Chapter 11:Data Analytics ■Overview Data Warehousing Online Analytical Processing ■Data Mining Database System Concepts-7th Edition 11.2 @Silberschatz,Korth and Sudarshan
Database System Concepts - 7 11.2 ©Silberschatz, Korth and Sudarshan th Edition Chapter 11: Data Analytics ▪ Overview ▪ Data Warehousing ▪ Online Analytical Processing ▪ Data Mining
Overview Data analytics:the processing of data to infer patterns,correlations,or models for prediction Primarily used to make business decisions Per individual customer E.g.,what product to suggest for purchase ·Across all customers E.g.,what products to manufacture/stock,in what quantity Critical for businesses today Database System Concepts-7th Edition 11.3 ©Silberscha乜，Korth and Sudarshan
Database System Concepts - 7 11.3 ©Silberschatz, Korth and Sudarshan th Edition Overview ▪ Data analytics: the processing of data to infer patterns, correlations, or models for prediction ▪ Primarily used to make business decisions • Per individual customer ▪ E.g., what product to suggest for purchase • Across all customers ▪ E.g., what products to manufacture/stock, in what quantity ▪ Critical for businesses today
Overview(Cont.) Common steps in data analytics Gather data from multiple sources into one location Data warehouses also integrated data into common schema Data often needs to be extracted from source formats. transformed to common schema,and loaded into the data warehouse Can be done as ETL (extract-transform-load),or ELT (extract- load-transform) Generate aggregates and reports summarizing data Dashboards showing graphical charts/reports Online analytical processing (OLAP)systems allow interactive querying Statistical analysis using tools such as R/SAS/SPSS Including extensions for parallel processing of big data Build predictive models and use the models for decision making Database System Concepts-7th Edition 11.4 ©Silberscha乜，Korth and Sudarshan
Database System Concepts - 7 11.4 ©Silberschatz, Korth and Sudarshan th Edition Overview (Cont.) ▪ Common steps in data analytics • Gather data from multiple sources into one location ▪ Data warehouses also integrated data into common schema ▪ Data often needs to be extracted from source formats, transformed to common schema, and loaded into the data warehouse • Can be done as ETL (extract-transform-load), or ELT (extractload-transform) • Generate aggregates and reports summarizing data ▪ Dashboards showing graphical charts/reports ▪ Online analytical processing (OLAP) systems allow interactive querying ▪ Statistical analysis using tools such as R/SAS/SPSS • Including extensions for parallel processing of big data • Build predictive models and use the models for decision making
Overview(Cont.) Predictive models are widely used today E.g.,use customer profile features (e.g.income,age,gender, education,employment)and past history of a customer to predict likelihood of default on loan and use prediction to make loan decision E.g.,use past history of sales(by season)to predict future sales And use it to decide what/how much to produce/stock And to target customers Other examples of business decisions: ·Vhat items to stock? What insurance premium to change? To whom to send advertisements? Database System Concepts-7th Edition 11.5 ©Silberscha乜，Korth and Sudarshan
Database System Concepts - 7 11.5 ©Silberschatz, Korth and Sudarshan th Edition Overview (Cont.) ▪ Predictive models are widely used today • E.g., use customer profile features (e.g. income, age, gender, education, employment) and past history of a customer to predict likelihood of default on loan ▪ and use prediction to make loan decision • E.g., use past history of sales (by season) to predict future sales ▪ And use it to decide what/how much to produce/stock ▪ And to target customers ▪ Other examples of business decisions: • What items to stock? • What insurance premium to change? • To whom to send advertisements?
Overview (Cont.) Machine learning techniques are key to finding patterns in data and making predictions Data mining extends techniques developed by machine-learning communities to run them on very large datasets The term business intelligence(BI)is synonym for data analytics The term decision support focuses on reporting and aggregation Database System Concepts-7th Edition 11.6 @Silberschatz,Korth and Sudarshan
Database System Concepts - 7 11.6 ©Silberschatz, Korth and Sudarshan th Edition Overview (Cont.) ▪ Machine learning techniques are key to finding patterns in data and making predictions ▪ Data mining extends techniques developed by machine-learning communities to run them on very large datasets ▪ The term business intelligence (BI) is synonym for data analytics ▪ The term decision support focuses on reporting and aggregation