Domain Knowledge

- Overview

Domain knowledge refers to understanding a particular industry, field, or business area that data analysts need to have to interpret data and draw meaningful insights effectively. Having strong domain knowledge is crucial for data analysts as it provides the necessary context for analyzing data and identifying trends and patterns.

Data exists in different forms such as structured and unstructured. Big data refers to data whose size, diversity and complexity require new algorithms, structures, technologies and analytics to manage and visualize and extract hidden information.

Visual context is made up of visualizations of user knowledge and data, transforming information through graphics or maps to make data easier for humans. In visualization, patterns are identified from large amounts of data and drawn through information visualization, graphs, and statistical graphics.

Data visualization is one of the data science processes in which data are collected, modeled and processed, so visualization should be done to draw conclusions from the data. Data visualization is of great significance in all areas of life. It can be used in teaching, medical care, artificial intelligence, big data and other fields to share the extracted information with shareholders.

Knowledge, data and information are widely used for visualization in an interrelated perspective. Visualization shows the different stages of understanding and abstraction. The purpose of visualization is to gain meaningful insights from data. Through data visualization, people can interact with and analyze data. Data visualization can provide many benefits, such as effective communication, concrete and abstract information, and innovative methods for scientific and engineering purposes.

Information visualization is a graphical representation of abstract data that attempts to reduce the time and effort required by users to analyze large data sets.

- Domain Knowledge in Data Science

Domain knowledge is a collection of skills and expertise specific to a particular field or industry. It can include facts, concepts, terminology, and insight into the sources and limitations of data, operational requirements, and context. Domain knowledge can come from hobbies, passions, personal research topics, professions, or specializations. 

Domain knowledge can be a crucial business skill for management positions, where managers need to be able to oversee projects and make decisions based on the current state of the industry. 

For example, in data science, domain knowledge can help with:

• Cleaning up data: Domain knowledge can help identify and quickly fix missing numbers, outliers, and discrepancies in data. For example, in manufacturing, a sudden increase in sensor data might indicate defective equipment, but without domain knowledge, it could be mistaken for a data error.
• Creating features: Domain knowledge can help develop relevant variables to feed models.
• Making tradeoffs: Domain knowledge can help answer questions like how many new people to hire or which shortcuts are worth it.

- Benefits of Using Domain Knowledge

• Improved data quality and consistency: Domain knowledge helps identify and eliminate errors, outliers, and inconsistencies in the data.
• Enhanced data understanding and exploration: Domain knowledge facilitates the discovery of patterns, trends, and relationships within the data.
• Facilitated data analysis and interpretation: Domain knowledge enables the use of domain-specific methods, metrics, and criteria for data analysis.
• Increased data value and utility: Domain knowledge aligns the data model and visualization with the user's needs and expectations.

- Example

Consider a healthcare domain. Domain knowledge helps in understanding the nuances of medical data, such as different types of diagnoses, treatments, and patient populations. This knowledge is crucial for building a data model that accurately represents patient records and for creating visualizations that effectively communicate trends in patient health, disease prevalence, or treatment outcomes.