Business Intelligence Chapter 3

Business Intelligence, Analytics, and Data Science: A Managerial Perspective

• Chapter 3 focuses on descriptive analytics, business intelligence, and data warehousing.
• Business intelligence (BI) used to encompass all data-driven decision support; now it's a part of business analytics, specifically descriptive analytics.
• BI is now defined as descriptive analytics.
• Descriptive analytics questions what happened and what is happening.
• Enables include reporting, dashboards, scorecards, and data warehousing.
• Outcomes consist of well defined business problems and opportunities along with business intelligence.
• Predictive analytics addresses what will happen and why it will happen.
• Prescriptive analytics tackles what should be done and why.
• Advanced analytics encompasses optimization, simulation, decision modeling, and expert systems.
• A data warehouse is centralized information that enhances informed decisions.
• Data flow from transactional systems, relational databases, and other sources is typical.
• Data warehouses must have integrated data in standardized format, and be cleansed for enterprise-wide use.
• A relational database organizes data into predefined relationships within tables (or relations).
• Data in a relational database is organized in columns and rows and makes connections between data structures easy to view and understand.
• Data warehouses are subject-oriented, integrated, time-variant, and nonvolatile.
• Data in a data warehouse is not updated; changes are stored as new data.
• Data warehouses do not go through data quality processes such as cleansing and de-duplication.
• Meta-data includes data about the data itself outlining how the data is organized.
• Data warehouses allow easy access to end-users through techniques such as web-based, relational/multi-dimensional, and client/server architecture.
• Data marts are departmental small-scale data warehouses containing only limited or relevant data (e.g. marketing, operations).
• Dependent data marts are subsets directly created from data warehouses.
• Independent data marts are alternative lower-cost scaled-down versions of data warehouses.
• An independent data mart is designed for strategic business units or departments and its source is not the Enterprise Data Warehouse.
• Operational data stores (ODS) are temporary, provisional areas of data warehouses used for short-term decisions, storing recent information in a short-term memory role, different from the long-term memory function of a data warehouse.
• ODS consolidates current volatile data from multiple sources.
• Operational data marts are created when operational data needs multidimensional analysis.
• In multidimensional analysis, data is organized by entities and dimensions.
• Time, location and item type are presented in a multidimensional view.
• A three-tier data warehouse architecture is comprised of the data acquisition layer, the data warehouse that holds the data, and the client software application tier.
• A two-tier architecture combines the data acquisition and data warehouse layer into one.
• A single-tier architecture presents the data warehouse as a virtual entity.
• ETL (Extract, Transform, Load) is a process comprised of data access, data federation, and change capture to create integrated enterprise data.
• ETL processes data from various sources for integration into a data warehouse.
• ETL uses several technologies, including Enterprise application integration (EAI), Service-Oriented Architecture (SOA), and near-real-time data delivery.
• Data warehousing comprises operational data stores (ODS), Operational data marts, Dependent & Independent Data Marts.
• Metadata stores information about the data.
• Ten factors influencing architecture selection relate to information interdependence, management information needs, urgency for a data warehouse, and end-user tasks and resource constraints with additional variables such as strategic view of the data warehouse, compatibility with current systems, ability of in-house IT staff, technical issues, and social and political factors.

Data Warehouse Development

• Data warehouse development is complex.
• Data warehousing affects multiple departments and includes many input and output interfaces.
• Direct benefits include end-user analysis capabilities, consolidated corporate data views, enhanced business performance, and simplified data access.
• Indirect benefits include enhanced business knowledge, gained competitive advantage, improved customer service and satisfaction, and assistance in reforming business processes.

ETL (Extract, Transform, Load)

• Includes extract, transform, and load processes.
• ETL tools face challenges such as expensive data transformation tools and a long learning curve.
• Important selection criteria include the ability to access various data sources, automatic metadata and open standard conformity, ease of user interaction.

Other DW Components

• Enterprise data warehouse (EDW) is large scale for enterprise decision support.

Metadata

• Metadata describes the contents of a data warehouse and its acquisition and use. (Data Dictionary)

Web-Based DW Architecture

• Internet, intranet and extranet are used for client server interaction and access.