Before meaningful analysis can begin, well-structured and reliable data is essential. This session is dedicated to the critical preparation stage, where we transform a raw, often messy tenancy schedule into a clean and organised dataset ready for real estate analytics. Using Power Query in both Excel and Power BI, we’ll walk step by step through the process of importing, examining, and reshaping source data so it becomes consistent, trustworthy, and easy to work with.
You’ll learn how to identify common data quality issues such as duplicate records, inconsistent formatting, missing values, and structural irregularities that can compromise reporting accuracy. We’ll cover practical methods for removing errors, standardising date and currency formats, and ensuring that numerical and textual fields behave correctly within your model. Along the way, you'll gain hands-on experience with essential Power Query transformations — including splitting and merging columns, unpivoting and restructuring tables, creating custom fields, and applying logic for automated data cleaning.
Beyond simple corrections, the session emphasizes building repeatable cleaning processes so updates to your tenancy data can be refreshed automatically with minimal effort. We’ll discuss best practices for organising query steps, documenting transformations, and structuring your dataset so it’s optimised for downstream modelling and visualisation.
By the end of this session, you’ll not only understand how to prepare and refine a tenancy schedule but also how to establish a robust data foundation that supports accurate analysis of occupancy, rental income, lease terms, and long-term property performance. This groundwork ensures that every insight you generate in later stages is built on clean, consistent, and professionally managed data.
