The approach this user describes, muddling through Power Query, building a Frankenstein's Monster of applied steps, then repeating the same lookups because the process was never clear, is far more common than most people admit. And it is the single biggest barrier to getting real value from the tool. The problem is not a lack of technical skill. It is a lack of methodology. You can learn every M function and still end up with a query that works but cannot be maintained, reused, or trusted. That is not a failure of effort; it is a failure of structure.
Here is what we think: before you open Power Query, open a notebook, or a blank sheet in Excel. Write down what the source data looks like, which columns you need to keep, which transformations are required, and what the final output should contain. List your governance checks separately, not as inline steps that turn your query into a tangled knot. That single act of planning, done in five minutes, eliminates the guesswork that creates those messy step lists. The user who posted this is already doing the hard part, they are cleaning and transforming data for major projects. The missing piece is simply deciding the path before walking it. For governance checks that need to flag errors back to the source data, a separate query that merges back to the main table is cleaner than burying those checks inside the transformation steps. It keeps the logic visible and the output manageable.
As for Power BI, the answer is yes, but not because it is "better" in some abstract sense. It is better because it separates data modeling from presentation. In Excel, your pivot tables and charts are tied to the same workbook that holds your queries. That works for small projects. For the kind of recurring, governance-heavy workloads this user is describing, Power BI lets you build the query once, publish it, and refresh it without touching the file again. The visuals become a separate concern. That is not hype; it is a practical reduction in maintenance effort. Start with a single report. Connect your cleaned output from Power Query into Power BI, build one dashboard, and see whether the separation feels like overhead or relief. Most people find it is the latter.
The real takeaway here is that mastery does not come from memorizing more functions. It comes from treating each query as a repeatable process, not a one-off rescue mission. Plan first. Separate your governance logic. And when the data grows beyond a single workbook, let Power BI handle the visuals so your queries stay focused on what they do best: transforming data reliably. That is the methodology that turns a Frankenstein's Monster into a clean, reusable asset.