March 5, 2020

6 Data Modeling Techniques To Elevate Your Data Culture

6 Data Modeling Techniques To Elevate Your Data Culture

Everyone wants to get in on big data analytics. The urge to be more data-driven has been a goal of most organizations for the last decade. In a recent survey from New Vantage Partners, 55% of companies reported that their investments in big data now exceed $50M. Yet, 72% of survey participants report that they have yet to forge a data culture. How can companies close the gap to get more ROI out of their data investments?

You can start by utilizing the data your company already collects. Unfortunately, up to 73% of existing enterprise data goes unused. This happens for a variety of reasons, including limited data access, low data literacy, or simply not knowing what data exists and whether it’s relevant.

The good news? Investing in a good data model and analytics tool can help. Data models organize and present raw data in ways that are especially useful to analysts and domain experts. It’s a process that can inform—and possibly transform—business analysis in almost any area of your organization. By working with data teams directly, or providing self-service capabilities that allow power users to create data models independently, your company can increase data value and adoption. So who at your organization should participate in data modeling? And what techniques and how much do they need to understand?

Data modeling is necessary for organizations that want to analyze and understand the growing amount of data they own. However, data modeling techniques and best practices can quickly get complicated. Here’s an overview of the most common approaches to data modeling, as well as ways to make it as painless as possible for business experts.

73% of existing enterprise data goes unused.

Speak the same data modeling language

The data language barrier is real. To help close the gap, you have to make sure teams are using the same definitions and know some basic data modeling techniques before when interacting with the data team. These terms and definitions will help business teams better collaborate with data scientists.

  • Entity: A data entity is an object that exists in the real world. Entities include people, organizations, products, and the like.
  • Attribute: An attribute is a characteristic of an entity. Examples include color, age, and address. A key attribute is a unique identifier, like a student ID number. A non-key attribute, on the other hand—like a first or last name—can exist in more than one record.
  • Relationships:  Relationships are the dependencies or associations between entities.

Business leaders can use this know-how to delve into data models with greater transparency for more efficient conversations between teams.


Is the Data Language Barrier killing your company’s insights? Find out how you can get everyone on the same page here.  

data modeling techniques

Familiarize everyone with data modeling techniques and concepts

Types of Data Models

Many of the definitions, ideas, and techniques about data modeling vary even among data scientists. However, these three types are generally accepted as the levels or phases of data modeling, with each getting more complex than the last.

  • Conceptual: As implied by its name, this is an abstract data model. It’s a simple summary model at the enterprise or organizational level. This kind of high-level model is designed to be easily understood by business stakeholders. It primarily covers entities and the fact that relationships exist. You don’t even need to use a digital solution—you can sketch it on a piece of paper. It’s easy for non-data scientists to understand.
  • Logical: This kind of model builds on the conceptual model by adding the attributes (key and non-key) of those entities and their relationships. It’s still fairly easy for non-technical experts to understand.
  • Physical: A physical data model includes all entities, attributes, and relationships. It uses tables and columns tied to your database. These are more technical than the other types and may be more difficult for non-data experts to understand.

Data Modeling Techniques

Data and IT professionals have traditionally used standard data model techniques or frameworks that include best practices like:

  • Hierarchical data models are organized—as you would guess—hierarchically in tree-like structures.
  • Relational data models are sorted into relations via tables.
  • Network models organize data in a graph structure.
  • Object-based models include both object-oriented data models and entity-relationship models and only represent real-world entities.
  • Object-relational models are a hybrid between relational and object-oriented models.
  • Star schema models are star-shaped representations with the fact table at the center.

These data modeling techniques vary in flexibility, and the one you want to use depends on what you’re trying to learn. Some data modeling techniques are more useful when trying to create simple informational models, while others are better to use for more in-depth analysis. But these methods are evolving as more business stakeholders use self-service analytics tools. Domain experts need solutions that don’t require coding— or as much assistance from the data team.

What should business experts know about data models?

How much domain experts should understand about data modeling techniques depends on the solutions and approach your company chooses to take. Some solutions will require domain experts to work through their organization’s data team to build the model entirely. New approaches to data modeling include the use of solutions that lean on automation and visual interfaces to do the heavy lifting. These techniques make it possible for domain experts to play a more significant role in shaping data models— by effectively adding calculations, definitions, and overall business context to data sets.

The bottom line: everyone who plans to analyze data with a business intelligence tool should understand the fundamentals behind dataset organization, and where they need to go to find the correct data. This knowledge will help analysts and domain experts find the right data quickly, and effectively analyze it to generate insights that can improve their daily decisions.

Fortunately, data modeling techniques and concepts are more accessible than ever with tools like Sigma, so everyone who needs to access data can dive in themselves and contribute to building a better data culture.  

Want to know more about data modeling techniques and best practices? Get more information in our free definitive guide.


By taking a visual approach to data modeling with Sigma, business experts can play a bigger role. See for yourself

Rachel Serpa
Director of Content Marketing
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