In recent years, organizations have been using data modeling techniques to improve their business processes. In the US alone, according to Statista, companies spent over 30 billion dollars on marketing data in 2021. Data scientists use data modeling methods to make sense of data and share insights with business stakeholders. Data is only valuable if you know how to interpret it.
There are many data modeling ideas you can use to get a blueprint of your data. This article will explain some traditional modeling techniques and modeling essentials used by data science professionals. Read this article if you want to know how to learn data modeling.
Best Data Modeling Technique Examples
Data analysts use data modeling techniques to interpret and present large data sets in a way that the stakeholders understand. Data scientists are in high demand, with predicted job growth of 22 percent over the next decade. Data analytics can help businesses grow, and it is important to learn the best data modeling techniques if you want to be a business analyst.
Hierarchical Data Modeling
Hierarchical data modeling uses tree-like structures to organize large data sets into hierarchical categories. A hierarchical model allows you to simplify a data set based on common characteristics between the data. For instance, if you are modeling the sales of different stores in a large chain, you could group stores based on the number of employees and geographical region.
Network Data Modeling
The network model is an alternative modeling technique to the hierarchical model. It can handle more complex relationships between data categories, but is more challenging for potential users because it is so complex.
Relational Data Modeling
A relational data model organizes data in master tables called relational databases. In this type of database design, you define the tables and the relationships between tables. Relational data modeling is one of the original traditional modeling techniques, and the entity-relationship model and dimensional data models were inspired by the relational model.
Object-Oriented Data Modeling
This modeling technique is a combination of object-oriented programming and the relational data model. An object-oriented model is a conceptual data model where an object represents a type of data. Each object has attributes and properties that define its behavior. This visual representation can help you understand your database better.
Star-Schema Data Modeling
Star-schema data modeling, also called dimensional data modeling, uses a single central fact table surrounded by two or more dimension tables that have different categories of data assigned to a fact. With many dimensional tables branching off of the center fact table, this database design looks like a star.
Ensemble Data Modeling
An ensemble data model combines two or more related but different analytical models to give you a single predictive model. Data scientists use ensemble modeling to get the benefits of multiple modeling methodologies, while reducing their individual weaknesses.
Model Validation
Model validation is used to check for performance issues in a predictive model. To validate a model, model developers feed models alternative data with known outcomes into the model and check the model’s predictions. A potential risk of not validating your model is making decisions based on bad predictions.
Graph Data Modeling
Graph data modeling is a network modeling technique. It is used for complex relationships in graph databases. A graph data model has nodes, data categories with a unique identity, and edges, relationships between data categories. Edges define how each node is connected.
How to Model Data
- Choose a data source. To start modeling data, you need some data. If you are modeling data for someone else, they should provide you with a data source, but if you are doing a project for yourself, pick a type of data you are familiar with.
- Select the data sets. Once you have chosen the source, it is now time to pick which data sets you are going to use. This is the data you will be putting into your model, so you have to determine which data is relevant to your overall question.
- Identify attributes and entity types. Now that you have your data sets, you need to identify the attributes and entity types for the data. Entity types are a collection of similar objects, while attributes are the characteristics of each entity type. An entity type can have more than one attribute.
- Use standard data naming conventions. Companies and organizations usually have a standard process when it comes to data naming conventions. Follow those business rules for naming your data so that other people can understand your data model.
- Identify relationships between entities. In this step, you will be analyzing which entities are interconnected and linked. Entities can have more than one relationship. You will also have to be careful in naming the relationships to avoid confusion.
- Apply a data model. Choose a data model to organize your database. Make sure you understand the benefits and limitations of the model you decide to use. Data science tools can help you with this step.
Data Modeling Ideas: Top 5 Tips to Master Data Modeling
Data modeling helps data analysts to come up with viable solutions for businesses and other organizations. Here are five tips to master data modeling. From the basic concepts of data modeling to the more complex model types, follow these tips to master data modeling.
Have a Clear Understanding of Business Requirements and Goals
The main goal of data modeling is to help a company, enterprise, or organization to have better business practices. If you are a data modeler, you need to have a clear idea of what your organization needs to deliver the best results. Knowing what the goal is will help you prioritize the right data and deliver the appropriate model to make data-driven decisions.
Visualize the Data You Need to Model
Handling big data sets can be overwhelming. Just looking at a table of values won’t help you understand your data. Learn data visualization techniques to explore your data. Use different kinds of graphs to help you see patterns and anomalies in your data.
Define Your Questions
As part of understanding the business requirements and goals, you should have a good understanding of what questions you are trying to answer. Your data model should be able to address these and provide coherent and relevant answers. The more specific your question, the easier it is to choose the right kind of model.
Verify Each Stage of Your Data Model
You should check and double-check your work at each stage before moving on to the next step. Check each attribute and all of the entity names in a dataset. Each step of the modeling process requires you to make decisions, so make sure you are making decisions based on facts and not errors.
Use the Right Data Modeling Tools
Having the right modeling tool will really help with data modeling. Many complex data modeling techniques require that you know how to code. Take advantage of data modeling applications by learning key data analysis programming languages.
Are Data Modeling Techniques Worth Learning?
Yes, data modeling techniques are worth learning, especially if you want a satisfying career in tech. The average data modeler reports job satisfaction of 4.2 out of five, according to PayScale. There are plenty of online data modeling courses to get you started.
Data Modeling Techniques FAQ
There are three common data model types, which are logical data models, physical data models, and conceptual data models. The conceptual model is a good summary tool for stakeholders and should be easy for people who are not data scientists to understand.
Data models focus on what relevant data should be stored in the database. The data model is used to design tables and determine the relationships between types of data.
Yes, data models do benefit consumers. Companies that use data models to make data-driven decisions and provide consumers with better products and services. Data models make communication easier between stakeholders and technical teams, improving collaboration within an organization.
A physical data model is the final stage of data modeling where you use the conceptual and logical data models to organize actual data into tables for analysis.
About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication.
