Business Analytics
Turning data into decisions — from understanding what happened to figuring out what to do about it.
Business analytics is the practice of using data to understand performance, diagnose problems, forecast outcomes, and inform decisions. It spans four types of questions: what happened (descriptive), why it happened (diagnostic), what may happen next (predictive), and what action should follow (prescriptive). Most organizations operate primarily in the first two. The shift toward predictive and prescriptive analytics — where AI plays a larger role — is where business analytics and AI strategy begin to overlap.
Data investment produces value only when it changes decisions. Organizations can build sophisticated dashboards that get read, discussed, and filed without changing a single choice. The question that separates useful analytics from expensive noise is: what would we do differently if we didn't have this information? If the answer is "nothing," the analytics isn't connected to decision-making. This is also why AI opportunities need to be evaluated on decision impact, not technical sophistication.
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Data Science
The discipline of turning messy data into decisions — combining statistics, code, and domain knowledge to find patterns that matter.
Data and AnalyticsPredictive Analytics
Using historical data to estimate what's likely to happen next — not a crystal ball, but a way to act on probability rather than intuition.
Business StrategyDecision Intelligence
Treating decisions as something that can be designed, measured, and improved — not just made.
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Business Analytics
Turning data into decisions — from understanding what happened to figuring out what to do about it.
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