As a data consultant at IOVIO, I have experience in data modeling and converting data into business requirements. In this article, I would like to share my experience on why data modeling is important, what types of data models you could use, and help you to start your data modeling process.
Organizations often describe a data-driven ambition to use data in day-to-day decision-making. This is to improve the predictability of the organization and to make sure teams are focused on the topics with the highest added value.
What I often see in business teams is that they make use of the rule of thumb to prioritize their work. The cause of this is either the doubts about the data quality or it takes too long to answer the business questions. Data modeling is the answer to close the gap between the way of working of the business teams and the ambition of the organization for data-driven decision-making.
Data modeling will bring a visual representation of your business process, service, or product from a data perspective that everyone can understand. This is the sweet spot where technical and business teams can collaborate using a common model to understand the challenges that need to be solved as well as opportunities for optimization.
Data modeling improves data quality by standardizing the database schema and allowing you to store metadata in a single point of truth.
Organizing your data will make it more fun and more importantly, it will reduce the time to answer your business questions.
Common types of data modeling are:
The relational model is commonly used in combination with SQL. The relationships of data segments are specified in a separate table. These separate tables (interrelated tables) are used to join data segments based on their unique primary key. This data model is used to directly query and/or join tables from the database.
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Data relationships in a hierarchical model are structured in a treelike format. The characteristic of this model is the one-to-many relationships between parent and child datapoints. A hierarchical model is used when data can be grouped into clusters with one or multiple levels. An example could be a collage with its whole structure or the amount of COVID-19 infections worldwide, for each continent, country, and city.
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The network models in comparison to the hierarchical model are not restricted to the one-to-many relationships. The visual representation is a graph with nodes and edges to describe the relationship between the data points. This type of modeling is used for faster performance of an application to access the data and the associations.
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Entity-relationship (ER) modeling is focused on creating a visual representation of the entities, relations between entities, and their restrictions. The visual/conceptual representation is not a data model by itself but can be implemented in a database.
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Dimensional modeling is introduced to optimize the database for reporting and analytical purposes. This can be done by creating entities that describe a domain with all its characteristics. The dimensional model is intuitive to understand how entities relate to each other.
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Data modeling is not an easy process. It all starts with understanding the business requirements, the ambition of the organization on data-driven decision making, and acquire in-depth knowledge of your data.
This will enable you to define the minimum required data and select the data modeling type for converting data into insights and/or actions.
As a data modeling expert, my best practice is to work in close collaboration with domain experts. Business colleagues are not always aware of the questions they could answer with data, but they are aware of their time-consuming tasks and decisions they have to make on a daily basis.
It is good to be aware of the data maturity level of the organization and where the organization is aiming for. Will there be systems that will give recommendations and make the decision for you or will there be a person that will make the decision based on insights?
For data modeling, it is important to have an in-depth understanding of the data. This can be done by:
Think big but start small. Based on the business requirements you should be able to identify the data requirements. While there might be more data, do not add it to your data model unless it is required for the insights you want to gain. Adding this unnecessary data will bring more maintenance and will at some point lead to performance issues.
The data model should be flexible enough to evolve over time, for example, to add additional entities. Adding additional entities or adjusting entities in the data model will make sure the data model is up-to-date with the (changing) business requirements and will lead to better decision making.
The data model with its standardized database schema brings the opportunity to introduce data quality controls. Examples of these are to make sure that data values are consistent and/or data events/transitions are stored in the correct order.
Introducing data quality controls will prevent issues during data research or creating insights.
Data modeling plays a crucial role in the ambition of organizations to use data in day-to-day decision-making. The visual representation will create a sweet spot where technical and business teams can collaborate more efficiently. A data model organizes your data which will improve the data quality and reduces the time to answer business questions.
Before starting to actually model the data it is important to:
The type of data model to select depends on the flexibility of the data and whether you focus on reporting and analyses or the performance of an application.
At IOVIO we help organizations to achieve their data-driven ambitions. We have strong expertise in the data domain, including building and maintaining cloud-native platforms, as well as data modeling and converting data into your business requirements.
