Just as an Architect prepares a blueprint before the construction of a building, a Data model designs a Data Model to achieve the business goals. Data Modeling refers to the creation of a model for the organization’s information stored in their database. It establishes the relation of the data elements with one another. So, this enables a finer understanding of the manner the data is organized and structured for effective analysis and accessibility. Its visual representation makes it easy to identify gaps and errors at the modeling stage.

Data Modeler
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Who is a Data Modeler?

A Data Modeler creates data models that are simple and easy to understand. His task is to identify the needs of the organization and develop a model to cater to those needs. So, he evaluates and measures complex business data to construct effective databases and innovative data solutions for the organization. Relevant data is chosen as a resource and incorporated into the system to achieve the organizational objectives. The clear, interpretable data helps the business to make decisions. So, the main intent is to enable organizations to make use of the data in an efficient manner.

A data model diagram provides a better understanding than a list filled with information. The Data Modeler is responsible for the development of conceptual data models, logical data models, and physical data models. He has to team up with data scientists and database administrators in the modeling process. This role can be regarded as an extended role of a data analyst. So, an experienced data analyst has a wide scope to fit into this position.

Data Modeler skills

A Data Model professional must have the ability to visualize the big picture behind the vast available data. More than technical skills, he must possess the ability to make logical usage of data. From the collection of interrelated data, he must be able to develop data coding practices to bring consistency to the system. So, with the implementation of data strategies, he must be able to create physical data models.

The below Data Modeler skills will prove advantageous to the candidates aiming for success in the field of data modeling.

1) Technology Savvy

A Data Modeler must have system knowledge to operate the basic hardware and software tools. He must familiarize himself with Digital logic that can simplify complicated data into a structured set of data. This knowledge improves productivity and profitability. So, a complete understanding of computer architecture will enable him to attain maximum efficiency.

Data Modeler

2) Strong Communication skills

A Data Modeler must possess strong communication skills to translate technical concepts in a clear modular form. The data model has to be understood even by people from non-technical data roles. An effective presentation skill is also a requirement for proper communication with the respective stakeholders. Since the data model process involves teamwork, there is also a need to develop collaborative and interpersonal skills.

3) Data representation

The Data Modeler has to convert complicated information into a simplified form by expressing them through text and symbols. So, such structured data will result in an easier analysis of data and saves time. The model will support the development of a sound information system. Insightful knowledge of data warehouses will enhance job performance.

4) Memory Organization

The data is stored in the system in a manner that enables fast and easy retrieval in the future. So, a data modeler must be acquainted with this process of memory hierarchy. Depending on the simplicity and complexity of the data and their subsequent requirement, they are stored in the respective hierarchy. The aim should be to use a method that provides the highest possible access speed at a minimum cost. 

5) SQL (Structured Query Language)

Learning SQL is among the basic Data Modeler skills. SQL is a programming language that is compatible with different programming languages. It is used for processing and storing information in tabular form. It contains rows and columns that represent different data attributes and the relation between data values. SQL is the standard language to store, search, and retrieve information from databases. An understanding of MySQL, PostgreSQL, and NoSQL databases will also add value to the job function.

Data Modeler

6) Continuous Learning

Today’s market is fast-changing and the data modeler skills need constant upgradation. So, such professionals must invest in acquiring new skills on an ongoing basis. Every organization expects these competent players to remain aware of the latest software development. Advanced tools can be cost-effective with better speed and reliability.

Data Modeler job description

The demand for a data modeler is growing at a fast pace. So, it offers a significant opportunity to build a suitable career. There is no specific qualification to become a data modeler. But, a degree in computer science or information technology can help to build a strong base in this profession. In a Data Modeler job description, some online courses on Data Modeling and Data Analytics from reputable institutions carry a good weightage.

Data Modeler

Responsibilities of a Data Modeler

1) Design models that can reduce data redundancy and increase operational efficiency.

2) Analyze and translate business needs into successful data models.

3) Work in collaboration with the Development team to create the model.

4) Develop conceptual, logical, and physical data models.

5) Evaluate and expand existing data architecture.

6) Use data coding to bring consistency to the system.

7) Review the existing system to bring further modification for better results.

8) Implement data strategies for faster decisions and better customer experience.

9) Follow a systematic approach to solve a problem by identifying the symptoms of a failed process and making it operational again.

10) Ensure data security and handle data cleaning requirements.

Henry Harvin Education

Henry Harvin Education offers a “Data Analytics Using R Course” for candidates aspiring to become data modelers. This online course helps the students to build a strong foundation in the area of data modeling.

Conclusion

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A Data Modeler makes a significant contribution to the data science process. So, even at the entry-level, this job offers good compensation. But, before joining a large organization, it is worth it to gain some practical experience through an internship. At the beginning of the career, working with experienced data model professionals can be highly rewarding. In addition, he can build a network with mentors and learn from their expertise on the latest market updates and technology trends.

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FAQs

Q.1: Is data modeling difficult?

Ans: Data modeling is not easy to learn. This job requires a clear understanding of the entire data organization process. So, it needs multidimensional skills with a high level of learning. But, with a firm determination, one can acquire the necessary skills.

Q.2: Is a data modeler the same as a data analyst?

Ans: Though there are some common skills between them, the job roles for these two professionals are different. Data analysts make use of statistical knowledge for data interpretation whereas data modelers make use of logic to establish relations between data sets.

Q.3: What is granularity in data modeling?

Ans: Granularity refers to the specific size that data fields are split into. So, it is the information that does not have any further subdivisions. It is the lowest level of raw data that cannot be broken down further.

Q.4: What is data sparsity in data modeling?

Ans: Data sparsity refers to datasets in which the majority of the values are zero or missing. So, when zero values are more than non-zero values, it indicates the dataset contains insufficient information for data processing. Such inconsistencies in data can affect the accuracy of the analysis results.

Q.5: What do you mean by NoSQL database?

Ans: Just as SQL databases are in tabular form, NoSQL databases are non-tabular. Secondly, the data points in NoSQL are not related to one another. So, with its flexibility and ease, NoSQL provides better performance for large datasets.

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