Domain Knowledge, Data Modeling, and Visualization

- Overview

With the development of modern technology, user knowledge is increasing day by day. Users try to capture necessary information from a large amount of domain knowledge. Capturing important information is the key to successful user knowledge.

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.

- Data Visualization

Data visualization is the practice of transforming information into a visual environment, such as a map or graph, to make it easier for the human mind to understand data and draw insights from it. The main goal of data visualization is to more easily identify patterns, trends, and outliers in large datasets. The term is often used interchangeably with other terms, including infographics, information visualization, and statistical graphics.

Data visualization is one of the steps of the data science process, which states that after data is collected, processed, and modeled, it must be visualized to draw conclusions. Data visualization is also an element of the broader discipline of Data Presentation Architecture (DPA), which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible. 

Data visualization is important to almost any career. Teachers can use it to display test results for students, computer scientists can use it to explore advances in artificial intelligence (AI), or executives who want to share information with stakeholders. It also plays an important role in big data projects. As businesses accumulated large amounts of data in the early days of the big data trend, they needed a way to quickly and easily get an overview of the data. Visualizers are a natural fit. 

For similar reasons, visualization is at the heart of advanced analytics. When data scientists write advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the output to monitor results and ensure the model is performing as expected. This is because visualizations of complex algorithms are often easier to interpret than numerical outputs.

- Data Modeling

Data modeling refers to the process of creating a visual representation of an entire information system or parts thereof to convey relationships between data points and structures. The purpose is to show the types of data stored in the system, the relationships between the data types, the format and properties of the data, and how the data is grouped and organized.

Data models are usually created around business requirements. Requirements and rules are predefined through feedback obtained from business stakeholders so that they can be used to design new systems. The data modeling process starts with gathering information about business needs from stakeholders and end users. Business requirements are then translated into data structures to develop a specific database design.

Today, data modeling has applications in every field you can think of, from financial institutions to the healthcare industry. A LinkedIn study named data modeling the fastest-growing occupation in the current job market.

- Data Modeling and Visualization: Key Similarities

Following are the key similarities between data modeling and visualization:

• They both deal with data: data is central to data modeling and data visualization. They help users make sense of ambiguous data sets and obtain relevant metrics to help make better decisions.
• No need for ML algorithms: Neither data modeling nor visualization requires the use of machine learning algorithms to get correct results.
• They both use visual elements: In both data modeling and data visualization, answers are in the form of visual elements, not text or numbers. However, they differ in the types of visual elements used.
• No data analysis required: Neither data modeling nor visualization requires analyzing data. Instead, data engineers and data modelers go straight to the data as-is to find inconsistencies in the data.