Excel: Use Power Query, Not VLOOKUP

Key insights

• Main takeaway: The video explains that Excel pros are moving beyond cell-by-cell lookup formulas and using the Power Pivot + DAX Data Model for faster, cleaner data work.
  Using a data model avoids duplicated data and scales much better on large files.
• Limitations of classic lookups: VLOOKUP and similar formulas duplicate data and slow workbooks as they grow.
  Even though XLOOKUP fixes many issues, formula-based lookups still struggle with very large datasets.
• What XLOOKUP fixes: XLOOKUP supports bidirectional lookups, simpler syntax, built-in error handling, and default exact matches.
  It’s a strong upgrade for single-sheet lookups and dynamic arrays when you use modern Excel versions.
• Why use Power Pivot and DAX: Create table relationships and write compact measures instead of many lookup formulas.
  This keeps one source of truth, reduces file size, and makes complex aggregations and multi-criteria queries far easier.
• Compatibility and alternatives: XLOOKUP needs Microsoft 365 or Excel 2021+.
  On older Excel, use INDEX-MATCH or import data with Power Query and then use the data model for better performance.
• Practical advice: Use XLOOKUP for quick or small tasks, but adopt the Data Model with Power Pivot and DAX for scalable reporting.
  Start by learning basic DAX measures and building simple relationships to see immediate speed and clarity gains.

In a recent YouTube video, Excel expert Mynda Treacy (MyOnlineTrainingHub) [MVP] argues that many users should move beyond classic lookup formulas and adopt a different approach for larger, more complex datasets. She shows why relying on VLOOKUP or even XLOOKUP can become inefficient as spreadsheets grow, and instead recommends using a data model-based workflow. Consequently, her presentation emphasizes performance, reduced duplication, and cleaner formulas. Moreover, the video provides a practical demonstration of how the alternative changes daily data work.

What the video demonstrates

Treacy begins by reproducing common lookup scenarios that many analysts face, and then compares the behavior of lookup formulas to a model-based approach. She highlights how lookup formulas often replicate columns across sheets, which may appear convenient but increases file size and slows recalculation. Next, she constructs the same report using a Power Pivot data model and DAX measures to show the contrast in speed and maintenance. As a result, viewers can see both the immediate performance gains and the long-term organizational benefits.

Why lookups become a problem at scale

First, lookup formulas like VLOOKUP and related combinations duplicate data by design because they pull values into each workbook or sheet that needs them. Therefore, large workbooks with many lookup formulas often suffer from longer calculation times and more frequent file corruption risks. Second, lookups create fragile formulas because they rely on positional ranges or on multiple nested functions that are easy to break during sheet restructuring. Consequently, teams that share files across different Excel versions can encounter inconsistent behavior and unexpected errors.

Moreover, even modern functions such as XLOOKUP improve flexibility but do not remove this underlying duplication issue when used inside many rows or across several reports. While XLOOKUP solves left-right limitations and adds built-in error handling, it still executes row-by-row and can multiply processing work in large models. Thus, performance and maintainability remain central concerns for heavy users and shared models. In turn, these factors motivate the search for a more scalable alternative.

How Power Pivot and DAX change the approach

The video promotes using Power Pivot to load tables into a central data model and then applying DAX measures to compute results dynamically without duplicating source rows. This approach stores each table only once and uses relationships to join them, which reduces file bloat and speeds up calculations. Additionally, DAX measures evaluate in context and return aggregated results on demand, so reports remain responsive even as data volumes grow. As a result, the model-based workflow often yields both faster refreshes and simpler report logic.

Importantly, Treacy shows that calculated measures avoid repeated cell-level formulas and centralize business logic in one place, improving consistency across reports. Meanwhile, a well-designed data model also supports multiple report views without copying or transforming the original tables repeatedly. Therefore, teams can deploy the same authoritative dataset for different stakeholders while maintaining a single source of truth. This structure encourages better governance and easier auditing of calculations.

Tradeoffs and real challenges

However, adopting Power Pivot and DAX is not without tradeoffs: the learning curve can be steep for users accustomed to formula-based spreadsheets. While the video simplifies concepts, mastering DAX requires time and practice, and some formulas behave differently than traditional Excel functions. Additionally, compatibility issues arise because not all recipients may have a version of Excel that supports the data model or modern features. Consequently, teams must plan transition paths and training to ensure broad adoption.

Furthermore, model-based solutions add governance and design responsibilities that some small teams may find onerous at first. For example, defining keys, designing relationships, and testing measures demands planning and discipline. Nonetheless, the long-term benefits in performance and clarity often outweigh these initial costs for organizations that handle frequent or large-scale reporting. In contrast, quick one-off lookups still serve as an efficient choice for simple, short-lived tasks.

Practical guidance for everyday use

Practically speaking, Treacy’s video recommends a balanced approach: use simple lookups for quick, ad-hoc tasks, and adopt a data model when reports scale or need consistent governance. Therefore, teams should evaluate file size, refresh time, and collaboration needs before choosing a strategy. Meanwhile, incremental adoption—starting with a few tables in the model—can reduce risk and help users build DAX skills gradually. As a result, organizations can combine the immediacy of formulas with the robustness of a model-based architecture.

Finally, the video encourages documenting model design and standardizing naming conventions to ease handoffs between team members. Consequently, this practice reduces confusion and makes maintenance more predictable over time. In short, Treacy frames the shift as a strategic investment: although it requires effort up front, it pays off with faster, cleaner, and more reliable reporting for teams that handle growing data volumes. Therefore, viewers are left with a clear case for reconsidering how they build Excel reports.

Keywords

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