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In life sciences, simple statistical software can analyze patient data. These traditional tools are often more than sufficient for addressing the bread-and-butter analytics needs of most businesses. While this process is complex and data-intensive, it relies on structured data and established statistical methods.
Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptiveanalytics: Assessing historical trends, such as sales and revenue. Predictive analytics: Forecasting likely outcomes based on patterns and trends to facilitate proactive decision-making.
If we dig deeper, we find that two factors are really at work: Causal data versus correlated dataData maturity as it relates to business outcomes. One of the most fundamental tenets of statistical methods in the last century has focused on correlation to determine causation.
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