There is a always a need for data modelers, however, the job description of this career field varies, depending on the needs of the organization. For example, a data modeler working for a startup would coordinate with data scientists and data architects in designing a new system — one that included the goals of the organization, and the steps needed to achieve them, within its architectural design. This “model” represents the organization and promotes understanding through the use of core data, such as attributes, entities, and relationships regarding customers, staff, products, and other factors.
A data modeler working for an organization with an already established system would be more focused on model maintenance, integrating data from multiple sources for purposes of presentations and decision making, and implementing changes to make the organization more efficient.
A data modeler working for an established organization should be technically skilled in the administration of databases, but may also need to assist in developing presentations, and should be comfortable dealing with both staff and customers.
The ideal data modeler is a creative thinker, with good analytical skills and a can-do attitude. They understand how to successfully evaluate problems and develop the appropriate solutions. (Many data modelers were former analysts.)
Successful data modelers work well under pressure. They must be able to work both independently, and as part of a team. They should be capable of working on multiple projects simultaneously and have the ability to grasp and understand new technologies, quickly.
Data Modeling is a growing field, with many opportunities. Data modelers often work with data architects and analysts to identify key information that supports both the goals and the system requirements of the organization. Managing and keeping the data’s integrity and quality is essential.
Data modelers are paid reasonably well. According to Glassdoor, a data modeler’s average salary is projected to $78,601, and, currently, there seems to be no shortage of career opportunities. Most data modelers begin as analysts, then move up the ladder as they gain experience.
Many organizations seeking data modelers require a bachelor’s degree, preferably in computer science, information science, or applied mathematics. However, a fair number of current employment ads are more interested in experience than in a degree. Certificates communicate knowledge of specific skills and, combined with a degree, can express flexibility and a continuing desire to learn.
Certifications are quite important when proving experience of Data Modeling within the formal setting of a job interview. Many businesses agree on the importance of obtaining reputable certifications showing proof of expertise and enhanced skills.
Useful Tech Skills
Metadata Management is essential useful for organizations looking to understand the context, definition, and lineage of key data assets. Data models play a key role in Metadata Management, as many of the key structural and business definitions are stored within the models themselves.
An understanding of digital, or Boolean logic is also useful. Understanding the fundamental concept behind coding can be quite helpful in cleaning and organizing unstructured data, and creates a foundation for understanding computer architecture.
Computer architecture provides a logical set of rules, allowing programmers to interact with the software and hardware. A good understanding of both the organization itself and the organization’s computer architecture is quite valuable, in terms of efficiency and communication.
Reverse engineering involves taking a product apart to determine how it works, with the intention of reproducing or enhancing it. It is a practice used in older industries, and has been adapted for use on computer hardware and software. There are a number of tools available. Forward engineering is the process of using a high-level model to build a product, including complexities and lower-level details. Forward engineering represents the “normal” software development process. (There is software that combines forward and reverse engineering.)
Data representation describes how information is stored and used in a computer. Understanding this makes it easier to gather, manipulate, and analyze data, saving time and money. Memory architecture deals with how computers store data. The goal is to find the fastest, most reliable, durable, and inexpensive way to save and retrieve data, while maintaining the data’s integrity.
Understanding the tools used for Data Modeling is a very good idea. The list of tools available is quite extensive. Some of the most popular include Enterprise Architect, Erwin, and PowerDesigner. SQL (structured query language) is the standard programming language for manipulating, managing, and accessing data stored in relational databases — and understanding it is a necessity. Without an understanding of SQL, Data Modeling is not possible.
Develop, publish, and maintain all documentation used for data models. This is useful for basic communications, and proving the position is necessary. Data warehousing is the storage of large amounts of data by an organization. Data warehousing is considered vital in gathering business intelligence, and it uses analytical techniques. Understanding data models. There are basically three kinds of data models: conceptual, logical, and physical. Data Modeling helps in providing a visual representation of the data and business rules, and supporting government regulations.
Useful Soft Skills
Adaptability is a mindset, and can be developed. As Data Modeling continues to evolve, it is a very useful mindset and should be consciously maintained. As the changes in infrastructure, models, and data sources become more complicated, the ability to learn quickly and adopt various modeling methods become crucial skills for a data modeler.
Good communication allows individuals to share their knowledge of complex data to nontechnical people, so they can make intelligent decisions. (If one of your short-term goals is impressing people with big words they don’t understand, this job is not for you.) Data modelers need to communicate with different levels of management and staff.
The Data Modeling Process
A data model should be designed with the goal of developing a database that complements the business model and works smoothly and efficiently. This process will require some experimentation and adjustments, and this fact should be communicated to upper management on a regular basis. Though there are a variety of methods available to create data models, two paths are extremely popular. They are known as top-down and bottom-up data modeling.
The bottom-up method (also referred to as integration models) involves using re-engineering techniques. This process typically starts off using the existing structure forms for data and underlying reports. A bottom-up approach pieces together systems to develop more complex systems, or makes the original group sub-systems of the final version.
Top-down data models are built using an abstract methodology, which includes gathering information from people who have a good understanding of how their departments operate.
Developing a model that truly represents a business will require a little tailoring, but is certainly worth the investment. However, many companies fail to achieve the expected return on their investment. In part, this is because the data scientists/modelers don’t have a full understanding of the business and its practices. There are four modeling tactics that can help in developing a fuller picture of the organization.
Real life business experiences, or use cases, common to the organization, should be collected prior to creating a model. For example, asking the customer satisfaction team about their problems in everyday decision-making can provide the data modeler with some of the information needed to build a model. If the customer satisfaction team wants to know when a customer is likely to churn (leave a website within a certain time frame), a good solution is a predictive model that scores churn risk with customer-level granularity.
A different solution would be used if the question is: “Why are customers churning?” In this situation, a model that isolates key factors normally preceding a customer’s cancellations is needed. An “algorithm” would be used to analyze the past activity of churned customers to identify and rank patterns associated with their loss. With this type of use case, it’s important the model allows for measurements of the algorithm’s success rate.
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