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User Knowledge, Data Modeling, and Visualization

[Castle, Bonn, Germany]

- User Knowledge, Data Modeling and Visualization

In modern technology, the level of knowledge is increasing day by day. This growth is reflected in volume, velocity and variety. Understanding this knowledge is essential for individuals to extract meaningful insights from it. With advances in computer and image-based technologies, visualization has become one of the most important platforms for extracting, interpreting, and communicating information. 

In data modeling, visualization is the process of extracting knowledge to reveal the detailed data structures and processes of data.


- 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.



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