A successful app starts with data modeling. This is the foundation for implementing business needs.
Your data model underpins your entire app.
However, designing a good data model is difficult. This complex process involves end-users, technical teams, and other business stakeholders.
Today, we’ll learn how to build a data model.
First, the essentials.
Table of Contents
What’s a Data model?
A data model abstractly describes how your program stores, connect to, organizes, and manages data. This includes your values, their source, and their structure.
A database schema is different:
The schema pertains to one source, whereas the data model covers all sources.
The data model focuses on data structure, not source specifics.
Build a data processing foundation for your project. Build your app’s automated processes and user interfaces from this.
Data models—why?
Why design a data model? All apps have data models. It’s only whether you’ve thought about making the best one for your needs.
This affects app security, functionality, performance, scalability, and usability.
Thus, a good data model from the start pays off.
Especially when obtaining data from sources within an application. External sources and an internal database may contain some of the information you require.
Establishing the data you need and where it will come from is crucial to data modeling.
A good data model ensures compatibility, performance, scalability, and security.
Here are the steps you may take to create the appropriate data model for your next app project, along with the critical considerations.
- Collect Business requirements
First, gather business requirements for data processing in your application. We don’t need specific variables yet, therefore requirements can be vague.
Gathering requirements involves determining what your app will accomplish and what data it will require.
To determine the app’s functionality, business stakeholders like end-users, decision-makers, clients, and technical colleagues must be consulted.
2. Establish workflows
Next, we may start defining our processes. Outlining the application’s behavior to events and triggers. This comprises system and user reactions.
Again, abstractly, without variables.
Logical data modeling is this step.
We’ll design a more concrete data structure with this information.
We can describe our desired processes in non-technical business jargon for now.
Our timesheet example could have several business processes:
All timesheets should calculate labor costs.
A relevant timesheet should notify project management.
Project managers should be able to track project costs and time.
Only employees should modify their submissions. Project managers should modify pertinent submissions.
Based on project timesheets, the system should invoice via external platforms.
These business rules are examples only. Logical modeling describes how our program will respond to user actions and any background operations.
3. Make a conceptual data model.
Create a conceptual data model next. This is a more organized data strategy for implementing the processes we identified in the previous stage. We’ll use non-technical business terms for now. Technical specifics follow.
A conceptual model is about structuring facts to achieve our goals.
First, choose our data’s broad entities. Then we may list what we need to know about each and start making basic connections.
Thus, our timesheets app entities must include:
Timesheets, Projects, Owners, Employees.
Your business may require more entities. Project tasks and other resources.
We’ll then sketch out each entity’s data. For instance, your employees’ personal information, hourly rates, project needs, budgets, and goals.
Finally, your conceptual model should show entity relationships. Each project has an owner, and employees can submit several timesheets.
4. Define entities and attributes
We can define our entities after identifying them. Translating each entity into a database table is the usual method. Each employee or project will be represented by a row here.
Each column will store an entity attribute. We must decide:
The variables we need, are their format, names, and rules.
Your schema will include a new application database if you build one. When considering sources for existing data, consider this.
5. Find data sources.
Your data model determines where values come from and how to store them for your app to work. Locating your app’s data sources.
Include:
Flat files, APIs, web services, internal and external databases, and other corporate assets.
These are our key data sources. Our finished app can query them to add or alter values.
Connectors
Choosing between new and old data sources is crucial here. Our data model can incorporate both.
Many methods can yield comparable effects.
Let’s brainstorm employee timesheet data source structures.
Building an internal database for the items we identified is the easiest approach. Since we’d have to build our own database schema, we’d have the most control over how our attributes and entities are stored.
This would also complicate platform integration.
We might connect directly or via API to an external database.
If a relevant database exists. This saves us from making our own. It integrates easily with other programs that query the same data.
Existing database schemas may not meet our demands. To format query outputs to our specifications, we’d need to change them.
We usually need both. Our timesheet app may pull personnel and project data from external sources yet save user inputs in an internal database.
We also have a database integration guide.
6. Link entities
We briefly discussed data model entity relationships. With a more technical perspective of our entities, we can construct more detailed relationships between them.
Here are a few things to follow.
First, each entity set needs the right relationship. Several choices:
- 1:1.
- Many-to-one.
- Multiple-to-many.
We must also choose which columns in each table to construct associations around. DBMS-specifics apply.
Each row in a SQL database must have a primary key. These unique values reference rows in other tables. Foreign keys are primary keys in connected tables.
Relationships must be established for multi-database data models. Building an internal database to query and store entities from diverse sources.
In the context of a particular field of study, the entities and relationships between them can be described using an entity-relationship model. ER diagram maker is a web-based tool for representing the interconnections between various entities (such as people or things) in a system.
The building blocks of any entity-relationship model are the entity types and the relationships that can exist between them.
7. Create a physical data model.
This provides specifics on internal database structure and external data source connectivity.
Creating a physical model involves applying the preceding processes.
If you’re designing your app’s database, this includes giving all attribute names, types, formats, integrity constraints, and other restrictions.
Database schemas are explained in our guide.
We must also consider how to connect external data sources to our app. Our app’s code can manually point to the source’s name, location, authentication details, and other information.
We can employ dedicated data interfaces with low-code tools and other emerging app-building platforms.
These customized interfaces connect to unique external data. Instead of writing code, you can enter information on a GUI.
You can then manually generate queries or import all accessible queries from the source, depending on the tool and data source.
8. Data normalization and integrity
To assure your app’s long-term validity, reliability, and integrity, you establish a data model. Avoid redundancy, conflicts, formatting, and more.
Data normalization can help.
Normalization merits discussion. This is a series of methods to avoid data redundancy and abnormalities.
Here are several methods.
The most prevalent is a data structure. The purpose is to construct things with a single theme or notion. If you’ve followed our recommendations, your data model already has this.
When a bunch of values applies to more than one row in a table, create a separate entity and use relationships to relate it to the original table.
This boosts performance and reduces storage.
In a personnel directory, each row may have a department name, department phone, and department head properties.
This would require us to duplicate data in the employee details table.
Listing or changing the company’s departments would be unnecessary.
Thus, creating a department table and linking each employee’s record is better. This simplifies queries, maintenance, and server burden.
9. Data model upkeep
Maintaining your data model is still a problem. This makes application scaling difficult. For instance, expanding your data set and adding app features.
You must adapt without damaging your data model.
Adding, editing, or changing objects, relationships, or properties is possible.
Make sure your data model is flexible. Adding additional functionality, entities, attributes, processes, or relationships.
You must be able to make necessary adjustments without affecting other data. Through transitive dependency, changing one characteristic may affect another.
To support a third-party tool, you may need to change an attribute’s format. Doing so could break other data model elements that use this attribute.
Preventive measures are essential.
Beyond this, you must provide sufficient server capacity for an increase in storage and users. Preventing data migrations requires adequate modeling.
