1. Descriptive Analysis Pattern: This pattern highlights what has happened and helps in understanding the historical data. This includes defining, classifying and illustrating data trends, summaries, and comparisons. Tools such as dashboards, visual analytics, and reporting can be implemented for better understanding and decision making.
2. Diagnostic Analysis Pattern: This pattern deals with finding out why something happened. It involves more in-depth data exploration, data discovery, correlations, and cause-effect analysis. Advanced tools like data mining, correlations, data discovery tools, etc. can be used in this pattern.
3. Predictive Analysis Pattern: With this pattern, future outcomes are forecasted based on historical data patterns and trends. Techniques such as machine learning, regression models, and forecasting models are commonly used in this pattern. The predictive analysis helps in making proactive decisions and risk mitigation.
4. Prescriptive Analysis Pattern: This pattern provides insights on what action to take to eliminate a future problem or take full advantage of a promising trend. Optimization, simulation, decision tree, and rules-based system can be used for this type of analysis. It advises on possible outcomes and businesses can prepare in advance, using this type of analysis.
5. Data Discovery Pattern: This pattern helps in finding hidden patterns and unidentified trends in the data. This pattern may employ data mining, data visualization, and big data analytics tools to enable analysts to find useful insights.
6. Anomaly Detection Pattern: The process of identifying unusual patterns that do not conform to expected behavior, called anomalies. Machine learning algorithms and statistical techniques are used in this pattern to detect outliers or anomalies in the data.
7. Text Mining Pattern: This pattern is focused on extracting valuable information from textual data. It may involve techniques such as natural language processing (NLP), sentiment analysis and text analytics to derive insights from unstructured text data.
8. Social Media Analysis Pattern: This pattern involves analyzing data from social media platforms to gain insights about user behavior, trends, and sentiments. Social network analysis, sentiment analysis, and trend analysis are common techniques used in this pattern.
9. Visual Analytics Pattern: This pattern uses visual representation techniques to present data and insights and make them easily comprehensible. Data visualization tools such as charts, graphs, infographics, and dashboards are used.
10. Geospatial Analytics Pattern: This pattern involves analyzing data based on geographical or spatial factors. Geospatial analysis may include using Geographic Information Systems (GIS) and location intelligence
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