Study Notes for Business Intelligence and Business Intelligent Systems (BIS)

UNIVERSITY OF NATIONAL AND WORLD ECONOMY

The Spirit Makes The Power

BUSINESS INTELLIGENCE SYSTEMS

Informatics 2021/2022

Introduction to Business Intelligence Systems (BIS)

  • Business Intelligence Systems (BIS) are developed based on the concept of "business intelligence".

  • The concept of business intelligence was established in the 1990s; however, its origins date back to the emergence of management information systems (MIS) in the 1960s.

  • In 1989, Howard Dresner from the Gartner Group defined business intelligence as:

“A set of concepts and methods to improve management decision-making processes through the use of information systems that work with real business data.”

Historical Context and Development of BIS

Early Development

  • The development of BIS began in the 1990s. Initially, it was solely focused on customer information but has since expanded to cover various aspects of business.

Main Components

  1. Data Integration:

    • Combining data from different sources and formats to ensure coherent access.

  2. Data Analysis and Visualization:

    • Providing techniques for analyzing and visualizing information in user-friendly formats.

Activities and Functions of BIS

  • BIS extract information from various sources, including:

    • Customer Relationship Management (CRM) systems

    • Supply Chain Management (SCM) systems

    • Enterprise Resource Planning (ERP) systems

  • Centralization and organization of information in data warehouses or other data extracts, including processes for:

    • Cleaning data

    • Supplementing data with necessary information

  • Provision of analytical tools allowing business and technical professionals to:

    • Search for information

    • Discover patterns

    • Solve problems

  • Designing BIS involves a "Road map" that details the steps for project implementation.

Definition and Essence of BIS

  • The essence of BIS is the complete utilization of data generated within the enterprise. This is achieved by:

    • Converting data into information and knowledge that supports management decision-making processes.

    • Enabling appropriate action to enhance organizational competitiveness.

  • BIS encompasses the processes, tools, and technologies used to convert data into usable information for decision-making.

Business Intelligence Architecture

Data Refinery Concept

  1. Turning Data into Information

  2. Turning Information into Knowledge

  3. Turning Knowledge into Rules

  4. Turning Rules into Plans and Actions

  5. Feedback Loop

Components of BIS Architecture

  • BIS architecture includes two environments:

    • Data Warehousing Environment:

    • Comprises data sources (operating systems, external sources), and ETL processes (Extraction, Transformation, Load).

    • Analytical Environment:

    • Involves methods for analyzing data in warehouses, Business Performance Management (BPM), and user interfaces for visual presentation.

Data Sources

Internal and External Sources

  • Internal Sources:

    • Operating systems, reports, and correspondence.

  • External Sources:

    • Research, trends, and competitive information.

  • Online Transaction Processing (OLTP) systems contain daily operational data and represent organizational history.

Importance of External Data

  • External data is vital at the highest management levels, aiding in the development and monitoring of key organizational indicators and forecasting.

Data Warehouse

Definition and Structure

  • A Data Warehouse (DW) is a specialized database designed to support decision-making processes.

  • An organization’s historical data is organized in relational or multidimensional structures, facilitating analysis and real-time access.

ETL (Extraction, Transformation, Load) Processes

Role in Building Data Warehouses

  • DW is constructed using methodologies that primarily involve metadata and ETL processes.

  • ETL Processes:

    • Retrieve data from operational systems

    • Clear and transform data into a suitable analysis form

    • Load the transformed data into the data warehouse

  • Challenges include dealing with unclean and incompatible operational system data.

Data Marts

Definition and Function

  • Data marts are smaller data structures built alongside data warehouses.

  • They typically contain data relevant to specific organizational units (e.g., departments) and support various types of analysis.

Analytical Environment and Tools

Users and Complexities

  • The analytical environment consists of business users employing different analytical tools to study data from the warehouse.

  • Tools differ regarding complexity, analysis capabilities, and user frequency.

Analytical Pyramid

  • Business Intelligence is represented as a pyramid with the Data Warehouse at the base and three analytical layers:

    1. Queries & Reports (Q&R)

    2. Online Analytical Processing (OLAP)

    3. Data Mining (DM)

Queries and Reports

Description and Use

  • The simplest analytical tools that generate reports answering user inquiries.

  • Reports can be standard (pre-set) or specialized based on specific parameters set by users.

Online Analytical Processing - OLAP

Description and Functionality

  • OLAP tools provide advanced analytical capabilities, allowing users to interactively analyze multidimensional data structures.

  • These tools are primarily used by middle managers for detailed data analysis.

Data Mining

Description and Importance

  • Data mining represents the highest complexity level of analytical tools.

  • This process involves researching large data volumes to identify patterns, formulate rules, and gain insights to support management decisions.

  • Effective data mining requires comprehensive knowledge of organizational activities and active user participation.

Information Visualization

Purpose and Tools

  • Visual representation of information and knowledge gained from analyses aids in rapid decision-making and corporate governance.

  • Common visualization tools include tables, graphs, charts, and interactive dashboards which illustrate business performance against objectives.

Business Performance Management (BPM)

Definition and Functions

  • BPM focuses on systematically improving and controlling an organization's performance.

  • It comprises a collection of methodologies, metrics, processes, and systems that monitor and manage enterprise performance.

Main Features of BPM

  1. Process Orientation

  2. Goal and Indicator Focus

  3. Methodological Framework

  4. IT Tools

Methodologies for Business Performance Management

Overview

  • Various methodologies exist to unify enterprise planning and implementation, bridging strategic and tactical goals. Popular methodologies include:

    • Six Sigma

    • Activity Based Costing

    • Total Quality Management

    • Economic Value Added

    • Balanced Scorecard System

Key Performance Indicators (KPIs)

Definition

  • KPIs measure how well an organization or individual performs critical activities essential for success.

  • They provide necessary information for informed management decisions and act as warning signs for potential organizational issues.

Visualization of KPIs

  • KPIs are typically showcased on interactive dashboards, scorecards, or reports for user accessibility and clarity.

Interactive Dashboards

Definition and Function

  • Dashboards serve as user interfaces that organize and present essential information understandably, focusing on key performance indicators across various business processes.

Designing Business Intelligence Systems (BIS)

Guidelines

  • BIS processes evolve traditional IT solutions to provide targeted information for management needs.

  • Transforming data into actionable information requires the application of flexible analytical tools and specific context.

Objectives

  • BIS supports decision-making across organizational levels, enabling real-time notifications, detailed reports, and scenario analysis for strategic improvements.

  • A unified BIS enhances business transparency and data quality for sustainable development.

Design and Implementation Stages of BIS

Justification Stage

  • Assess the business problem/opportunity to propose an appropriate BIS solution, including cost-benefit analysis.

Planning Stage

  • Ensure a unified information environment that encompasses the entire enterprise’s decision-making processes (includes both technical and non-technical infrastructure considerations).

  • Project planning must account for dynamism and potential changes.

Business Analysis Stage

  • In-depth comprehension of business requirements is critical through extensive analysis.

Design Stage

  • Creation of proposals to adequately address identified business problems/opportunities.

Construction Stage

  • Implementation of the solution ensuring return on investment is monitored.

Commissioning Stage 5

  • Evaluate the effectiveness of the implemented solution, considering the outcomes.

Roadmap for BIS Development

Phases

  1. Justification: Define business conditions and costs vs. benefits for BIS implementation.

  2. Planning: Establish robust enterprise infrastructure and dynamic project plans to ensure adaptability.

  3. Business Analysis: Determine scope and conduct data quality assessments. Ensure the thorough analysis of metadata requirements.

  4. Design: Develop metadata storage, design databases, ETL processes while considering accessibility.

  5. Construction: Undertake the development of ETL processes and applications, extracting data patterns for enhanced insights.

  6. Commissioning: Implement and test all components, alongside user training to operationalize the BIS effectively.

Agile BI Development

Flexible Approaches

  • The need for timely adaptations has led to Agile BI development methodologies, characterized by repetitive cycles of planning, analysis, design, development, testing, and maintenance.

  • This approach allows for iterative enhancements and a focus on small-scale developments within shorter timeframes.

Future Trends in BIS Development

Key Trends

  • Shift towards SaaS-based BI and analytics platforms.

  • Development of innovative data preparation tools encouraging user self-service and enhanced data discovery.

  • Growth in Mobile BI maturity and significance.

  • Increasing relevance of Cloud BI infrastructure.

  • Addressing challenges posed by Big Data.

Conclusion

  • Thank you for your attention!