and confidence metrics. While much attention is given to building detection models, far less focus is placed on how these outputs are operationalised, analysed, and communicated at scale. This is where Microsoft Fabric and Power BI become critical.

In this session, I present a deep technical walkthrough of transforming ML-based malware detection outputs into a scalable analytics solution using Microsoft Fabric and Power BI, based on my MSc Cyber Security research into heuristic-based Trojan detection on Windows.

The talk focuses on:

• Designing Fabric Lakehouse schemas for ML-generated security telemetry

• Modelling detection outputs (labels, probabilities, feature scores) as analytical fact tables

• Using Spark SQL and T-SQL for feature aggregation, windowed analysis, and trend extraction

• Building semantic models optimised for high-cardinality, time-series security data

• Writing advanced DAX measures for detection accuracy, false positives, and behavioural drift

• Visualising long-term trends and anomalies using Power BI dashboards

Rather than revisiting ML theory, the emphasis is on data modelling, analytics engineering, and performance optimisation within Fabric and Power BI. While the data originates from a security context, the architectural patterns demonstrated apply equally to observability, fraud detection, and other event-driven analytics workloads.

This session is aimed at Power BI and Fabric practitioners who want to push beyond traditional BI scenarios and work with complex, real-world analytical data.

Topics Covered: Power BI, Microsoft Fabric, Azure Analysis Services, Azure AI, OneLake, Real-Time Data

1st time attendees what to expect:
Engaging tech talks/demos/presentations and an opportunity to network in a friendly community environment with complimentary drinks and pizza served.