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Historically, these questions are answered by analysts who collect and clean new datasets and aggregate them up in some bespoke way. It usually starts with a looser question-“was our Super Bowl commercial worth it?”-that can’t easily be answered with off-the-shelf metrics. The second method of analysis is less structured. You can’t fully improvise in a BI tool, and that’s the point-to let people ask questions, but make sure that whenever they make a chart of their company’s revenue, they’re using the correct formula for calculating it. Though people can be combinatorially creative in how they complete their sentences, this approach puts guardrails around the sorts of questions that they can ask. The former defines the structure of Mad Lib sentences and the words that people can choose to fill them in the latter lets people fill them in and gives them tables and charts of their results. And second, there’s an interface where people choose which metrics they want to see and how they want to aggregate, filter, and combine them. First, there’s some sort of data model in which people define metrics, how they should be calculated, and the ways they can be aggregated, filtered, and combined. Most BI tools support this with more or less the same architecture. These questions are often answered using BI tools. And that will prompt more questions of the same style-“now show me total orders in Massachusetts by month and by product category ”-until people find whatever they’re looking for. This question will reveal something, like a spike in new orders in February. “Show me total orders in Massachusetts by month compared to the same number from a year ago, ” someone might ask. In business contexts, the first type of analysis typically consists of asking a series of Mad Lib-style questions about known metrics, like revenue retention or daily active users or ad spend, and aggregating and filtering those metrics by different dimensions. Rather than looking for new ways to assess a question, they start by asking, “how do we currently measure that?” work like scientists, creating new datasets and aggregating them in novel ways to draw conclusions about specific, nuanced hypotheses. work like journalists, collating existing metrics and drawing conclusions by considering them in their totality. There are, very roughly, two ways to analyze data : 1