As a business leader, you should cultivate trusting relationships with your data teams. Doing so is crucial, as implementing data management initiatives and developing different data models may involve input from various stakeholders, including CEOs.
If you develop or implement new software solutions without ensuring that your business objectives align with data management strategies, you won’t be able to succeed in your industry and ensure tangible results for your organization, such as increased profits and higher customer retention and attraction.
In this blog post, you will learn about:
- the role data modeling plays in designing a scalable and reliable data architecture for your software system
- what business benefits you can count on if you follow a tried-and-true data modeling flow
- common data modeling examples and techniques
- tools you can use to speed up data modeling
- how Yalantis can help you with efficient data modeling
Explore time-tested data modeling best practices from Yalantis.
What is a data model and why is it important?
A data model is an easy-to-read graph that considers your business requirements and prepares the ground for designing the software application database. The data modeling process:
- involves drafting schematic relationships between core business processes and entities that will be the essence of a software application, such as drivers, a vehicle fleet, and orders in a supply chain management software solution
- begins at the requirements analysis stage prior to software architecture development
However, the value of data modeling is twofold. The more accurately you approach data modeling of a single application, the better you’ll be able to establish strong data management practices in your organization, as data modeling can help you better understand the condition of your cross-functional data assets. According to Piethein Strengholt, author of the book Data Management at Scale, data modeling is a crucial area of data management.
Below, you can see the data management pyramid created by Peter Aiken, author of the book Data Management Body of Knowledge. It reflects the typical hierarchy of data management practices in an organization.
Data governance, which includes policies and procedures for maintaining data integrity within the company, is at the base of the pyramid. The second level is about data architecture, data quality, and metadata management, which lay the foundation for the third level, which is devoted to data security, data storage, and data modeling. In turn, these directly affect data integration and interoperability capabilities, without which such important business initiatives as data analytics and business intelligence won’t function properly.
Later, we cover how you can build a data model and what stages this process involves to help you dive even deeper into data modeling specifics.
3 reasons why C-level executives should be actively involved in data modeling
Here are a few reasons for you to engage in the data modeling process with your data teams:
- Ensure that business processes within your data model align with your current and future goals. This is critical to avoid situations where, for example, the C-suite plans a complete restructuring of your finance department and the data model for the organization-wide application doesn’t account for this. Plus, if you participate in data modeling, you can ensure your application is scalable enough to fit your business expansion strategy and avoid costly changes during software development.
- Better understand data analytics results and insights. If you’re involved in the data modeling process at the initial stages of software development, you’ll have a more in-depth understanding of what data aspects and relationships are behind the results of your data analytics tools.
- Efficiently allocate resources and budget to data management practices. If you’re actively involved in every aspect of data management at your organization, including data modeling, you’ll have a clearer perception of how to reasonably allocate resources to foster data initiatives and increase the value of data at your company.
Developing software or managing data without a data model is similar to renovating an apartment without a detailed and accurate design. When remodeling, blueprints indicate where all the outlets will be, for instance, so you have enough outlets in the right places and you don’t have to make costly changes after all the work is done. By the same principle, an efficient and well-thought-out data model identifies all important data assets, establishes clear relationships between them, and helps you minimize costly changes during software development.
To better grasp how data modeling works, let’s cover what challenges you can address with a well-defined data model.
Prepare for streamlined software development with a well-structured data model.Explore our data engineering services
5 benefits of an efficient data model
Data modeling benefits vary depending on the organization’s size and industry. As we suggested in the title of this article, data modeling is your go-to for developing scalable and secure applications. At the data modeling stage, with the help of diverse tools and best practices, you can:
- lay the foundation for incremental growth of the data your future application can process while maintaining stable performance
- ensure that your data assets don’t corrupt each other and can be safely integrated with other applications integration with other applications is safe
However, the benefits of data modeling don’t stop at improved security and scalability. We can also speak of the following data modeling advantages for your business:
Enhanced data management and data governance. Data modeling allows organizations to optimally structure the data their software systems produce. A structured data model is the ultimate cure for chaotic and ill-organized data management.
Even though a data model can’t solve every problem, it still helps you be more aware of how your key business entities relate to each other and quickly find out if there are departments or processes in your organization that don’t properly structure the data they use and store.
Awareness of your data assets and their relationship helps you boost your data governance initiatives to maintain proper data integrity, quality, and usability within your organization. As a consequence, well-defined data use allows your employees and stakeholders to derive tangible value from various data assets.
Prompt recognition of data issues. Data modeling allows data architects and teams providing data engineering services to accurately define issues that can potentially complicate or disrupt the software development process, such as inconsistent, inaccurate, duplicate, or low-quality data. This data modeling benefit is especially crucial if a software application needs to be integrated with other corporate systems or third-party solutions and it’s important to ensure that data exchange between the solutions is accurate, timely, and seamless.
Accelerated time to market for your software solutions. With the help of a well-defined data model, a development team can have a clear understanding of the components that need to be built at each application development stage without frequently referring to business stakeholders.
Developing a detailed data model may take time as well, but the more prepared you are at the beginning, the quicker your product will be launched (and the more likely you are to avoid cost- and time-intensive workarounds during the software development process). After investing time and resources in data modeling, First Tech Credit Union Bank realized that building a robust data model up front helped reduce the project go-live time and allowed team members to create better business intelligence (BI) reports.
Improved project documentation and knowledge sharing. Data modeling requires active cooperation between business and technical stakeholders, ensuring that everyone is on the same page. Such alignment at the start of a project helps you to develop detailed and accurate project documentation to pass the knowledge to other company teams if necessary or use it as the basis for accelerated development of other software systems.
Preparation for data analytics and visualization. Data modeling is part of business intelligence services and advanced analytics solutions, as it allows business stakeholders to reach a mutual understanding of how data at their organization is organized. And the more accurate the data modeling process, the better and more business-oriented the data analytics. When you have a clear understanding of the relationships between your core data assets, you can set the right criteria for business intelligence and advanced analytics tools to generate beneficial insights.
Now that you have a general idea of what data modeling is and how you can benefit from its development, let’s move to learning more about different types of data models.
Learn how we implemented a streamlined data aggregation strategy for a healthcare solution.See case study
Types of data models: The simplest is always the most important
There are three data model types: conceptual, logical, and physical. These are called types of data models, but they are also considered stages of data model creation.
Conceptual data model example
This data modeling type is frequently overlooked and considered insignificant due to its simplistic nature. But conceptual models are exactly what set the tone for the entire data modeling process. Developing conceptual data models involves data architects and critical business stakeholders providing high-level business concepts that describe their business processes, relationships between those processes, and the main entities included in them.
For a conceptual data modeling example, let’s take an enterprise that needs to develop an application for their manufacturing department and starts by outlining key concepts for the application’s data model. The concepts could be material, product, bill of materials (BOM), assembly line, employee, supplier, and purchase order. There could be more entities depending on the type of applications, objects, and user roles involved. After defining key entities, the next step is establishing relationships between them.
Logical data model example
Developing a logical model begins with constructing an entity relationship diagram (ERD) that specifies the relationship between entities in the conceptual model in greater detail. Data in a logical data model can already be organized into tables, columns, and rows. Relationships between entities in the logical data model can fall within the following categories:
- Many-to-many, when multiple elements in one data table refer to many elements in another table. For example, many teams can work on diverse projects, and projects can involve many team members.
- One-to-many, when there is one parent element connected to many child elements, such as one customer who can place several orders.
- One-to-one, when one data element is linked to only one element in another table. For instance, each product can have only one BOM document.
It may seem that thinking through all possible relationships between your data entities is a relatively easy task, but it often takes lots of time to unveil all possible and hidden dependencies. If you want to create a software system that engages two or more departments (such as manufacturing and supply chain), you’ll have to work with many more entities.
And this is when it can get difficult and confusing, especially if certain entities exist across departments. For example, both the manufacturing and supply chain departments may have an order business entity, but it can have a different meaning for each department. For manufacturing, it may refer to an order placed with a supplier company to deliver materials, while for the supply chain department, it may refer to an order placed by a customer.
Physical data model example
A physical data model more closely resembles your application’s database than previous models do, and it’s better not to to avoid building it.
A physical data model defines all columns, tables, relationships, and dependencies between datasets — as well as the context for recording and storing data — allowing data architects to design a complete database for the application. Mainly, a physical model answers the question: How do you implement a database tier in the application architecture?
Creating conceptual, logical, and physical models is only the beginning of your data modeling journey. In the next section, we’ll learn what’s next.
Data modeling techniques, or how to ensure smart data organization
Once a physical data model is created, it’s up to the data architects to combine those tables and columns in a way that corresponds to your business requirements and supports your business operations. Here are a few typical data modeling approaches.
Relational data modeling
In a relational model, data is organized in tables, columns, and rows for efficient and simple retrieval. This type of model fits applications with complex many-to-many relationships. Relational data modeling is the most common approach, as it reveals how entities in one table — such as all data about a customer (customer ID, address, state) — relate to data in another table (such as the orders table).
This model uses a range of keys to organize data. Primary keys (PK) indicate what entities one table includes, and foreign keys (FK) are used to show relations between two separate tables.
Such common systems as sales management software, supply chain management systems, hospital management systems (HMSs), content management systems (CMSs), and customer relationship management (CRM) systems use a relational data modeling approach for efficient data organization.
Hierarchical data modeling
As the name suggests, hierarchical data modeling allows data architects to organize data entities with a parent–child relationship. In such a data model, child nodes can also become parent nodes for their child nodes, but there is always one primary parent node for each set of child nodes. For instance, this type of data modeling suits applications that need to include complex organizational structures, such as all company departments with the board of directors as the entity responsible for all of them. The hierarchical data modeling approach also fits when building file systems with strict subordination.
Hierarchical data models are considered rather inflexible and have a more difficult procedure for data retrieval than relational data models. If you want a more flexible approach, you can choose a relational model.
Network data modeling
The third data modeling approach we’re going to cover is network data modeling, which is an improvement to rigid hierarchical modeling. Network data modeling allows each child branch to have multiple parent nodes. Thus, a one-to-many relationship in a hierarchical model evolves into a many-to-many relationship in a network model.
This type of data model is a high-level graphical representation that consists of boxes and arrows instead of a complex system of tables and columns as in a relational data model. Boxes indicate data records and arrows relationships between them.
The network data modeling technique is suitable for building a complex organizational structure, indicating more relationships between entities than a hierarchical data model. Such models are simple in nature and easy to read, as they look like diagrams but still lack the flexibility of relational data models.
Object-oriented data modeling
This data modeling approach is based on the principles of object-oriented programming and works especially well with programming languages such as C++, Java, and Python. However, the major downside is that this data modeling technique isn’t equally productive when combined with other programming languages.
The critical principle of object-oriented data modeling is that instead of defining entities and their unique properties as with relational data models, data architects first define entities that have common properties. Data and real-world actions that can be performed with it (methods) are stored in a single entity, known as an object.
The aim of the object-oriented data modeling approach is to save development resources by making a concise data model and reusing its components whenever possible. Object-oriented modeling can be used in many scenarios, but this approach is particularly beneficial if your application needs to be developed in an object-oriented programming language with a focus on a code-first software development approach (when application code to implement functionality is developed prior to the software architecture and other documentation).
In this section, we’ve covered the most typical data modeling techniques, but there are many more that spring up as the result of evolving digital technologies. Plus, there’s the possibility of building hybrid data models that combine several techniques. Which data model is best for your project depends on the unique functional and non-functional requirements you’ve defined for your software system.
Let’s move on to software solutions that can facilitate the data modeling process.
Get step-by-step guidance on how to develop an enterprise data warehouse with data model examples.See the article
Tools and software that foster data modeling
The software development market boasts lots of data modeling software and tools that simplify this process, making it more efficient. We’ll discuss five common solutions.
Lucidchart is a visualization tool suited for building data models and software architecture. It is easy to understand and supports shared real-time collaboration for development teams. Most of its features are available for free, and Lucidchart is a perfect tool for quick data modeling that’s understandable for technical and non-technical stakeholders.
Erwin Data Modeler is the top pick for many companies across diverse industries, as it also includes metadata management, data governance, and data intelligence. In fact, Erwin Data Modeler boasts the widest range of features compared to other tools we list here. Users can easily customize the data modeling canvas to their needs and use only those functionalities that suit them.
ER/Studio boasts an extremely convenient collaborative environment and provides lots of scalable opportunities for enterprises of all sizes. ER/Studio offers efficient functionality for data architects such as forward and reverse engineering. Thus, this tool allows for end-to-end database modeling along with creating a data architecture. ER/Studio also has a wide variety of free tools that help you simplify data modeling without making any financial investment.
Toad Data Modeler is another convenient tool for data architects, as apart from standard data modeling features, it offers separate solutions to maximize integration with various databases. Toad Modeler also allows end users to generate extensive reports based on data models or data architectures.
You should discuss types of data modeling solutions with your technical teams to discover the service that best fits your needs, your business model, and the skills of your data architects and database administrators.
If you have a software solution in mind and are unsure where to start, composing a data model can be the perfect way to structure and visualize your abstract ideas and assess their feasibility. Yalantis’ expert team can choose fitting data modeling tools, apply data modeling best practices, and assist you in quickly and effectively achieving your goals with well-organized software solutions.
How does Yalantis approach the data modeling process?
We begin with getting to know your business, its internal processes, and external connections. Then we elicit requirements for your software project, which can include functionality you want your software to have or integrations that are necessary, and compose a comprehensive list of those requirements to hand over to our data modeling experts. The more detailed the requirements you share with us, the more accurate the data model we can build.
How does data modeling relate to data governance?
Data modeling is directly related to data management and data governance. If you have data governance procedures in place such as regular data quality checks, data modeling will go smoothly, without any risks for your business.
What are common mistakes in data modeling?
The most common challenges include miscommunication between key stakeholders, leading to costly workarounds and frequent changes in the data model design. Building a communication plan including all critical stakeholders would be an optimal solution to this challenge. Another common challenge is overcomplicating a data model with too many dependencies, relationships, and entities. You should analyze the feasibility of your data model during its development to avoid making costly changes at the software architecture level. The last mistake is developing a data model based on assumptions in unclear situations. Each decision in data modeling should be justified and backed up by a clear business need.
Ensure a solid start for your software project
Design a fitting data model
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