With ever-growing technology and varying customer demands, businesses seek the best software development practices to delight their customers. Agile concept of modeling harness the transition for the competitive advantage for the customers. Agile methodologies promote continued and sustainable development while ensuring improved collaboration between software professionals and business stakeholders. Agile data modeling has gained immense popularity over the years as it can be performed most iteratively. To learn further, it is important to understand the data modeling process. So, let’s begin…
What is Data Modeling?
Data modeling is the approach to creating the orientation of data structure, rules, and relationship that matches your data management system. It allows your team to develop, design and manage the data assets more logically and consistently. But, it is not as easy as it seems to incorporate data modeling for your agile development projects. This blog will give you a clear insight into how to incorporate data modeling into agile development.
Well, data modeling techniques are performed in diverse ways, which include-
- Logical data modeling
- Domain data modeling
- Physical data modeling
- Architectural data modeling
- Dimensional data modeling
- NoSQL data modeling
So, how to choose the best data modeling approach in the iterative environment? Choosing the appropriate data modeling techniquescompletely depends on the scope and complexity of your information management project. It is possible to use different data modeling approaches for similar goals. For instance, you can use logical data modeling to apply the keys and attributes of entities, conceptual data modeling to understand the major business concepts, and physical data modeling to optimize the storage, performance, and security of sensitive data.
Besides, you can use NoSQL data modeling to organize semi-structured or unstructured data and dimensional data modeling to meet the analytical as well as reporting requirements.
Thus, it is clear that you need to choose the data modeling approach that suits your project goals, technologies, and data sources the best.
Importance of Data Modeling in Agile Development
Data modeling is highly effective and compatible with agile methodologies. It supports changing business requirements, delivering intuitive software, and collaborating with stakeholders and customers. To incorporate data modeling in agile development, it is recommended to use any flexible data model that fits the MVP criteria and then conduct refining based on the customer feedback.
Besides, it is helpful to use certain tools that support version control, collaboration, and automation. These tools include but are not limited to, Hackolade, ER/Studio, or SQL DBA. Also, it is important to apply the concept of modelingstandards and quality checks to rest assured about the integrity, consistency, and accuracy of the data model.
How to Incorporate Data Modeling in an Iterative Environment?
Data modeling is typically incorporated in agile development processes such as Kanban and Scrum.
In the initial phase of the agile development process, define the project objectives and future scope and then align them with business goals as well as value propositions.
When it comes to the planning and backlog refinement phase, categorize the data engineering or modeling tasks and accomplish them based on the business dependencies and value.
During the Sprint and iteration, follow the agile approaches such as continuous integration, test-driven development, and peer review to accomplish modeling tasks effectively.
During the demo phase, validate the data model and ask for feedback from the targeted customers.
During the sprint improvement phase, identify the action areas and make improvements in the data modeling process.
The Bottom Line
Data modeling or data engineeringalso involves the techniques that lead to faster prototyping, deployment, and testing of data models. Such kinds of data models are data virtualization, data modeling software, and automation. Data modeling can streamline the agile and iterative development process. It is easy to enhance the usability, scalability, and efficiency of your data assets. Businesses can leverage data modeling in agile development projects to deliver great value to their customers while responding to current trends.
Also Read: Manufacturing in the Digital Age: The Importance of Quality Products
