Data modeling is a flourishing field and an exciting option for anyone looking to carve a career in data science. Data modelers are system analysts in charge of identifying an organization's needs and developing data models to meet those needs. Data modelers often work with data architects and database administrators to ensure that business data is well-managed and optimized to help attain critical objectives. The demand for data modelers is growing by leaps and bounds.
What Is Data Modeling and Why Do You Need It?
Data modeling evaluates and measures how an organization manages the flow of data in and out of the database management system. Since it is responsible for creating the space needed for your data, data modeling is one of the most important parts of a Big Data project. Data modeling structures the space for your data, and looks after the factors related to the environment your data lives in. In short, data modeling is the management of data within an organization.
Data modeling also determines how the data should be treated, how the data neurons connect with each other and define how the data is generated, and what story it will tell going into the future.
Considering the impact it has on an organization, decisions regarding data modeling need to be made early on in the data-gathering process. It is up to the organization to decide what story each data set will narrate, and for data to tell the perfect story, it needs to be modeled to perfection.
Numerous software applications make use of data modeling processes to give the most seamless customer experience. With the changing culture of the world, it is imperative that the data you hold should be altered in a way that best matches the needs of the end customer. Ensuring a perfect customer experience is something that many organizations are working on, and this experience can be achieved only through the use of perfect data modeling strategies.
The Data Modeling Process
Data modeling serves as a means to complement business modeling and to work towards generating a sufficient database. The process for designing a database includes the production of three major schemas: conceptual, logical, and physical. A Data Definition Language is used to convert these schemas into an active database. A data model that is fully attributed and covers all major aspects includes detailed descriptions for every entity contained within it.
Although data models can be created through the use of numerous methods, there are two methodologies that produce the best model. These are known as the bottom-up and top-down data modeling processes.
- Bottom-up Model: Bottom-up models, also known as integration models, are created through re-engineering efforts. This method usually starts with the existing structure forms for data and underlying reports. This model may not be feasible for data sharing, considering that they are built without specific reference to all other departments/parts of the organization.
- Top-down Data Model: Top-down data models are created through an abstract methodology, by garnering information from people who have sufficient expertise in the subject area. The system for this data model may not implement in all entities, but the model does serve as a brilliant template or reference point.
Who Is a Data Modeler?
A data modeler is a system analyst and engineer who designs computer databases and data models used to turn complex organizational data into usable computer systems. They use relational, dimensional, and NoSQL databases to manage the flow of information between departments in an organization. Data Modelers use their understanding of data flows to propose innovative data solutions to help the organization achieve its end goals in all areas of operation, including product lifecycle and customer experience.
The Role and Responsibilities of Data Modelers
Data Modelers design and manage data systems to support organizational goals and system requirements. Being a data modeler means working with other IT professionals like data scientists and database administrators to design critical data models that help the organization's decision-making and customer experience processes. You will be responsible for understanding how the organization uses its core data and maintaining data integrity by eliminating redundancy.
Most data modelers start their careers as data analysts and subsequently move up the ladder as they gain the experience and certifications needed in the data modeling field.
As a data modeler, your responsibilities will include:
- Analyzing and translating business needs to create solution data models
- Evaluating current data systems
- Working with data architects and database administrators to design conceptual data models and data flows
- Developing ideal data coding practices to ensure consistency within the system.
- Evaluating changes to existing systems for cross-compatibility.
- Implementing data strategies and creating physical data models
- Updating and improving local and metadata models
- Assessing implemented data systems to look for variances, discrepancies, and efficiency
- Troubleshooting and optimizing data systems
What Do You Need to Become a Data Modeler?
The skills required for data modeling are quite different than the skills required for programming and systems administration. While programmers and administrators are required to have sufficient expertise on the technical front, data modelers are required to be more apt at the logical side of things. The skills required for data modeling include the following:
- Conceptual Design
- Abstract Thinking
- User communication
- Internal communication
Based on these requirements, a person who does not have the required software and system knowledge, but has the proven ability to think conceptually and abstractly, will be considered perfect as a data modeler.
Communication skills are essential for all data modelers. Organizations look for strong communication skills in data modelers because modelers are required to translate and balance all user requirements. Moreover, they are also required to document the final results in a perspective that is easy to understand for all users.
Many recruiters looking for data modelers want candidates with a bachelor’s degree, preferably, in information science, applied mathematics, or computer studies. These degrees are deemed perfect for a data modeler, and the candidate is considered suitable in most cases. However, some employers may also want to look out for data modelers with multiple courses in information systems management or business management. Data Modelers should also be skilled in database administration and should know how to look over a database and to think of plausible outcomes for different data complications.
You must exhibit the following nine skills before pursuing a career in data modeling:
- Digital logic: Digital logic is also known as boolean logic, and it is the basis for all modern computer systems and programming languages. It is a system that simplifies complicated problems into “yes/no”, “true/false” or “1/0” values that are placed into equations to produce input and output operations. As the fundamental concept behind coding, it is important to possess this skill in order to clean and organize an unstructured set of data.
- Computer architecture and organization: This skill builds on the first listed skill of digital logic. Logic, architecture, and organization are all interrelated, and a firm grasp is needed of all of these in order to optimize performance. Computer architecture is the logical set of rules that allow a programmer to interface between the hardware and software, and how it internally functions and is implemented. The organization of the computer is an expression of its architecture, and how the system itself is structured. A solid understanding of computer architecture and organization will enable you to maximize efficiency when working with data.
- Data representation: Data representation involves breaking down complicated information into simpler bits, such as being coded into numbers. This allows for easier gathering, manipulation, and analysis of data, which can save valuable time and money.
- Memory architecture: After you understand how to best represent and code the data, it is important to be able to store it for future retrieval. Memory architecture concerns how binary digits come to be stored in a computer’s cells, as well as the storage of more complicated data in spreadsheet and database programs. The most important part of memory architecture is being able to find the method that best combines speed, durability, reliability, and cost-effectiveness while not compromising the integrity of the data.
- Familiarity with numerous modeling tools that are currently in place within organizations: The list of tools that exist to aid in data modeling is extensive, however, some of the top tools include PowerDesigner, Enterprise Architect, and Erwin. Organizations utilize these tools in order to structure and define data for optimal results. Already being familiar with these tools can help save valuable training time on the job and enable you to more efficiently be able to analyze your data sets.
- Adapt to new modeling methods: Data modeling will continue to evolve. The differences in infrastructure, data sources, and models will likely become more complicated in the coming years. The ability to quickly learn and adapt modeling methods from case studies or other proven approaches is a crucial skill for a data modeler to stay up to date.
- SQL language and its implementation: SQL stands for “structured query language” and holds primary importance when becoming a data modeler, as it is the standard programming language for manipulating, managing, and accessing data stored in relational databases. Its ease of development and portability helped to make it the nearly universal language for querying databases. In short, without a foundation in SQL, it is not possible to be a data modeler.
- Sufficient experience using database systems: Relational Database Management Systems (RDBMS) that possess big data handling capabilities, such as the ability to quickly store and fetch data. Experience with these is absolutely necessary for managing a complex data environment.
- Exemplary communication skills that will help you in making your way around organizations with an intricate hierarchy: Data modeling isn’t just about possessing technical skills. You also need to be able to communicate your knowledge of complicated technical data in such a way that those in any non-technical data roles will also be able to understand. Data modelers need to be able to communicate with all levels of a business in order to best help implement well-informed changes and promote growth. This can be quite challenging, but it is important to be able to relate to and inform everyone while understanding the nuances of business politics.
How Do You Advance as a Data Modeler?
As soon as a novice modeler starts their training period, they are assigned to an experienced mentor. The experienced mentor should preferably be someone who has years of experience in data modeling behind them and has partaken in many training programs, both as a learner and as a trainer. The mentor should be well versed with the techniques used for data modeling within the industry and should know of all the systems in place with the specific organization. The experience of the mentor and the training methodology used by them, usually defines how well the data modeler is able to apply his or her skills within the organization.
There are numerous advancement opportunities for data modelers in the workplace. A data modeler’s career can grow over time, and they can soon head their own department or even become a manager of an IT firm that works in data marketing or data modeling.
Stepping into a career as a modeler, you’ll have to work with data analysts and architects to identify key dimensions and facts to support the system requirements of your client or company. You will be required to manage and keep the integrity and quality of the data. It’s essential to have domain knowledge to be able to interpret the results.
Most data modelers start their journey as analysts and then move up the ladder of the hierarchy as they prove themselves and gain experience in the lower ranks. There is a lot of scope for learning, and data modelers can be assured of being greatly compensated. In fact, according to Glassdoor the average salary in the market for data modelers is projected to $78,601 on average. Data Modelers also get paid well, which is why there is no shortage of adequate monetary and career opportunities.
Data Modeler Jobs
There are many different types of data modeling jobs available. Some data modelers work in the IT department of a company, developing databases and data systems. Others work in research and development, creating models to test theories or help develop new products. Still others work in marketing, using data to create better customer profiles or understand consumer behavior.
Data Modeler Salary (According to Location and Experience)
With increasing demand for professionals with data modeling skills, there is no shortage of career opportunities in this field. As such, data modelers across the globe earn handsome pay, perks, and benefits.
In this section, we’ve put together the latest salary data of data modelers in India, the US, and the UK.
Data Modeler Salary in the US
According to Salary, the average salary for a data modeler in the United States is $102,340 per year as of September 26, 2022. Data Modeler Salary ranges ($89,340 to $117,090) can vary depending on education, certifications, additional skills, and years of experience.
- Fresher - The entry-level Data Modeler Salary in the US starts at $104,526 per year.
- Experienced – Most experienced data modelers in the US can make up to $156,000 per year.
Data Modeler Salary in India
According to Glassdoor, the national average salary for a data modeler in India is ₹12,51,051 per year. The pay ranges between ₹ 8.3 Lakhs to ₹ 28.3 Lakhs depending upon education, certifications, additional skills, and years of experience.
- Fresher – Freshers with less than four years of experience can earn about ₹ 8.3 Lakhs per year working as Data Modeler in India.
- Experienced – Experienced Data Modelers can earn more, with candidates having data modeling experience of 14 years making as much as ₹ 28.3 Lakhs annually.
Data Modeler Salary in the UK
According to Glassdoor, the national average salary for a Data Modeler in the UK is £47,665 per year. The average per-day data modeler salary in the UK is £527.
- Fresher – The entry-level Data Modeler positions start at £28,575 per year in the UK.
- Experienced – The most experienced data modeler's salary in the UK can be as high as £75,000 per year.
Frequently Asked Questions
1. How much does a data modeler make a year?
A data modeler in India earns an average of ₹20.3lakhs per year, with salaries ranging from ₹12.0lakhs per year to ₹31.8lakhs per year depending on education, certifications, additional skills, and years of experience.
The top 10% of Data Modelers earn over ₹28.6lakhs per year.
A data modeler in the US can earn $89,340 to $117,090, while the average base salary is approximately $102,340 per year.
The average salary for a Data Modeler in the UK is £47,665 per year.
2. Is data modeling a promising career?
Data Modeling is an emerging field in the data science industry, with numerous job opportunities. Data modelers work with data architects and database administrators to ensure that business data is well-managed and optimized to help attain business objectives. Successful data modelers can earn high salaries depending on their expertise and experience.
Working as a Data Modeler typically requires a bachelor's degree in computer science, information technology, or a related field. 2 to 4 years of hands-on experience with physical and relational data modeling is preferred. Additionally, you need knowledge of metadata management and associated tools, mathematical foundations, and statistical analysis.
3. What does a data modeler do?
Data Modelers are system analysts who design computer databases and data models used to turn complex organizational data into usable computer systems. They use relational, dimensional, and NoSQL databases to manage the flow of information between departments in an organization. Data Modelers use their understanding of data flows to propose innovative data solutions to help the organization achieve its end goals in all areas of operation – including product lifecycle and customer experience.
A data modeler works with data scientists and database administrators to design critical data models that help the organization's decision-making and customer experience processes. They help understand how the organization uses its core data and help maintain data integrity by eliminating redundancy.
4. What degree do you need to be a data modeler?
To become a Data Modeler, you need a bachelor's degree in computer science, information technology, applied mathematics, or a related discipline. These degrees are considered perfect for a data modeler, and most recruiters consider the candidate suitable.
You must have hands-on experience in a similar role. Additionally, you need to possess skills in problem-solving, communication, and interpersonal multitasking. Knowledge of metadata management, related tools, mathematical foundations, and statistical analysis is preferred. Candidates well-versed in Oracle, SQL Server, and Microsoft Office, among other tools, have an added advantage.
The top skills required to become a Data Modeler are:
- Data Modeling
- Data Warehousing
- Data Architecture
5. Do data engineers do data modeling?
Data modeling is typically done by data analysts, who work with data architects and database administrators to identify an organization's needs and develop data models to meet those needs.
Data engineers typically know more about the data itself – where it resides, how it is structured and formatted, and how to retrieve data. They know less about how the business uses data. This makes the data engineer's role ideal for getting data into the cloud and transforming raw data into data models.
On the other hand, data analysts know less about raw data but have complete knowledge of how the business would use data and how to incorporate it into analytics. Thus, they are the ideal candidates for data modeling and transformation.
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