How to Become a Data ArchitectHow to Become a Data ArchitectHow to Become a Data Architect

At the foundation of every recent technological advancement are models: system models, information models, software models, and data models. These models visualize and map every aspect of the systems they represent, and explain how discrete systems interact with each other to perform specific tasks.

Data modelers and data architects use their training and experience to help managers solve business problems. They accomplish this by creating models of applications and data. As technologies such as machine learning and other forms of artificial intelligence continue to transform the way companies do business, demand for data modelers and data architects is skyrocketing.

A survey conducted by software vendor Algorithmia found that the number of companies employing between 11 and 50 data scientists, a job category that includes data modelers and data architects, increased from 9% in 2018 to 20% in 2019.
The number of firms employing between 51 and 100 data scientists increased from 2% in 2018 to 5% in 2019.
The number employing between 101 and 1,000 data scientists increased from 5% in 2018 to 11% in 2019.
The number employing more than 1,000 data scientists rose from 2% in 2018 to 3% in 2019.

To meet the growing demand for professionals in the data modeling field, programs such as an data science bachelor degree online are geared to provide data scientists with the training and experience to become data modelers and data architects. Here’s a close look at how to become a data modeler or data architect, including the steps to qualify for the positions and the outlook for career opportunities.

A data modeler consults a tablet while two monitors in the background display analytical graphs.

What Does a Data Modeler Do?

Data models illustrate the design and operation of computer and information systems. For example, a sales manager may need a model of the customer-order process to help identify bottlenecks in order tracking and fulfillment. Data modelers create the models and explain to business managers how the models can be applied to help the organization achieve its goals.

Data models are made up of entities that represent objects or concepts in the data being analyzed. The entities are converted into database tables relating to products, vendors, customers, or other items.
Each entity has attributes that describe the data types that apply to the data. For example, the entity “product” has attributes that include “name,” “price,” “model,” and other information about it.

Roles and Responsibilities of Data Modelers

The primary duty of data modelers is to ensure the accuracy of the data they are modeling and the models themselves. They enforce the integrity of entities and the relationships between entities by applying processes such as normalization to verify that the data is error-free and eliminate redundancies.

The roles of data modelers overlap with those of data architects and data scientists, although the three job categories are distinguished by their primary focus.

Data modelers create the framework used by data analysts to extract business intelligence from raw data.
Data architects design the organizational environment in which the modeling frameworks and all other data processing and storage operations run.
Data scientists work closely with business managers to define business problems, extract the data required to address the problems, and apply software to the data to resolve the problems.

Three Levels of Data Modeling: Physical, Conceptual, and Logical

The relationships between entities in a data model represent the business rules that define the parameters of the problem being addressed. The three levels at which data models operate are physical, conceptual, and logical.

Physical models are the frameworks, or schemas, that describe how the data is physically stored in the database.
Conceptual models are the high-level views of the data that business managers see.
Logical models lie between the physical and conceptual models to represent the logic of the data apart from its physical storage.

Importance of Data Modeling to Enforce Data Integrity

Data models are useful only if the data they’re based on is accurate, relevant, and trustworthy. Data modelers apply two types of rules to test the integrity of a model’s data.

Entity integrity ensures that all entities have a primary key, that the values of the primary keys are unique, and that the value of a primary key isn’t null.
Referential integrity guarantees that the relationships between any two entities (or database tables) are reliable. This is accomplished by using foreign keys, which are primary keys in one table that also appear in another table. A foreign key value in one table must have a corresponding primary key value in the table being referenced.

Data normalization verifies that each entity in a data model has a unique theme or topic by removing partial and transitive dependencies. Relational models ensure the integrity of data as it is added to a database by storing each piece of non-key data only once. Dimensional models extract accurate data from the database for use by business managers.

How to Become a Data Architect or Data Modeler

Data modelers and data architects fall within the job category of computer systems analyst, according to the U.S. Bureau of Labor Statistics (BLS). While most professionals in these positions have earned a bachelor’s degree in a field related to computer or information science, a specific degree isn’t a requirement for all such jobs. It’s also common for data modelers and data architects to continue to attend classes to stay abreast of new technologies, business dynamics, and market trends.

Steps to Become a Data Modeler or Data Architect

The first step students should take in becoming a data architect or data modeler is to determine whether a career in data science is right for them. Data modelers have to be skilled in data analytics, report writing, and statistics. They must also be well versed in business and management topics, and they are required to clearly communicate technical information to business decision-makers.

Next, students choose an academic path that may include an advanced degree. Many bachelor’s degree programs offer data science students options to minor in business or actuarial science. These programs also facilitate pursuing a master’s degree in data science, business data analytics, business administration, or software development.

Finally, students must develop important critical-thinking and innovation capabilities for data modeling and data architect positions, including the following:

Analytical skills for distilling many types of information from diverse sources into reports that business managers can use to support their decisions
Communication skills to act as a liaison between business departments and IT departments
Creativity to devise novel approaches for tackling data-related problems

Education Paths of Data Modelers and Data Architects

Most data modeling and data architect positions require a data science bachelor degree online or a related field. The positions frequently interact with business managers, so data modelers and data architects must be familiar with business concepts and information systems theory.

Some more complex positions may require an advanced degree. Towards Data Science notes that 27% of data scientist job postings require a master’s degree or higher, and 49% require a bachelor’s degree or higher.

The Analytical and Problem-Solving Skills Data Modelers Need

Data analytics are the heart of data modeling and data architect positions. The SmartData Collective lists the analytical and problem-solving skills data modelers rely on:

Mathematics and statistics
Programming: R, Python, and other languages
Data mining that includes unstructured data (video, social media feeds, etc.)
Data visualization that displays analytics results as graphs, charts, and maps

Data Modeler Career Paths and Employment Outlook

In many ways, data modeling is an intermediate step between data analytics and data science, as QuantHub explains. Data scientists are distinguished from data analysts in the timeframe they consider: Data analysis and data modeling are concerned primarily with the present, while data science looks more frequently to future states. Data modelers bridge the gap by applying predictive analytics to large data stores in support of business decisions.

Typical Career Paths for Data Modelers and Data Architects

PayScale notes that the most common career paths for data modelers involve landing data architect and data engineer positions. More than 20% of data architects and data engineers previously worked as data modelers. Similarly, data architects typically move to positions such as data engineer, solutions architect, and senior data analyst.

Job Outlook for Data Modelers and Data Architects

The BLS estimates that jobs for computer systems analysts will increase by 7% between 2019 and 2029, which is faster than the average for all occupations, with the role having a median annual salary of $90,920 in May 2019. Salaries vary according to factors such as location, industry, and education and experience levels.

According to PayScale, data modelers earned an average annual salary of about $68,000 in their first year of employment as of October 2020, increasing to an average of $90,000 in years five to nine, and $110,000 after 20 or more years of experience. Meanwhile, data architects earned an average annual salary of about $69,000 in their first year, which grows to an average of $106,000 in years five to nine, and $131,000 after 20 or more years of experience.

How the Positions Help Businesses Adopt New Technologies to Stay Competitive

A key role of data modelers and data architects is applying innovative approaches to an organization’s analytics operations. The positions serve as a bridge between technology and the latest business and industry trends. Data modelers, in particular, spend a great deal of time interacting with business managers and other data professionals. The objective is to help them understand the results of a company’s data analytics activities and apply the business insights in their daily decision-making.

Preparing for a Career as a Data Modeler or Data Architect

Data is the raw material that businesses convert into competitive intelligence. Data modelers and data architects play the vital role of explaining to business managers the implications of advanced data analytics as they impact a company’s short- and long-term goals.

Maryville University’s data science bachelor degree online program is designed to provide both the technical and business backgrounds that serve as the foundation for students to become data architects or data modelers.

Come be brave with us. Discover more about how Maryville’s online bachelor’s in data science program helps embolden students to pursue their professional goals.

Recommended Reading

Machine Learning vs. Data Scientist: Which Option Is or You?

Data Science Degree vs. Statistics Degree: Analyzing the Difference

Key Careers in Data: Comparing Data Analyst vs. Data Scientist vs. Data Engineering Professions

Sources

Algorithmia, “2020 Machine Learning Predictions and the Shortage of Data Scientists”

Credera, “Data Modeling Explained in 10 Minutes or Less”

Forbes, “Roundup of Machine Learning Forecasts and Market Estimates, 2020”

PayScale, Average Data Architect Salary

PayScale, Average Data Modeler Salary

QuantHub, “The Role of Data Analysts in 2020 and Beyond.”

Smart Data Collective, “Here Are the Skills You Need to Work with Big Data”