Data aggregation combines detailed records or categories into summaries that answer a practical business question. When a product-category review chart becomes too large to read, aggregation can make patterns visible, but the grouping should reflect stakeholder needs rather than convenience alone. section 3.3.3
A practical aggregation workflow can:
- Inspect the detailed distribution before simplifying it, because a large chart may reveal that the current categories are too numerous. section 3.3.3
- Combine review scores into positive scores of 4 or 5 and negative scores of 1, 2, or 3, while recognizing that this threshold is an arbitrary decision. section 3.3.3
- Group product categories by a broader industry or area of interest when the dataset lacks an ontology, then explain the grouping decision. section 3.3.3
- Combine responses from multiple model queries when one response cannot produce the complete analytical output, and check the result carefully. section 8.4.3
Aggregating product reviews
Merge order, item, product, review, and translated-category data; group by English product category, seller, and review score; and count the resulting rows. Add a positive or negative review label, then map detailed categories into groups such as Home & Furniture. Inspect the resulting chart and validate whether the grouping remains useful for the intended presentation. section 3.3.3
