We listed the top 2-3 types of lists that caused the most pain (e.g., "customer verbatim feedback," "new feature requests," "bug reports").
Define the "Ideal State": For each problematic list type, we sketched out exactly what the final, structured data should look like. What columns did we need? What data types? What were the allowable values for each category? (e.g., [Feedback ID, Original Comment, Sentiment, Feature Category, Priority, Action Item, Date]).
Quantify the Pain: We estimated the time lost, errors made, and delayed decisions due to our current inefficient process. This helped us build a strong case for dedicating resources to the improvement.
Phase 2: Strategy & Tooling (Day 1-2 / Week 1 / Month 1 - Mid-Period)
Standardize Templates for Input (When Possible): For new lists, we created simple templates brother cell phone list or guidelines for how information should be captured initially. Even small changes, like using consistent delimiters (Feature X: Y Description [Priority Z]), made a huge difference.
Choose the Right Tools:
Spreadsheets (Advanced): For many cases, we realized we weren't leveraging Excel/Google Sheets nearly enough. Functions like TEXTBEFORE, TEXTAFTER, FIND, MID, REGEXEXTRACT (Google Sheets), and XLOOKUP/VLOOKUP became our best friends for parsing and enriching data.
Basic Scripting (Python/R): For larger or more complex transformations, we explored simple Python scripts with libraries like Pandas. This allowed us to automate repetitive parsing and cleaning tasks.
Data Validation Rules: We implemented data validation (dropdowns, number ranges) directly in our target spreadsheets to prevent future inconsistent entries.
Develop a "Mapping Playbook": For each type of list, we documented the steps: "If you get a customer feedback list, here's the target schema, and here are the formulas/steps to extract Sentiment from the Original Comment."
Phase 3: Implementation & Iteration (Day 2-3 / Week 1 / Month 1 - Final Days.