Dwshort
Data Warehouse: centralized repository storing integrated and historical data for analysis and decision–
Making; Characteristics of Data Warehouse: subject-oriented, integrated, time–
Variant, non-volatile; OLAP: technology used for fast multidimensional data analysis; OLAP Operations: slice (single dimension), dice (multiple dimensions), drill-down (detailed view), roll-up (summary view); Data Cube: multidimensional structure representing data across dimensions like time, product, and location; Star Schema: data warehouse model with a central fact table connected to multiple dimension tables.
Business Intelligence (BI): process of collecting, analyzing, and presenting data for decision-making; Leveraging Data in BI: using data and knowledge to improve business performance and gain insights; BI Components: data sources, data warehouse, and reporting/visualization tools; BI Dimensions: perspectives like time, product, and region used for analysis; Information Hierarchy: data → information → knowledge → wisdom; BI vs Business Analytics: BI focuses on reporting and past data, while analytics focuses on prediction and future outcomes; BI Life Cycle: stages include data collection, integration, storage, analysis, and decision-making; Data Issues: problems like missing, noisy, duplicate, and inconsistent data; Data Quality: ensures accuracy, completeness, consistency, and reliability of data for correct decisions.
BI Implementation: process of applying BI systems using business goals and data strategies; Key Drivers: factors like competition, data availability, and need for better decisions that motivate BI adoption; KPI (Key Performance Indicators): measurable values used to evaluate business success (e.G., sales growth); Performance Metrics: quantitative measures to track performance (e.G., profit, revenue); BI Architecture/Framework: structure showing data flow from sources → data warehouse → analysis → reporting tools; Best Practices: proper data governance, data quality maintenance, and effective tool usage; Business Decision Making: using BI insights to make strategic and operational decisions; Styles of BI: reports, dashboards, and ad-hoc queries; Event-Driven Alerts: automatic notifications triggered by specific conditions in data; Intelligence Creation Cycle: continuous process of data collection, analysis, and action; Value of BI: improves performance through value-driven decisions and effective information use.
Advanced BI: use of modern technologies to enhance data analysis and decision-making; Big Data: large, complex, and high-volume data that requires advanced tools for processing and analysis; Big Data in BI: integration of big data technologies to improve insights and real-time analytics; Social Networks: platforms generating user data used for analyzing trends, behavior, and sentiments; Mobile BI: access to BI tools, reports, and dashboards through mobile devices for real-time decisions; Emerging Trends: include AI integration, real-time analytics, cloud BI, and self-service BI; Pentaho: open-source BI tool used for data integration, reporting, and analytics; KNIME: data analytics platform used for data processing, machine learning, and visualization.
BI Integration Implementation: process of combining different BI tools, databases, and systems for unified analysis; Connecting in BI Systems: linking data sources, data warehouses, and applications for seamless data flow; Legality Issues: compliance with laws and regulations related to data usage and storage; Privacy: protection of personal and sensitive data from unauthorized access; Ethics: responsible and fair use of data without misuse or bias; Social Networking and BI: use of social media data for analysis while ensuring privacy, security, and ethical considerations.