This is the Elector Review. Today, why your reports disagree and why more data won't fix it. A sales manager opens a weekly report and sees one revenue figure. The finance lead checks the accounts and gets another. Both calculations can be correct. One counts an order when the customer submits it, cancelled orders included. The other counts revenue only after settlement. The argument isn't about arithmetic, it's about what the word revenue means. That's the core claim of Electi's guide to conceptual data modeling. The real problem is often a lack of shared meaning, not a lack of data. Adding dashboards or connecting systems on top of definitions that don't match can spread the confusion instead of fixing it. The article's fix is a conceptual model. It names the business concepts called entities, customer, order, product, promotion. It says how they relate. A customer places orders and order contains products. It records cardinality, meaning how many of one thing can link to another, and it records business rules. Does a promotion apply by order date or by payment date? Do cancelled orders count? It deliberately leaves out tables, storage, and platforms. Those come later, in the logical and physical models. The idea isn't new. Peter Chen presented the entity relationship model in 1975 and published it in 1976. Also in 1975, the ANSI S Spark study separated the conceptual view of data from physical storage. The article's warning skip the conceptual level, and technical teams end up making business decisions by assumption. Now the uncomfortable part. Practitioners know this and still don't do it. A study of 485 database professionals plus 34 interviews found that fewer than 40% consistently used formal conceptual modeling, even when they believed it could help. 42.1% named lack of time as a barrier. So the article doesn't push a big modeling program. It argues for a minimum viable model. One live business question, a few entities, plain language definitions, checked against real spreadsheets and CRM exports. In its own words, a model earns its place by resolving a decision, not by filling a page. The stakes go up with AI. A request like customers who bought during promotions hides choices. What counts as a purchase, which date defines, during, and whether several orders qualify. Automated analysis won't settle those questions for you. Define the meaning first, then trust the result. That's the review.