Data Modeling Techniques and How To Integrate Them
The number one objective of data modeling techniques is to help your team easily and quickly extract information from databases to make better business decisions. The modeling process identifies data relationships and workflows, helping you get rid of bottlenecks and speed up development.
Because organizations are collecting ever-increasing amounts of information from different sources, they often struggle with structuring relationships between data points. Failing to structure information relationships in a useful way makes analyzing data challenging. Shaping information with data modeling techniques helps analytics provide the answers your business team needs to make fast, accurate decisions.
Understanding your business requirements allows you to select the data modeling techniques and best practices suited for your situation. Perhaps you’ve experienced these problems:
- Ad hoc requests from the business side overwhelm your data team.
- Frustration grows in your business team because it takes so long to get bits and slices of data.
- A mess of spreadsheets and manual data exports is confusing and time-consuming for your business teams to sort through.
Knowing the different data modeling techniques, their strengths and weakness, and when to use each will help you plan the best data management strategy for your business to create a speedier, more accurate analytics process.
What Is Data Modeling?
You could think of data modeling as using text, boxes, lines, and symbols to create a simplified diagram or flow chart of a software system and its data. You map out the workflows and connections within the database.
Simple data model diagrams show three elements:
- The data an organization accumulates
- What section of the organization gathers the data
- The relationships between the data in each section
Why Do We Use Data Models?
Data modeling is a crucial step in designing any business system. Without upfront data modeling, it’s challenging to define a raw data schema to support analytics applications. Companies can struggle to accomplish goals because they lack the precise information needed to make critical decisions in time.
A data model’s goal is to aid a better understanding of the types of data you’re using, the relationships between information, and the organization of the data. Data models supply a blueprint for designing or reengineering a database, enabling businesses to optimize data and streamline information flow to satisfy their needs.
Creates a Visual Representation of Business Processes
Relationships between entities in a database become complex very quickly. A pictorial model like an entity relationship diagram (ERD) helps companies spot opportunities to improve operations and understand markets and customers better, increasing their competitive edge.
The visual representation data modeling creates allows even non-technical teams to understand business processes better.
Prevents Performance Issues Like Redundancy or Inefficiency
Different departments within organizations usually collect different data sets, but there’s often overlap. Redundancies and inefficiencies can easily occur. But when all departments are working with the same data models, it’s easy to spot and correct these before they become major issues that can distort the analytics.
Redundancies and inefficiencies can also occur if all the developers in an organization aren’t working from the same data model.
Assists in Achieving Quicker Time to Value
Using data modeling techniques, companies can reduce the time required to bring products and services to market. A good data model will reveal bottlenecks businesses can eliminate when deploying data strategies.
Because data modeling helps you define business problems and plan your data collection process, you streamline the data flow and enhance data quality. By creating relationships between data in the data model, you can expedite analytics, getting better information faster.
Serves as a Simple Communication Tool
Trying to make sense of the immense volume of data in a database by looking at it is enough to make anyone’s brain shut down. Attempting to extract valuable insights from that data by working with the relationships within the database or with other databases adds even more confusion.
But when you break the data down and represent data segments and relationships in illustrations using boxes, lines, and words, you get business-oriented picture stories that are easy to take in at a glance. You can also get detailed technical specifications that keep all team members using the same guidelines as they design and build a system or modify an existing one.
Data modeling facilitates collaborative discussions and workflows between database analysts, business analysts, and developers. Organizations with poor data modeling methods often have to go back to square one and create a better data modeling strategy to improve accuracy and reduce costs.
Types of Data Models
Different types of data models each have their roles and strengths. There are three basic types of data models that businesses find helpful:
- Conceptual data models
- Logical data models
- Physical data models
Understanding that each database can have multiple data models representing different relationships is critical. As you move through creating each data model type, you’ll gain a more granular view that will result in working models that will guide your development team.
It’s also helpful to be aware that different users need different views. A C-suite executive may only be interested in the primary relationships presented in a conceptual model. At the same time, a data analyst will want to dive into the nitty-gritty details provided in more advanced models.
Conceptual Data Models
Conceptual data models show you what is in the system. They display entities and relationships well but have low levels of detail. The conceptual model’s simplified view of your business processes helps you understand how the entities in your organization and database work together. These entities could include employees, suppliers, products, projects, customers, and more.
In this model, you can define the columns in tables and include essential information to identify and map relationships between tables. Business stakeholders and data architects use conceptual modeling to organize and illustrate concepts and business rules.
Conceptual Data Model Example
An order management system for a retail or wholesale business could use a conceptual model starting with a customer being represented in the first entity. Below the customer are two more entities: one for the customer’s order and one for the invoice. Additional orders or invoices require additional entities.
One customer can have multiple orders and invoices, but each order or invoice can have only one customer.
Logical Data Models
Developing a technical map of rules and structures is the goal of the business analysts and data architects working with logical data models. How your team should apply the system is the focus of these models that contain a medium level of detail.
You gain insights into how data is harvested and stored in tables and columns and their relationships. Logical data models go beyond the conceptual model to present detailed information for coders to work with.
Logical data models give you more knowledge regarding the tables, the columns in the tables, and the relationships than conceptual models show.
Logical Data Model Example
The relationship between a customer and their address can be shown in a logical model that uses strings to present more detailed information. The entity for the customer’s name could include strings for first name, last name, phone number, and email address.
To give customers the option for changing addresses or using two addresses, there are two address entities below the customer entity. Each adress entity wil have strings representing address line one, address line 2, city, state, country, and postal code.
Physical Data Models
Physical data models show how you will apply the system in a particular DBMS. Data architects and developers can design and build that system using the high level of detail presented. Physical data models display specific information about column types, table names, the location of data elements, and more.
A physical model enables all team members to use the same information in their development work. The benefit is avoiding duplications and conflicts.
Physical Data Model Example
Relationships between tables containing high levels of detail are shown in physical data models. Let’s use a doctor appointment for an example. At the top of the model are two entities each containing detailed information.
The patient entity contains strings showing patient ID number, name, email, and phone number. The doctor entity contains strings for doctor ID, name, and phone.
Both entities have lines going to a single appointment entity below them that contains strings for appointment ID, patient ID, doctor ID, room number, and time.
Each step in the data modeling process presents you with an extra level of detail represented by a different data model type as you move through the models from the conceptual to the logical and finally to the physical working model. Now that you know the most widely used data models let’s go on to the types of data modeling techniques.
Basic Data Modeling Techniques and How To Use Them
Understanding the four most widely used types of data modeling techniques and how to use them will help you choose the best modeling tool to organize your information. Choosing the model that matches your goals and information type brings many benefits, but using the wrong model can create performance issues.
The purpose of relational database techniques is to gain an understanding of how information is related between parent-child entities. Organizations commonly use it to store detailed information about an employee or customer like their phone number, address, birth date, orders, and much more.
Because the data structures are separate from the physical storage structures, it’s easy to edit information without making structural changes to the database.
Advantages of relational data modeling include:
- It’s simple to store multiple entries in one table, like all the purchases by a single customer
- It’s easy to use SQL queries to extract information such as customers with outstanding orders
- You can create complex reports using data from many tables
- You can maintain security easily
- Users need little training to use relational databases
Disadvantages of relational data modeling include:
- It can be challenging to map objects
- Data integrity is difficult to ensure
- It’s not suitable for big databases
- Joins are needed to report some relationships, which can slow queries
Relational databases are one of the most commonly used ways to work with information.
Typically, hierarchical models don’t have too many relationships. They identify overlap between tables as they organize complex information sets for reporting purposes.
A hierarchical data model shows parent/child relationships in treelike structures using one-to-many relationships. Boxes, or entity types, can be a parent, child, or both. Each parent entity can have multiple child entities as you classify the data into smaller subsets, but each child can only connect to one parent.
Advantages of hierarchical data modeling:
- Easy to add or remove information
- Supports systems that have one-to-many relationships like an organizational employee structure
- You can quickly identify the number of records in an entity
- Easy to report on parent/child elements
- Easy to understand relationships between tables
Disadvantages of hierarchical data modeling:
- No support for many-to-one or many-to-many relationships
- Requires data to be stored in many entities repetitively because the structure is treelike
- Forces slow sequential searching as the DBS must sort through the entire model from top to bottom to find information
- Difficult to access detailed information about child entities because the many joins would slow down the system
Entity-Relationship (ER) Data Modeling
The popular ER data modeling technique is a high-level data model that defines elements and relationships. It’s useful for representing real-world objects such as customer records. Companies commonly use the ER model for designing business intelligence solutions.
Advantages of ER data modeling:
- Easy to convert to any other data model
- High level of abstraction
- Easy to understand because it uses clear diagrams
Disadvantages of ER data modeling:
- Requires a complex understanding of data structures
- There can be a loss of information contents
- Limited ability to display relationships
Dimensional Data Model
Data warehousing often uses dimensional modeling techniques to speed information retrieval by breaking large information sets into smaller, more manageable chunks. Fact tables use rows and columns to group information about a subject.
Dimensional models contain a set of two entities:
- Facts that are measures of events like sales returns
- Dimensions like who, what, when, where, and why
You can meet reporting needs by constructing star schemas using Kimball dimensional modeling. These models can perform number-based information analysis super fast, like annual sales by region or by a team.
Advantages of dimensional data modeling:
- Querying is fast and easy because dimensional models use fact tables
- Groups business information clearly and simply
- Creating detailed reports using customer-specific information is easy
Disadvantages of dimensional data modeling:
- Changes in one area can ripple through the entire structure, making it difficult to maintain
- Highly risky in case of a security breach.
Break Down Complex Structures Into Helpful Models
Many data model types and formal modeling techniques can help people working with data easily visualize structures and relationships within databases. By examining three data models and four techniques, we’ve hopefully covered the question, “What are data modeling techniques?”
The main purpose of creating different data models should be to make complex databases easy to understand and extract useful information by using simple visual mapping with boxes, lines, arrows, and text.
If you don’t have the time or expertise to develop data modeling techniques, Canvas makes data easier for everyone by providing a clear, fast way to turn information into customer revenue. You can have a data strategy that works in minutes instead of waiting months for experts to design and create it. Talk to Canvas today.