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What is data modeling? Why do you need it?
A must-have for becoming a data modeler
Academic requirements + professional requirements
Top 9 Capabilities Data Modeling Needs
Career Development for Data Modelers
The importance of certification
Data modeling sounds great, but do you know what data modeling is and why does it play a key role in the life and death of an enterprise?
What is data modeling? Why do you need it?
Data modeling evaluates and measures the data flow of a database management system, and manages the input and output of the data flow. Data modeling is responsible for creating the space required for data, so it is one of the most important parts of a big data project. Data modeling builds space for data and takes care of factors related to the environment in which the data resides.
In layman's terms, data modeling is the process of solving real-world problems by building data science models. For example, when an enterprise encounters problems of uneven distribution of resources and serious waste of resources, when the enterprise encounters a growth problem, or when the company's profit declines, data modeling can quickly help the enterprise to understand the problem and find a solution.
In the development and optimization process of many apps, the data modeling process is used to provide the most seamless customer experience.
data modeling process
The process of designing a database consists of three main schemas: conceptual schema, logical schema, and physical schema. These schemas are transformed into the active database using the Data Definition Language. A data model with full properties and covering all major aspects, which itself contains a detailed description of each entity.
While there are several ways to create a data model, there are two ways to generate the best model. These processes are called bottom-up and top-down data modeling processes.
Bottom-up data models: Also known as ensemble models, created through re-engineering efforts. This approach typically starts with an existing structured form of data and underlying reports. This model may not be suitable for data sharing because they are created without specific reference data for all other departments within the organization.
Top-down data model: Created through an abstract approach, taking information from people with sufficient expertise in the subject area. This model system may not be available to all companies, but it is a very informative one.
A must-have for becoming a data modeler
- Conceptual Design Capability
- abstract thinking skills
- User communication skills
- Internal communication skills
Based on these requirements, if you do not have the required software and system knowledge, but have the ability to think conceptually and abstractly, you will also be considered to have the potential for data modeling.
Communication skills are essential for all data modelers. Organizations require data modelers with strong communication skills as they need to interpret and balance all user needs. In addition, the end result needs to be documented in a perspective that all users can understand.
Academic requirements + professional requirements
Generally speaking, a bachelor's degree is required, and information science, applied mathematics or computer majors are more popular; of course, if they are particularly excellent, the conditions are lenient. Other companies want to hire data modelers with multiple information systems management or business management experience. A data modeler should also be proficient in database management, know how to look at a database, and consider possible outcomes of varying data complexities.
Top 9 Capabilities Data Modeling Needs
1. Digital Logic: Also known as Boolean logic, it is the foundation of all modern computer systems and programming languages. It is a system that reduces complex problems to "yes/no", "true/false" or "1/0" values, which are put into equations to produce input and output operations. As the fundamental concept behind coding, having this skill is important for cleaning and organizing unstructured datasets.
2. Computer Structure and Organization: This skill builds on the first listed digital logic skills. Logic, architecture, and organization are all interrelated. All of this needs to be firmly grasped in order to optimize performance. Computer architecture is a set of logical rules that allow programmers to interface between hardware and software, including their internal functions and implementation. Computer organization is an expression of its architecture and the structure of the system itself. A solid understanding of computer architecture and organization allows you to maximize efficiency when working with data.
3. Data representation: Data is the decomposition of complex information into simpler bytes, eg encoded into numbers. Makes data collection, manipulation, and analysis easier, saving time and money.
4. Memory Architecture: We learned earlier how to better encode and represent data, then it is more important to store data for subsequent retrieval. Memory architecture deals with how binary numbers are stored in computer cells and more complex data in spreadsheets and database programs. The most important part of memory architecture is being able to find the best way to combine speed, endurance, reliability and cost-effectiveness without compromising data integrity.
5. Familiarity with the many modeling tools available: The tools used to help with data modeling are extensive, however, some of the top tools include Power Designer, Enterprise Architect, and Erwin. These tools are used to organize and define data for optimal results. Familiarity with these tools can save training time and be able to analyze datasets more efficiently.
6. Adapt to new modeling approaches: Data modeling will continue to evolve. In the years to come, the differences in infrastructure, data sources, and models will become more complex. For data modelers, the ability to quickly learn from case studies or other proven methods and adapt their modeling approach is key to staying ahead of the technology curve.
7. SQL language and its implementation: SQL stands for "Structured Query Language" and is very important for data modeling, it is a standard programming language for manipulating, managing and accessing data stored in related databases. Its ease of development and portability make it a universal language for database queries. It is impossible to become a data modeler without a foundation in SQL.
8. Rich experience in using database systems: Relational database management systems (RDBMS) have big data processing capabilities, such as the ability to quickly store and retrieve data. This experience is absolutely necessary for managing complex data environments.
9. Exemplary communication skills to navigate complex hierarchies: Data modeling requires more than technical skills. Also, communicate knowledge about complex technical data with any non-data professional in a way that is easy to understand. Data modelers need to communicate with all business levels to better drive change implementation and drive growth. It's going to be extremely challenging, but it's important to be able to connect and inform everyone while understanding the politics of business.
Career Development for Data Modelers
If you are a newbie, you need to be trained by an experienced instructor, preferably someone with many years of experience in data modeling and who has participated in many training programs as both a learner and a trainer. Mentors should be proficient in data modeling techniques and understand all systems in a given organization. The experience of the mentor and its training method has a decisive influence on the ability of the data modeler to apply the skills.
When you graduate and enter the company, you will have many opportunities for advancement. For example, start taking a small project, lead your own department, and maybe even become a manager of an IT company that does data marketing or data modeling.
Career Outlook
Data modelers typically work with data analysts and architects to identify key dimensions and facts to support a client's or company's system requirements. Data modelers need to manage and maintain the quality and integrity of data. You need to have extensive domain knowledge to interpret data analysis results.
Many data modelers start their careers as data analysts, and as they gain experience and success at the foundational level, data modelers continue to climb the industry. There is a lot of room for learning in data modeling, so the pay is also decent. According to a survey by Glassdoor, the average salary of a data modeler in the market is expected to be $78,601, or about RMB 510,000 per year.
The importance of certification
Just as project management must be certified by PM, data modelers also need to obtain relevant authoritative certifications. These certifications include big data and data science courses, big data engineer master certification, big data Hadoop training certification, R language science certification, etc. etc.
If you are interested in becoming a data modeler, St. Pullen can provide you with the right guidance to help you learn data analysis, data exploration, data visualization, predictive and descriptive analysis techniques, functional programming, Spark SQL and more skills to build a solid foundation for your career.