<p>Theย twenty-first century is a time of intensifying competition and progressive digitization. Individual employees, managers, and entire organizations are under increasing pressure to succeed. The questions facing us today are: What does success mean? Is success a matter of chance and luck or perh
Business Intelligence and Big Data: Drivers of Organizational Success
โ Scribed by Celina M. Olszak
- Publisher
- CRC Press
- Year
- 2020
- Tongue
- English
- Leaves
- 195
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Theย twenty-first century is a time of intensifying competition and progressive digitization. Individual employees, managers, and entire organizations are under increasing pressure to succeed. The questions facing us today are: What does success mean? Is success a matter of chance and luck or perhaps is success a category that can be planned and properly supported?
Business Intelligence and Big Data: Drivers of Organizational Success examines how the success of an organization largely depends on the ability to anticipate and quickly respond to challenges from the market, customers, and other stakeholders. Success is also associated with the potential to process and analyze a variety of information and the means to use modern information and communication technologies (ICTs). Success also requires creative behaviors and organizational cleverness from an organization.
The book discusses business intelligence (BI) and Big Data (BD) issues in the context of modern management paradigms and organizational success. It presents a theoretically and empirically grounded investigation into BI and BD application in organizations and examines such issues as:
- Analysis and interpretation of the essence of BI and BD
- Decision support
- Potential areas of BI and BD utilization in organizations
- Factors determining success with using BI and BD
- The role of BI and BD in value creation for organizations
- Identifying barriers and constraints related to BI and BD design and implementation
The book presents arguments and evidence confirming that BI and BD may be a trigger for making more effective decisions, improving business processes and business performance, and creating new business. The book proposes a comprehensive framework on how to design and use BI and BD to provide organizational success.
โฆ Table of Contents
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
About the Author
Preface
1. Changing Business Environments and Decision Support Systems
Changing Business Environments
Growing Information Flows
New Organizational Structures and a New Distribution of Decision-Making Powers
New Professions and Fields of Employment
Customers
Competition
Stakeholders
New Conflicts and Threats
Resource-based View
Information and Knowledge โ Organizationโs Strategic Resources
Knowledge Management
Creative Employees and Creative Organizations
Information Communication Technology (ICT) as a Driver of Change and Innovation
Information Communication Technology (ICT) in Business Value Creation
Computer Decision Support Systems
Designing Decision Support Systems
References
2. Business Intelligence in Management of Organizations
The Essence of Business Intelligence
Business Intelligence Generations
Architecture of Business Intelligence Systems
Data Warehouse
Extraction-Transformation-Load
Online Analytical Processing
Data Extraction
Visualization and Dashboards
Models of Business Intelligence Systems
Data Marts
BIโPredictive Analysis
Business Activity Monitoring
Real-Time BI
Corporate BI
BI Portals
BI Networks
BI-Business Performance Management
Development of Business Intelligence Systems
Business Intelligence Competence Center
References
3. Big Data for Business Intelligence
Era of Data-intensive Computing
Big Data Term โ Interpretation
Big Data V Model
Volume
Velocity
Variety
Veracity
Visualization
Variability
Value
Techniques for Big Data Analyzing
Data Mining
Regression Models
Time Series Model
Classification
Association Rules
Clustering
Text Mining
Web Mining
Graph Mining
Network Analysis
Machine Learning
Deep Learning
Neural Networks
Genetic Algorithms
Spatial Analysis
Search-Based Application
Big Data Technologies
NoSQL
Big Table
Apache Cassandra
Google File System
Apache Hadoop
MapReduce
Mashup
Big Data Management
References
4. Analysis of Business Intelligence and Big Data Adoption in Organizations
Areas of Business Intelligence and Big Data Utilization in Organizations
Business Intelligence and Big Data in Relational Marketing
Analysis of Websites
Designing and Analyzing Marketing Campaigns
Recommender Systems
Price Comparison Websites
Group Shopping
Customer Relationship Management Systems
Business Intelligence and Big DataโBased Analyses in Customer Relationship Management
Customer Profitability
Customer Time Value
Customer Segmentation and Profiling
Market Basket Analysis
Customer Loyalty and Migration Analysis
Customer Behavior Analysis
Fraud Detection
Business Intelligence and Big Data in Planning and Budgeting
Business Intelligence and Big Data in Sales and Distribution
Business Intelligence and Big Data in Insurance
Business Intelligence and Big Data in Credit Risk Assessment
Business Intelligence and Big Data in Engineering and Manufacturing
Business Intelligence and Big Data in Telecommunications
Business Intelligence and Big Data in Energy Sector
Business Intelligence and Big Data in the Financial and Banking Sector
Business Intelligence and Big Data in Logistics Industry
Business Intelligence and Big Data in Health Care
Business Intelligence and Big Data in Human Resources Management
References
5. Measurement and Assessment of Business Intelligence and Big Data Development in Organizations
Maturity Models
Maturity Models for Business Intelligence
TDWIโs Business Intelligence Model
Gartnerโs Maturity Model
AMR Researchโs Business Intelligence/Performance Management Maturity Model, version 2
Business Information Maturity Model
Business Intelligence Maturity Hierarchy
Infrastructure Optimization Maturity Model
Business Intelligence Development Model
Lauder of Business Intelligence
Hewlett Package Business Intelligence Maturity Model
Information Evaluation Model SAS
Watson Model
The Teradata Maturity Model
Business Intelligence Maturity Models Based on Analytical Capabilities
Model Proposed by Davenport and Harris
Model Based on Dynamic Capabilities Business Intelligence
Big Data Maturity Models
The TDWI Big Data Maturity Model
Big Data and Analytics Maturity Model (IBM model)
Dell Data Maturity Model
Big Data Business Model Maturity Index
The Hortonworks Big Data Maturity Model
Critical Success Factors for Implementing Business Intelligence and Big Data in Organizations
CSFs for Business Intelligence and Big Data Use
Big Data-Based Business Value Creation
Conclusions
References
Index
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