In business intelligence, decision-making is categorized into three major ways, which include unstructured, semi-structured, and structured. Structured decisions entail an equivocal approach to handling decisions to avoid misinterpreting them as contemporary consistently. Unstructured decisions involve decisions in which the administrator must assess and provide an opinion on the matter (Halim, Mubarokah, & Hidayanto, 2020). Lastly, semi-structured decisions revolve around the problem and provide a bold solution supported by specific procedures.
Writing a dissertation chapter requires careful planning, research, and organization. Each chapter should contribute to your overall research and thesis. Here’s a step-by-step guide for writing a dissertation chapter:
Chapter Title: Begin by giving your chapter a descriptive title that reflects its content.
- Start with an introduction to the chapter’s topic.
- Clearly state the chapter’s specific objectives and its relevance to the overall research.
- Provide an overview of what the reader can expect in the chapter.
- Review and summarize relevant literature, theories, and prior research related to the chapter’s topic.
- Identify gaps or areas where your research contributes to the existing body of knowledge.
- Provide a conceptual framework for your research within this chapter.
- Explain the research methods and approaches you used in this chapter.
- Describe your data collection procedures, including any experiments, surveys, interviews, or analysis.
- Discuss the research design and justify why it’s appropriate for your research objectives.
- If relevant, address any ethical considerations
Title: “Unlocking Business Potential: An Examination of Business Intelligence in the Modern Enterprise”
- Provide a concise summary of the term paper’s objectives, methodology, and key findings.
- Introduce the concept of Business Intelligence and its relevance in today’s business environment.
- State the objectives of the term paper and the significance of studying BI.
- Provide an overview of the organization of the term paper.
Chapter 1: Understanding Business Intelligence:
- Define Business Intelligence and its core concepts.
- Explain the importance of data in decision-making and problem-solving.
- Introduce the historical development of BI
Business intelligence assists in data control, particularly prediction and decision-making, while data warehousing supports data storage. Operations held in data warehousing aid in appropriately storing information. Such operations include extracting, transforming, and loading data. All these operations help in maintaining the confidentiality of information stored in the warehouse.
Three types of data that might be controlled in the warehouse include metadata, historical data, and derived data. Metadata involves information that defines it and schema objects. Moreover, metadata is utilized by various applications to obtain and compute data accurately. Normally, a data warehouse is supposed to store historical data safely for a couple of years. Furthermore, the amount of data made available is based on the size of the disk used and the type of assessment done. Such information could be gathered from different sources or the database comprising archives. Finally, derived data is obtained by translating the current data or applying arithmetic computations. Derived data can be established as a constituent of the database control process or run-time in the event of a dispute.
Five major tables that might be involved in the data warehouse for oncology patients include:
- Symptom dimension
- Disease stage symptom
- Disease type
- Treatment dimension
- Patient dimension
Indexing is a data structure procedure for faster and more efficiently obtaining records from a database file. An index is defined as a short table comprising two columns only. The first column supports the candidate or primary key of a table, while the other column involves a group of pointers that stores the block disk location, where specific fundamental values are maintained. Indexing types include clustering indexing, primary indexing, and secondary indexing.
Index properties entail matters characteristic of engineering soil properties but are not quite critical for geotechnical engineers. Additionally, index properties involve features that ease the grouping and categorization of soils for engineering rationales. The shape of soil particles determines how efficiently particles can be packed concurrently. Soil density, such as coarse-grained soil, indicates solidity and depth. The type of soil that constitutes a wide variety of particle capacities is considered well-graded. In contrast, the other classification of soil comprises a high level of fragment amounts, termed as poorly graded.
The three widely recognized kinds of index properties include:
- Soil grain index properties
- Non-cohesive( coarse-grained) soil index properties
- Particle shape
- Clay and clay minerals constituents
- Particle-size distribution
- Relative density
- Cohesive (fine-grained) soil index properties
- Water content
- Clay and clay mineral constituents
Null values are essential because their values vary from areas that support places or vary from zero values. Zero is itself a value and carries a value. The null value is an explicit marker that implies that there is absence of value in a certain situation (Gravelle, 2020). Therefore, it can be concluded that the null value is sound and differs from the value of a zero. Otherwise stated, null values are usually used to indicate that there might be a value that is not identifiable. Null values are used as placeholders until an administrator obtains data sufficient to fill a table entry using a valid value (Gravelle, 2020). Furthermore, null values should not be replaced with blank strings and zeros.