What you get
- A workbook audit that finds slow formulas, broken links, and hidden errors.
- Rebuilt models that recalculate in seconds instead of minutes.
- Power Query imports that replace manual copying and pasting.
- Spreadsheet data migrated into a database with a clean structure.
- Live connections that bring warehouse data back into Excel for analysis.
- Documentation and a walkthrough, so your team can maintain it.
Modern Excel techniques I use
Power Query for repeatable data prep
Power Query imports, cleans, and combines data from files, folders, databases, and websites. It records each step, so next month's update is a single Refresh instead of an afternoon of rework. Its M language handles transformations the menus can't.
The Data Model, Power Pivot, and DAX
The Excel Data Model holds millions of rows, far beyond the worksheet's 1,048,576-row limit. Relationships between tables replace fragile lookup formulas, and DAX measures handle calculations like year-over-year growth and running totals.
Dynamic arrays, LET, and LAMBDA
FILTER, SORT, UNIQUE, and XLOOKUP replace columns of helper formulas. LET names intermediate results, making long formulas readable and faster. LAMBDA turns a formula into your own reusable function without any VBA, and GROUPBY and PIVOTBY build summaries with a single formula.
Python in Excel
Python in Excel runs pandas and charting libraries directly in a cell, which opens up statistics and analysis that would be painful in formulas, while the results stay inside the workbook your team already uses.
From workbook to database
When several people edit the same file, or data gets typed in twice, it's time for a database. I normalize flat sheets into related tables such as customers, orders, and products, then load them into SQL Server, PostgreSQL, or SQLite with proper data types and keys, so bad data can't sneak in.
From database back to Excel
Power Query and ODBC connections pull governed warehouse data into Excel on demand, with parameters for date ranges or regions. PivotTables built on the Data Model let analysts explore it freely. Excel becomes a window onto trusted data instead of yet another copy of it.
Performance tuning
The usual culprits are volatile functions like OFFSET, INDIRECT, and NOW, whole-column references, piles of conditional formatting, and lookups repeated thousands of times. Converting ranges to Excel Tables and restructuring calculations often makes a workbook ten times faster.
Tools and technologies
- Excel 365
- Power Query (M)
- Power Pivot
- DAX
- Dynamic arrays
- LAMBDA
- Python in Excel
- VBA
- Office Scripts
- Power Automate
- SQL Server
- PostgreSQL
- SQLite
Common questions
Why is my Excel file so slow?
The most common causes are volatile functions that recalculate constantly, formulas referencing entire columns, heavy conditional formatting, and the same lookup repeated thousands of times. An audit pinpoints which ones are hurting your workbook.
When should we move from Excel to a database?
Common signs: several people need to edit at once, the file is getting huge, the same data is entered in more than one place, or you need a reliable history of changes. You can keep Excel as the front end.
Can you fix a workbook someone else built?
Yes. I start by mapping how it works and documenting it, then fix and simplify it in stages so nothing breaks along the way.