What is this meeting about?
Do you recognize this?
Repetitive manual work in Excel-combining data from multiple sheets and sources, with a constant risk of errors?
Why attend?
Manual processes are time-consuming and prone to inconsistencies. This session shows how to improve accuracy, completeness, and cycle time by reducing manual effort through applying Power Queries.
What & how?
In this practical session, you'll learn how to use Power Query in Excel to combine data, create a controlled and repeatable process, and build a more reliable way of working-through hands-on examples you can apply immediately.
Presentation:
Stream:
Key take-aways:
Excel remains central to many HR and reward processes, but repetitive copying, reconciling, cleansing, and rebuilding of formulas creates avoidable work and increases the likelihood of errors. Power Query offers a more sustainable approach by turning recurring data preparation into a transparent and refreshable process. Its Extract, Transform, Load, or ETL, model creates a practical processing layer between raw information and reporting outputs. [meeting no…Next level | Word], [meeting no…Next level | Word]
The session showed that successful automation depends not only on the technology, but also on disciplined reporting design. Stable folder locations, structured source data, matching column names and data types, clearly named transformation steps, and separation between source and reporting layers all contribute to reliable and maintainable solutions. [meeting no…Next level | Word], [meeting no…Next level | Word]
- Design the source environment before building the query. Use a stable source location with replaceable files when only current information is required. For year-to-date or trend reporting, keep recurring period files together so they can be combined automatically. [meeting no…Next level | Word], [meeting no…Next level | Word]
- Separate raw data from reporting outputs. Keeping source files independent from the reporting workbook can reduce workbook size, improve performance, protect raw data, and make the reporting process easier to audit and maintain. [meeting no…Next level | Word], [meeting no…Next level | Word]
- Standardize data before automating it. Matching column headers, capitalization, spacing, and data types are essential when merging or appending datasets. Small inconsistencies can produce failed matches, duplicate fields, or incomplete results. [meeting no…Next level | Word], [meeting no…Next level | Word]
- Build once, refresh repeatedly. Recurring activities such as splitting columns, cleaning records, applying mappings, merging sources, and consolidating monthly files can be recorded as reusable transformation steps, reducing manual work and improving consistency. [meeting no…Next level | Word], [meeting no…Next level | Word], [meeting no…Next level | Word]
- Make every solution understandable to the next user. Organize queries logically, give transformation steps meaningful business names, document the process flow, and retain visible dependencies. AI can help troubleshoot or simplify M code, but should complement rather than replace understanding of the underlying logic. [meeting no…Next level | Word], [meeting no…Next level | Word], [meeting no…Next level | Word]
Writing approach: The copy has been condensed into an external-facing narrative, individual participants have been kept anonymous, and the most actionable lessons have been prioritized for quick scanning.
Source: meeting notes-26-08-Excel for Reward Nerds Next level.docx [meeting no…Next level | Word]
AI generated by Copilot from designated meeting content only | Rumbold
