Data modeling vs. data analysis: A breakdown of their differences

What’s the difference between data modeling and data analysis? Which is the right approach for your next project? This guide helps answer those questions.

While the terms data analysis and data modeling are often intertwined, they are two different concepts. Simply put, data analysis is about using data and information to drive business decisions, while data modeling refers to the architecture that makes analysis possible. In other words, data modeling and data analysis work best when they are used together.

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

But how do organizations embed data into every decision and process? The answer starts with effective data modeling and continues with data analysis. Let’s compare the two concepts below and learn how overlapping them can benefit your business.

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What is data modeling?

Data modeling is a data strategy that focuses on transforming raw data into structural, often visual representations that help analysts derive more meaningful insights from the data.

Data modeling seeks to map out the types of data your organization uses and where it is stored within systems. Additionally, it illustrates relationships between data types and finds ways to group and organize data by establishing formats and attributes.

“A data model can be compared to a roadmap, an architect’s blueprint or any formal diagram that facilitates a deeper understanding of what is being designed,” analysts from IBM said.

Companies must build models around business needs, translate business needs into data structures, create concrete database designs and be ready to evolve as businesses change.

Types of data modeling

These are the three most common data model types:

• Relational model: Stores data in fixed-format records and arranges data in tables with rows and columns. Basic relational approaches define raw data as a measure or a dimension.
• Dimensional model: Less rigid and structured, the dimensional approach favors a contextual data structure related to business use or context. This database structure is optimized for online queries and data warehousing tools.
• Entity-rich model: These are formal diagrams that represent relationships between entities in a database. IBM explains that data architects use several ER modeling tools to create visual maps that convey database design objectives.

The three levels of data abstraction

• Conceptual data model: The vision or roadmap. This layer represents the overall structure. This is where data modeling usually starts by identifying data sets and data flow through the organization.
• Logical data model: This is the second layer of abstraction and goes into more detail about the data model. It outlines data flow and database content.
• Physical data model: This layer defines how the logical model will be applied to the actual data set. Using this layer, IT teams create the real database structure, as well as the hardware and software, to support the plan. Multiple physical models can be derived from a single logical model if different database systems are used.

What is data analysis?

Data analysis is a holistic data strategy that involves examining, interpreting, cleaning, transforming, migrating and modeling data to extract useful information for internal and external business goals. While data modeling creates the architecture that helps data teams derive valuable data insights, data analysis actually puts the model in motion and leverages data to drive outcomes.

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

Some of the most common data analysis approaches include:

• Statistical analysis
• Inferential analysis
• Diagnostic analysis
• Data mining
• Predictive analysis
• Prescriptive analysis

The data analysis process

• Setting priorities, goals and targets: Companies that are first starting their data analysis journeys usually begin by asking what problem they are trying to solve. What are the business goals surrounding data analysis efforts?
• Gathering raw data: Organizations move to collect raw data that might answer those questions or support progress toward meeting data-driven targets.
• Data cleansing: Data is cleaned and checked for quality, ensuring the data is “fit for business use.” This means the data must have no duplicates, anomalies, or inconsistencies; be safe; and be correctly formatted.
• Data analysis: Once data is cleaned, it is analyzed to look for data patterns, trends and relationships. Analysts should try to spot opportunities and risks in the data at this time. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash and Microsoft Power BI.
• Data interpretation: Data analysis results are interpreted and presented to anyone working on data-driven tasks in the company. Results are also verified at this stage.
• Data visualization: Data visualizations or presentations use charts, graphs, maps, bullet points and a host of other methods to deliver easy-to-understand insights to a variety of company stakeholders.

The main differences between data modeling and data analysis

Data modeling and analytics are both integral to data management and data-driven operations. Organizations on a data transformation journey cannot choose one over the other but have to engage in both concepts to fully develop data architectures and use their data to improve their operations.

SEE: Top data modeling tools (TechRepublic)

As mentioned, data modeling is the roadmap and blueprint that is used to build the hardware and software where databases will be connected. Then, data analysis comes into play once the model is built and is strictly concerned with using that data to improve decision-making. It relies on the infrastructure that data modeling provides, but data analysis itself is not concerned with changing data infrastructure.

For effective data-driven businesses, data modeling and data analysis share a lot of common ground. They must both be aligned with business goals and priorities. Additionally, both are part of a strong data culture. When they are used together, companies can serve customers better, increase sales, make better decisions, meet governance and privacy standards, and ultimately back up all business decisions with higher-quality data.