Data modeling plays a vital role in the growth and overall success of many businesses. Since businesses generate enormous quantities of data—especially with the advent of technology like the Internet of Things, customer reviews, and chatbots—it’s critical that enterprises organize their data in ways that make structural sense, and are scalable and understandable so that they can be leveraged to drive real business impact.
Given the inexorable incoming tides of using data for machine learning and artificial intelligence, success with analytics projects heavily depend on well-built data models.
As with any large organizational project, it’s important for IT and the rest of the business to be aligned prior to actually designing and building a data model. The time-honored methods of ensuring that all teams strive to achieve a solid foundation of collaboration and coordination to steer key performance indicators (KPIs) are paramount.
Each group should have a deep understanding of the other’s goals, desires and drivers, in order to generate real business impact. This way, your data modeling is set up for success – no matter the area of mastery or skillset of those involved.
What Is a Data Model?
Simply put, a data model is a way of organizing disparate data elements while keeping track of how those elements relate to each other. A data model consists of entities—high-level buckets like products or customers—and attributes—details about those entities, like customer addresses or product descriptions.
Three flavors of data models
In general, at a high level, we can think of three categories of data models:
- A conceptual model organizes the data in a way that’s independent of how it might be used or specific technologies. This provides an overview of business’s data, as well as allowing the data to be harvested and used for other purposes.
- A logical model codifies the structure of the data in terms of features like relational tables or XML tags. Thus, unlike the conceptual model, the logical model is not technology agnostic. You can think of this as a graphical way of representing data relationships.
- A physical model describes and organizes the data using database components. It includes things like column names and how different tables relate to one another. Essentially, it includes all of the details that the data needs to live in a database structure.
Another way to think about types of data models
In addition to the above (a more abstract way of thinking about the details that data includes in different kinds of models), we can also explore the models that businesses use based on what they want to accomplish.
- A hierarchical model organizes the data into a tree-like structure. This model proposes that each child record has only one parent, while each parent can have many child records. It’s a simple, rigid structure, but it can help illuminate relationships between different data points.
- A network model is similar in structure to a hierarchical model but, unlike hierarchical models, records can have more than one parent.
- A relational model represents data in tables and allows users to describe the data with their own queries, while the database management system takes care of storing and retrieving data.
- An object-oriented model is a collection of ‘objects’ that use real world terminology to describe the features of the data, just as object-oriented programming does.
- An entity-relationship model represents entities and the relationships between those entities. You can think of it as being similar to a hierarchical or network model but describing more than just the parent-child relationship.
4 Key Data Modeling Techniques
So how do you actually use these different kinds of models in your organization? Here are the top four techniques business analysts and data modelers use to help drive impact.
- Entity relationship diagrams show the relationships between the entities, and, in some cases, help to illustrate how business-related concepts interact. By looking at the relationships as a diagram, database modelers can develop a deeper understanding of the system.
- Development of a data dictionary, which provides information about the attributes of the data model, including names, definitions, and other elements that are captured in a database, can be used to help better understand where the data is coming from, what it looks like, and how it might be used.
- Data mapping, which can be thought of as a specialized version of a data dictionary, is used during a data migration project or when merging two disparate existing systems. In a data mapping scenario, data attributes are mapped across different data sets in a way that different labels can easily be merged.
- A glossary, which acts as the source of business truth to ensure that people are on the same page when discussing various data attributes and entities of the model. In general, a data glossary is owned by the business, not the IT-focused team. And it’s a good practice to have only one glossary, even if there is more than one data dictionary.
How to Prepare for Better Data Modeling
If you’re about to embark on a data modeling mission, here are some ways to get prepared.
- Develop a deep understanding of the business, the requirements and goals, and the planned use of the results of the data modeling mission. The first parts of this are pretty straightforward—meet regularly with business-focused colleagues and leaders to learn and understand their drivers, obstacles, and success metrics. How the data will be used is trickier, as requirements and goals may change. This means that it’s incumbent on the data modeler to design the model in a way that makes it agile and able to adapt to shifting conditions.
- Rather than a huge spreadsheet or otherwise flat screen of data, many data modelers and the businesspeople they’re working with find that it’s often easier to visualize the data in a graphical format. This can help ensure that the data is clean, complete, and consistent.
- Filter your data for the initial modeling to only use what you need, rather than everything that’s available. As Einstein is reputed to have said, “everything should be made as simple as possible, but not simpler.” The larger the dataset, the slower the query responses will be, and the more likely you’ll get lost in irrelevant minutiae.
- When you’re setting up your model, make sure that each step in the process is checked prior to moving on to the next step, because once the data grows, it’s harder to correct errors.
The underlying goal of building a data model is to make your organization more successful. With that in mind, it requires a deep understanding of key business objectives, as well as technical and database knowledge. Armed with that, your data modeling mission should achieve great success.
If you’d like help getting started with a new data project, sign up for a free AI Assessment—we can help you figure out what would have the most impact on your business.