Business Intelligence and Data Science Overview

Business Intelligence, Analytics, and Data Science

• Business intelligence (BI) was previously everything related to using data for managerial decision support
• Now, it's a part of business analytics
• BI is descriptive analytics
• Descriptive analytics answers "what happened?" and "what is happening?"
• Predictive analytics answers "what will happen?" and "why will it happen?"
• Prescriptive analytics answers "what should I do?" and "why should I do it?"
• Business analytics uses BI, dashboards, scorecards, data warehousing and well-defined business problems/opportunities, to support Business Intelligence

Data Warehousing

• A data warehouse is a physical repository for relational data, specially organized for enterprise-wide, cleansed data
• Standardized formats
• Central repository of information analyzed for informed decisions
• Data flows into the data warehouse from transactional systems, relational databases, and other sources
• Typically on a regular pace
• The data warehouse is a collection of integrated, subject-oriented databases designed to support decision support systems (DSS) functions
• Each unit of data is non-volatile and relevant to a specific moment in time

Relational Databases

• A relational database is a collection of information organized into predefined relationships
• Data is stored in tables with columns and rows
• Relationships between tables allow analysis of how different data structures relate to each other.

Data Warehouse Characteristics

• Subject-oriented: Data is organized by subject (e.g., sales, products, customers)
• Integrated: Data from different sources is integrated, resolving naming conflicts and discrepancies
• Time-variant: Contains historical data (e.g., daily, weekly, monthly, annually) for analysis of trends, deviations, long-term relationships, and forecasting
• Non-volatile: Data cannot be changed; changes are recorded as new data, rather than updating the original data
• Metadata: Data about data, describing how data is organized
• Web based, relational/multi-dimensional client/server, real-time/right-time/active: Ensures easy access to end-users

Data Mart

• A smaller, departmental data warehouse
• Stores only limited/relevant data
• Dependent data mart: Created directly from an existing data warehouse; all users view the same data version
• Independent data mart: Lower cost, scaled-down version of a data warehouse for a strategic business unit or department; its source is not the enterprise warehouse

Operational Data Stores (ODS)

• A type of database used as an interim/short-term storage area for a data warehouse
• Provides a fairly recent form of customer information
• Data is updated throughout the course of business operations
• Used for short-term decisions
• Classified as a short-term memory that stores very recent data; the data warehouse stores permanent data

Multidimensionality

• Dimensions are perspectives/entities about which an organization keeps records (e.g., time, location, item type)
• A 2D representation of data with two dimensions (organized in quarters and item) (e.g., sales of various items in a city per quarter)
• A 3D representation where a third dimension (organization location) is added (e.g., items sold per quarter per city)

DW Architecture

• Three-tier architecture: Data acquisition software (back-end), data warehouse, client software (front-end)
• Two-tier architecture: Combines the first two tiers in a three-tier architecture
• One-tier architecture: Only one single tier physically accessible

ETL (Extract, Transform, Load) Processes

• Processes to integrate data from various business systems
• Key tasks include data extraction, transformation, and loading
• Uses Enterprise Application Integration (EAI)
• Creates a process to deliver data to the data warehouse, to create a near-real-time data warehouse or to the OLTP systems

Metadata

• Data about data, describing aspects like its contents and acquisition
• Acts as a data dictionary for the warehouse

Data Warehouse Development

• A major project that influences various organizational units/departments
• Has direct benefits (enhanced analysis capabilities, consolidated data views, enhanced business performance, simplified data access); and indirect benefits (enhanced business knowledge, competitive advantage, improve customer service, facilitate decision-making)

Factors Affecting Architecture Decisions

• Information interdependence between units
• Upper management needs
• Urgency for a warehouse
• User tasks
• Resource constraints
• Strategic view of the data warehouse
• Compatibility with existing systems
• Ability of in-house IT staff
• Technical issues
• Social/political factors