It's always important to take a proactive and step-wise approach to decision-making in order to improve decision effectiveness and maturity.
How to drive information-based decision-making and transform feedback into actionable insights
Analysis: After collection and cleaning, data can be analyzed using descriptive, exploratory, and predictive techniques.
-Descriptive Analysis: Use statistics to organize and summarize data.
-Exploratory Analysis: Look for insights through descriptions of distribution, central tendency, or variability, often using visualizations such as histograms.
-Predictive Analysis: Use machine learning, regression analysis, and classification techniques to predict future outcomes and identify trends, potentially involving data mining.
-Decision Support Systems: Information systems support decision-making, with decision support systems specifically designed for this purpose. These systems analyze data and are increasingly known as business intelligence or business analytics applications. The two main types are model-driven and data-driven systems.
Model-Driven Systems: Apply a preprogrammed model to a limited dataset for "what-if" scenarios. Data-Driven Systems Analyze large data pools in data warehouses to discover patterns through data mining, using statistical models and AI techniques such as neural networks and machine learning.
We live in the digital era with "VUCA" characteristics, decision-making is more science than art. Decision-making is situational; nothing is clear or concise. So it's always important to take a proactive and step-wise approach to decision-making in order to improve decision effectiveness and maturity.
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