Before any meaningful analysis can happen, well-prepared data is essential — and that’s exactly where this session begins. We start with a raw tenancy schedule, the kind that typically arrives messy, inconsistent, and difficult to work with. Using Power Query in both Excel and Power BI, we’ll walk through the complete process of transforming this unrefined dataset into a structured, reliable, and analysis-ready foundation for real estate reporting.
You’ll learn how to identify and fix common issues such as inconsistent date formats, mixed currency fields, duplicated rows, missing values, and structural irregularities that can quietly undermine your outputs. We’ll explore step-by-step techniques for removing errors, standardising data types, cleaning text fields, and applying rules that ensure your numbers behave exactly as they should inside your models.
The session also covers more advanced transformation skills: reshaping tables, unpivoting and pivoting data, splitting and merging columns, building automated logic for recurring cleaning tasks, and organising your query steps so they’re easy to follow and maintain. Along the way, you’ll see how Power Query can turn manual cleanup work — often done painfully in spreadsheets — into a repeatable, refreshable process that saves hours every month.
By the end, you’ll have a solid understanding of how to convert a raw tenancy schedule into a clean, dependable dataset ready for modelling and visualisation. This preparation phase sets the stage for accurate insights into occupancy, rental income, lease structures, and long-term asset performance. With a strong data foundation in place, every subsequent step in your real estate analytics workflow becomes more efficient, more accurate, and far more impactful.
