It is the process of collecting, storing, and managing data from various sources in a centralized repository. This allows organizations to consolidate their data, making it easier to access and analyze.

• Centralized Storage: Data from multiple sources, such as transactional databases, CRM systems, and external data feeds, is integrated into a single location. This centralization simplifies data management and retrieval.
• Historical Data Analysis: Data warehouses store historical data, enabling organizations to analyze trends over time. This is crucial for understanding long-term patterns and making strategic decisions.
• Data Quality and Consistency: It  involves processes for cleaning and transforming data, ensuring the information is accurate, consistent, and reliable for analysis.

It is the graphical representation of data and information. It involves using visual elements like charts, graphs, and dashboards to present complex data in an easily understandable format.

 It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events. This component of BI helps organizations anticipate trends and outcomes.

• Statistical Modeling: Predictive analytics relies on statistical models to identify relationships within data. These models can be used to forecast future behavior based on historical patterns.
• Machine Learning Algorithms: Advanced predictive analytics often employs machine learning algorithms that can learn from data and improve their accuracy over time.
• Scenario Analysis: Organizations can use predictive analytics to run various scenarios, assessing the potential impact of different decisions and strategies.