Introduction to Business Intelligence (OSCM 603)

Introduction to Business Intelligence (OSCM 603)

• Business intelligence (BI) involves methods, processes, architectures, applications, and technologies to gather and transform raw data into meaningful and useful information to support strategic, tactical, and operational insights for business operations and growth.
• BI encompasses the evolution of "business insight systems," progressing from executive information systems (EIS) and decision support systems (DSS) in the 1980s, to data warehousing (DW) and business intelligence (BI) in the 1990s.
• The early 2000s saw the development of dashboards, scorecards, and performance management tools.
• Modern BI includes big data, mobile BI, and personal BI.

Overview of Business Intelligence

• BI involves the process, components, applications, technologies used for business intelligence.
• BI includes different types of data (numeric and textual data).
• The BI industry, market, and career perspectives are also part of the overview.

Data and Analytics

• Data is raw value elements or facts.
• Information is the result of collecting and organizing data in context.
• Knowledge is the understanding of information to derive useful and actionable insights.
• Wisdom is the understanding of what is best in decision-making.
• Big Data involves voluminous amounts of structured and unstructured data (text, numeric, imagery, etc.)
• Analytics involves analyzing data to gain insights.

Evolution of Business Intelligence

• The "perfect business insight system" evolved over time, progressively incorporating new capabilities.
• Increase in sophistication of computational and analytical needs, coupled with advancements in computer hardware and software, aided in this evolution.
• Key milestones include 1980s (EIS/DSS), 1990s (DW/BI), 2000s (dashboards), and 2010+ (big data).

Types of Data

• Structured data: data that is organized in a predefined structure (e.g., databases).
• Unstructured data: Data without a predefined structure (e.g., images, text files).
• Numeric data: Data expressed in numerical form (e.g., numbers).
• Textual data: Data expressed in text or natural language (e.g., descriptions).
• Internal data: Data stored within an organization's systems.
• External data: Data from sources outside the organization.

Data Processing Types

• Transactional processing: Processes routine data modifications (insertions, modifications, deletions, and transmissions).
• Analytical processing: Processes data for reporting, analysis, transformation, and decision support (e.g. CRM).

Using Business Intelligence

• The ultimate use of gathered and processed data is for business intelligence.
• Processes involve creating data warehouses/marts.
• Utilizing front-end analytical tools (e.g., Oracle Sales Analyzer, Micro Strategy).
• Tools allow end-users to access the data, make queries, create reports, examine scenarios.

How Business Intelligence Works

• BI starts with raw data stored in corporate databases.
• Data about various business aspects (e.g., inventory, customer info, sales) are usually scattered across multiple systems.
• BI software brings together this dispersed information using a data warehouse/mart, forming linked tables and data cubes.
• Data cubes enable deep analysis of various business aspects (e.g., inventory and sales).

Business Intelligence vs. Data Science

• BI focuses on extracting meaningful data from organized data, enabling informed decision-making.
• Data science utilizes various algorithms to discover information through deep analytical processes.

BI System Capabilities

• Key components include Presentation, Insight Creation, Information Integration, and Organizational Memory.

Business Intelligence Process

• The process involves gathering data, applying analytical components (e.g. OLAP, data mining), and producing actionable knowledge.
• The process involves analyzing data and transforming it into meaningful information.

BI Systems Architecture

• A typical BI architecture comprises operational systems, data warehouses, and external data sources, linked by ETL tools.
• Activities include extracting, transforming, and loading (ETL) data.

BI System Values

• BI systems enable integrated data processing.
• This system allows easy access to various types of information (raw data, analysis results, and metrics).
• BI systems streamline the process of well-managed, controlled data-driven decision-making.

Components of a BI System

• Data sources
• Data warehouses/marts
• BI methodologies

BI System Architecture

• Operational systems are a crucial part of the BI architecture and feed the data warehouse.
• External data sources further enrich the data pool with industry, market, or economic trends.
• ETL tools play a vital role in integrating and transforming data from diverse sources.

BI Applications

• A variety of applications are employed using BI dashboards, including performance management, strategy management, benchmarking, CRM (customer relationship management), and web site/analytics.

BI Users

• Different types of BI users exist, from executive-level decision-makers to technical data analysts.
• Data analysis, information discovery, and insightful decision-making are all impacted by user roles and responsibilities.
• Understanding the diverse needs of various users is paramount for designing and developing effective BI systems.

BI Tools and Frameworks

• Reporting tools (operational reports, dashboards, ad-hoc reports)
• Data analysis/mining tools
• Analytical tools (e.g. OLAP)
• Multidimensional databases

Benefits of Implementing a BI System

• Enables informed decision-making by providing data-driven insights and more accurate conclusions.
• Facilitates identification and development of alternatives leading to well-reasoned decisions and effective actions.

BI Market

• BI market trends identify and indicate trends in adoption.
• BI market share reflects market share in specific regions/countries/industries.
• BI market growth figures provide useful insight into the market's expansion and projected future business.

BI Careers/Positions

• BI solution architects (integration specialists)
• Business and analytics specialists
• BI application developers and testers
• Data warehouse specialists
• Database specialists

Critical Skills Needed for BI

• Solid understanding of database systems & data warehousing
• Efficient data management integration
• Creation of complex reports
• Expertise in data policies/regulations
• Developing & managing business solutions

Resources for Learning More About BI

• Organizations/Institutes (e.g. The Data Warehousing Institute)
• Web sites/resources (relevant web pages, etc.)
• Industry expert information

Assignments

• Data warehousing
• Data Analytics
• Data mining
• Data science

Next Week's Topic

• Data warehousing