Data Analytics for Business
Foundations and Industry Applications
Fenio Annansingh, Joseph Bon Sesay
- 276 páginas
- English
- ePUB (apto para móviles)
- Disponible en iOS y Android
Data Analytics for Business
Foundations and Industry Applications
Fenio Annansingh, Joseph Bon Sesay
Información del libro
Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results.
The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor's manual are also provided as online supplements.
This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas.
Preguntas frecuentes
Información
1 History and Evolution of Data Analytics
LEARNING OBJECTIVES:
- Understand the history and evolution of data analytics
- Identify the difference between data, information, knowledge, intelligence, and wisdom
- Define the different types of knowledge
- Identify the different categories of data
- Explain data science and its components
- Discuss the phases of data science
History and Evolution of Data Analytics
Data
When Does Data Become Information?
Data
Example Looking at the examples given for data:
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Information
Long Description for Figure 1.1
Knowledge
- Explicit Knowledge is the knowledge that is formalised and codified and is sometimes referred to as know-what. Explicit knowledge is relatively easy to identify, store, and retrieve. It is similar to information and involves management ensuring that employees have access to what they need. Explicit knowledge in the organisation should be stored, reviewed, updated, or destroyed.
- Tacit Knowledge is difficult to pass from one person to another. It is knowing how to do something. Tacit knowledge is intuitive, which is hard to define and is mainly experience-based. It is also considered the most valuable source of knowledge and the most likely to lead to innovation and creativity. Tacit knowledge is used to provide companies with a competitive advantage when applied to business goals. It is also used to create or increase value for the company.
- Implicit Knowledge is unconscious knowledge that is it lies outside of the individual’s awareness. Implicit knowledge is difficult to verbalise and can only be inferred from behaviour. It is difficult to transfer. It emerges from task performance. Once the employee knows and understands the explicit knowledge as indicated by a manual or supervisor, it leads to more profound knowledge. An individual who implicitly understands the reason behind a specific set of instructions will better appreciate why a particular course of action occurs. This allows for collaboration and leads to new and improved ways for companies to operate.
Relationship Between Data, Information, and Knowledge
Data + Meaning = Information
Example 1 of data: Looking at the examples given for data:
Only when assigned a context or meaning, does the data become information. It all becomes meaningful when we are told:
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