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Understanding the Data Modeler

Technology   |   Paul Warburg   |   Jun 1, 2022

Who Is a Data Modeler?

A Data Modeler is a highly specialized technical role that creates data flows and data models in support of data analysis and data management systems.

A data model is a representation of data, the relationships between data, and the rules that govern data. Data Modelers typically use relational, dimensional, and NoSQL databases to create data models. A Data Modeler is often a systems analyst who works closely with data architects.

The modeling itself can include diagrams, symbols, or text to represent data and the way that it interrelates. Because of the structure that data modeling imposes upon data, Data Modelers subsequently increase consistency in naming, rules, semantics, and security, while also improving data analytics.

Data modeling is the foundation for gathering clean, interpretable data that organizations can use to forecast and plan, adapt to changing markets, and make critical business decisions. Data Modelers help make data easy enough for anyone to understand. Their work helps organizations answer these types of questions:

  1. How can my product team use customer IDs to adjust its product roadmap?
  2. How can my marketing team conduct pricing analysis by connecting price points to certain products?
  3. How can my logistics team predict and circumvent the effects of a supply chain disruption?
  4. How can my finance team improve strategic investments based on more accurate forecasts?

What Are the Responsibilities of a Data Modeler?

The primary responsibility of a Data Modeler is to create conceptual, logical, and physical data models to meet business needs.

Data Modelers must have an in-depth understanding of an organization’s data, business operations, and business goals and objectives. Data Modelers collaborate with business stakeholders to understand the inner workings of business operations in detail in order to define the data—and the necessary structure of that data—and assign relational rules to it.

Responsibilities of a Data Modeler include:

  1. Analyzing, interpreting, and integrating solutions and data
  2. Visualizing and representing data for storage in a data warehouse
  3. Make sure that there is consistent data flow with minimal errors within the systems

By encouraging the business to consider its data in greater detail—how it’s generated and moves through applications, where and how data errors and anomalies occur, and how best to identify, remediate, and prevent them—Data Modelers play a key role in maintaining data quality, optimizing the performance of data management systems, and reducing development and maintenance costs.


Maintaining Data Quality 

By visually depicting requirements and business rules, Data Modelers allow developers to foresee and prevent large-scale data corruption before it happens. Plus, the work of Data Modelers allows developers to define rules that monitor data quality, reducing the chance of errors and contributing to greater trust in the data.


Optimizing Performance of Data Management Systems

Data Modelers design databases that help translate difficult business data into organized, user-friendly systems. An organized database operates more efficiently. Data Modelers prevent the schema from endless searching and enable results to be returned faster.


Reducing Development and Maintenance Costs 

Data Modelers surface data errors and inconsistencies early, making them far easier and cheaper to correct.

How Does Alteryx Designer Cloud Help Data Modelers?

Alteryx Designer Cloud helps Data Modelers transform data, ensure quality, and automate data pipelines, making data consumable at any scale. This intelligent, collaborative, self-service data engineering cloud platform helps Data Modelers:


Connect to data from any source. With universal data connectivity and a self-service architecture, Designer Cloud makes it fast and easy for Data Modelers to connect to data from any source. Data Modelers can use Designer Cloud to reduce their reliance on IT to get access to the data they need to perform their job. This makes it easier to synthesize data models from source data and auto-map data to predefined targets.


Ensure data quality. Designer Cloud’s active data profiling allows Data Modelers to easily discover and validate data quality issues. Statistical data profiles are used to identify complex patterns, automatically suggesting possible quality rules such as integrity constraints, formatting patterns, and column dependencies.


Transform raw data into ready-to-use data. Data Modelers can use Designer Cloud’s visual interface and predictive data transformation suggestions to greatly reduce the time it takes to detect and resolve complex data patterns and transform them into robust and statistically valid models and statistical analysis.


Build, automate, and deploy smart data pipelines. With just a few clicks, Designer Cloud helps Data Modelers model data flows while managing relationships across data sets and recipes. They can operationalize and automate data flows through plans that enable parallel and conditional execution, as well as pre-and post-processing.

Interested in learning how Alteryx Designer Cloud can help your Data Modelers reduce the time, technical skills, and costs required to create data flows and accurate data models? Schedule a demo of Designer Cloud today.