Database Reference
In-Depth Information
Figure 1.8 Comparing BI with Data Science
One way to evaluate the type of analysis being performed is to examine the time
horizon and the kind of analytical approaches being used. BI tends to provide
reports, dashboards, and queries on business questions for the current period or
in the past. BI systems make it easy to answer questions related to quarter-to-date
revenue, progress toward quarterly targets, and understand how much of a given
product was sold in a prior quarter or year. These questions tend to be
closed-ended and explain current or past behavior, typically by aggregating
historical data and grouping it in some way. BI provides hindsight and some
insight and generally answers questions related to “when” and “where” events
occurred.
By comparison, Data Science tends to use disaggregated data in a more
forward-looking, exploratory way, focusing on analyzing the present and enabling
informed decisions about the future. Rather than aggregating historical data to
look at how many of a given product sold in the previous quarter, a team may
employ Data Science techniques such as time series analysis, further discussed
in Chapter 8, “Advanced Analytical Theory and Methods: Time Series Analysis,”
to forecast future product sales and revenue more accurately than extending a
simple trend line. In addition, Data Science tends to be more exploratory in nature
and may use scenario optimization to deal with more open-ended questions. This
approach provides insight into current activity and foresight into future events,
while generally focusing on questions related to “how” and “why” events occur.
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