7. Microsoft Copilot for Data Analysis and Data Mining
- Faster, repeatable analysis workflows — automate data cleaning, feature engineering and routine exploratory steps so analysts spend time solving problems, not preparing data.
- Actionable Lean insights from raw data — mine operational datasets for waste, anomalies and lead indicators that directly feed daily management and improvement actions.
- Trustworthy, explainable AI support — practical model-validation and governance patterns that make Copilot-driven insights auditable, interpretable and safe for operational decision-making.
Introduction
This one-day, hands-on workshop equips advanced analysts, continuous improvement practitioners and Lean leaders to use the Microsoft Copilot Analyst Agent and the Microsoft 365 analytics stack to generate real Lean insight from raw data. The course focuses on automating repetitive data-preparation work, applying data-mining and anomaly-detection techniques to operational datasets, and producing predictive signals that drive daily management and improvement cycles.
We begin with the Copilot Analyst Agent — how it can accelerate exploration, suggest features and surface hypotheses — then move into practical patterns for automating data cleaning and transformation across Excel, Power Query and Power BI. You’ll learn methods for mining production and transactional data to find waste, variability and performance anomalies and how to convert those findings into prioritized improvement opportunities.
The workshop covers model validation, bias checks and a governance-first approach so AI-derived insights remain trustworthy and explainable. Through instructor demos and hands-on labs you’ll produce reusable notebooks, Power Query scripts, Power BI templates and Copilot prompts that automate analysis workflows and feed findings into Lean reviews and Obeya boards.
Deliverables & outcomes: a Copilot Analyst Agent prompt library and sample project, automated data-cleaning scripts and Power Query patterns, anomaly detection and clustering examples, a small predictive model (sample code), Power BI report templates for Lean insights and a playbook for integrating AI-driven findings into daily reviews.
About the course
Overview & Objectives
Learn how to operationalise data analysis using Copilot and Microsoft 365 tools so Lean teams can make faster, data-driven decisions. The session focuses on practical workflows and reproducible artefacts.
- Course goals
- Workshop structure and hands-on labs
- Deliverables and success criteria
Copilot Analyst Agent
Overview of the Copilot Analyst Agent: how it supports exploration, suggests features, creates code snippets and summarises findings for operational use.
- Agent prompts for exploration
- Generating code (Power Query, M, Python/PowerShell snippets)
- Turning agent outputs into reproducible workflows
Data Cleaning & Transformation
Hands-on patterns for cleaning messy operational data using Power Query and Excel automation so datasets are consistent and analysis-ready.
- Standardising timestamps and event logs
- Handling missing values and duplicates
- Reusable Power Query recipes and templates
Data Mining & Anomaly Detection
Apply clustering, outlier detection and statistical tests to find waste, variability and performance anomalies that warrant improvement work.
- Clustering and segmentation to find similar failure modes
- Time-series anomaly detection for process signals
- Automated summary reports for Lean reviews
Predictive Insights
Build and validate simple predictive models that rank opportunities or predict deviations, and convert scores into actionable signals.
- Feature engineering for operational use
- Model selection and evaluation metrics
- Deployment patterns for scoring and refresh
Integration with Power BI
Practical ways to surface AI-driven findings in Power BI dashboards and Obeya rooms so teams can act directly from insights with traceability.
- Explainable visuals and tooltips
- Actionable cards and drillthroughs
- Embedding narrative summaries and Copilot prompts
Validation & Governance
Governance practices for ensuring models and Copilot outputs are auditable, explainable and safe to use in operations.
- Bias and stability checks
- Explainability and simple SHAP-style approaches
- Versioning, retraining cadence and audit logs
Capstone Lab
Apply the workshop techniques to a real dataset: automate cleaning, run a mining pipeline, create a predictive score and surface findings in a Power BI report with Copilot summaries.
- Capstone build and demonstration
- Peer feedback and refinement
- 30/60/90 plan for operational integration
Instructors
Hamza Elharchi Elmaslohi
AI & Digital Lean Consultant
Hamza is an AI & Digital Lean Consultant with a Master’s in Data Science from Eötvös Loránd University (ELTE), Budapest. He holds certifications as an Azure Data Engineer Associate and Fabric Analytics Engineer Associate, showcasing his proficiency in Microsoft’s data and AI platforms. Specializing in integrating AI and data into Lean Management practices, Hamza leverages tools like Azure AI Foundry and the Power Platform to enhance operational efficiency and drive continuous improvement. His approach focuses on automating workflows and transforming data into actionable insights, enabling organizations to optimize their processes effectively
