3 Data Modeling Best Practices for IT, Data Scientists, Business Analysts

A good data model accounts for the context of the business process it must address and then addresses that context with the types of data that are needed. But an effective data model doesn’t always come that easily. Strategies for and a general understanding of how data models work have been up in the air for many years. For too long, these models have seemed to be the abstract province of data engineers and data scientists only.

To change this mindset, business analysts must get directly involved in defining data models, but they don’t have to do this work by taking data science and programming classes in their spare time. In this guide, we explain how IT, data science and business analyst teams can work together to create accessible and effective data models with varying levels of data modeling knowledge.

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What is data modeling, and how does it work?

Data modeling is the process of visualizing an information system to identify relationships between data and help organizations understand how they use data. Although often intertwined, it’s worth noting that data modeling and data analysis differ, as data analysis is centered on using data to drive business decisions. Data modeling focuses on how data is structured, related, stored and retrieved and is typically approached through steps:

• Requirement analysis: Understanding the data requirements of an organization or project before creating a model is a must, which can be done through discussions with stakeholders.
• Conceptual design: Creating a high-level overview of the organizational data that focuses on entities, their relationships and data flow.
• Logical design: Refining the conceptual model by adding details and attributes to entities and further defining relationships.
• Physical design: Translating the logical model into a physical design for a specific database system, with considerations for factors such as storage and performance.
• Implementation: Implementing the physical model using a database management system, whose elements are created based on the physical design.
• Maintenance and evolution: Regularly updating and refining the model based on changing business needs to keep it relevant and efficient.

Part of the reason for the confusion and perception associated with data modeling is that data models are always being discussed in the technical, or physical, structure of the models. By physical, I mean the technical names of data elements and datasets, the technical names for databases and data transformations, and the jargon of programming languages such as R and Python that end users and many IT staff have little to no knowledge of.

SEE: Discover the responsibilities of a big data modeler with this job description from TechRepublic Premium.

Data modeling tips for analysts working with IT and data science teams

This technical abstraction of data models has impeded the development of data models that truly address businesses’ end goals. However, there are a few ways IT, data science and business analyst teams can deal with these misconceptions and improve their data modeling.

1. Define the business requirements

What is the business problem that needs to be solved by the data model? The business analyst is best equipped to work with users and visualize the business process and data that are needed. The analyst can also describe those needs in plain English.

What should result is a logical data model, usually in the form of a bubble chart, that shows the different data needed and an accompanying narrative that explains how the data must be processed.

While doing this, the business analyst remains focused on what the business needs. They don’t need to be concerned about which datasets, systems or programming modules must be used to make the business model happen. Through this kind of work, the business analyst makes valuable contributions to a data model that will accurately reflect business goals.

2. Work with IT and data science

Once the logical chart of data bubbles is developed, along with a narrative of what needs to happen in processing this data, the business analyst will meet with IT or data science colleagues. These are the people who transform the logical data model into a physical model that defines the data stores, system internals and programs that need to be written in technical terms.

IT engineers and data scientists require this physical data model to do their work, but the demands on the business analyst are less. The business analyst only needs to have a working knowledge of technical terminology and processes, so they can communicate at a high level with IT.

It’s also important for the business analyst to serve as a liaison for the end user, assuring that the data model and any application development stays on course with the business use case.

3. Test and install the results of data models

Once data models and applications are built, it’s time for the end user to test them. During this process, the business analyst plays a critical role, functioning as a liaison between users and IT and data science professionals.

At this stage in data model development and application, analytics applications are fine-tuned, signed off and then installed into production.

Working together isn’t a huge leap

In many respects, the role business analysts play in data modeling doesn’t substantially differ from what analysts have historically done. Analysts define user requirements for applications, articulate a basic business design, shepherd the process through IT, and ultimately test and install the app in production.

While there might be some terminology and technology business analysts need to master for data model discussions with technical personnel, getting to know the fundamentals and the vocabulary of data modeling isn’t daunting. With the number of simplified data science training and glossaries that exist today, business analysts can quickly get up to speed and effectively contribute to the data modeling process.

Read next: Put your knowledge to the test using one of these top data modeling tools.