3 steps for better data modeling with IT and data science

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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. Part of the reason for the confusion 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 like R and Python that end users and many IT staff have little to no knowledge of.

SEE: Job description: Big data modeler (TechRepublic Premium)

This technical abstraction of data models has led to a widespread fear of them among the ranks of users and IT business analysts; it’s a fear that has impeded the development of data models that truly address businesses’ end goals.

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 effective and accessible data models with varying levels of data modeling knowledge.

Jump to:

1. Define the business requirements

What is the business problem that needs to be solved by the data model? Is it an automated loan decisioning process? Or a recipe formulator for best ingredients to be used in cattle feed for a specific herd?

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. He or she doesn’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.

SEE: The different data model types and their uses (TechRepublic)

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 IT 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 he or she can communicate at a high level with IT. It’s also important for the business analyst to serve as a liaison back to the end user, assuring that the data model and any application development stay on course with the business use case.

3. Trialing and installing the results of data models

Once data models and applications are built, it’s time for the end user to trial 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 on and then installed in production.

Working together isn’t a huge leap

In many respects, the role that 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 trial and install the app in production.

SEE: Job description: Big data modeler (TechRepublic Premium)

While there might be some terminology and technology that 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. Especially with the number of simplified data science trainings and glossaries that exist today, business analysts can quickly get up to speed and effectively contribute to the data modeling process.

Read next: Top data modeling tools of 2022 (TechRepublic)