Data Modeling & Data Architecture – two sides of the same coin?

DataData Architecture

Iliya Koparanov

March 18, 2023
Data architecture and data modeling are two interrelated and intertwined concepts which often overlap. Building an efficient, high-performing, and seamless data infrastructure requires consideration of both. In this post, we will explore the two different concepts and explain how they impact a business' data strategy and its stakeholders.

Data Modeling

We can describe Data Modeling as the process of creating a visual representation of a data system to describe connections between its data points and structures. This process essentially involves defining the organization of the data and establishing the foundations for the entire system to work. It is like a blueprint for a house, describing what and how to build before construction begins.

Data models typically consist of various elements such as entity types, attributes, naming conventions, relationships, rules and more. They provide a comprehensive representation of the data; and how these elements are used together to eventually come up with a Data Model is key to build systems that serve the business long term. Data Modeling practitioners usually do that through three different stages:

Conceptual Data Model: Think of it as a simplified diagram that outlines the main entities or data elements (i.e buyer, supplier, stock, purchase…) and their relationships (i.e buyer is related to supplier through a purchase) in a high-level manner. This model is mainly used to check with our business colleagues if what we are trying to build reflects the reality of the organization’s processes.

Logical Data Model: Once the concept is approved by the business, it’s then when we get into the details and start asking ourselves (using the previous example), questions like - what defines a buyer? A buyer (in our terminology, an entity) will normally have a name, a representative, an address, an email and many other details - all of those are what we call attributes and this is precisely the step where we define them. As you can see, this phase is where the model is starting to take shape from a logical point of view.

Physical Data Model: This is when the model is tailored to a specific technology or set of technologies. It entails the definition of detailed information such as table names, primary and foreign keys, indexes, constraints, and other technical details related to database implementation. This model is of a high importance as it’s the bedrock of the system that Data Engineers and Architects will use to scale it whilst guaranteeing its consistency.

Data Architecture

Another construction analogy -  anyone?

If Data Modeling is the blueprint, guidelines or roadmap for a construction’s design, then Data Architecture can be thought of as the foundation and framework of the building. 

Just as the foundations and framework provide the structural support for a building, the data architecture provides the infrastructure that supports the storage, processing and retrieval of data. This includes determining what hardware and software components are needed, how they should be configured, and how they potentially work together to meet the needs of the business.

But wait, isn’t this the Physical Data Model? Not exactly - although data architects are normally involved during the development of the Physical Data Model, Data Architecture’s main responsibility is to take a broader look on how data should be managed across the organization. This broader look comprises the following considerations:

How should data be stored? Should it be stored in a database or perhaps in another storage system, and if so, why? All these are questions that should be discussed and agreed upon along with other aspects such as how data should be partitioned, indexed, backed up, recovered etc.

How should data flow within and through the system? Are we going to use batch processing, real-time processing, or a combination of both? Additionally, factors such as standardization of processes like data validation, transformation and enrichment are of utmost importance.

How should data be accessed within the system? Through APIs, User Interfaces, or maybe other types of data access methods? Furthermore, topics including data security, access control, and performance optimization should also be taken into consideration.

What rules and principles should govern the data and its usage? What policies, standards, and procedures for managing data within the system should be established? How should data quality, privacy, and compliance be?

How should data be used? Making data available to downstream systems or end users is the ultimate goal of a data strategy. Data Architecture practices should ensure that data is easily accessible for all those stakeholders that can use it to bring value to the business.

The discussion of all the above considerations concludes with the elaboration of a framework that establishes the data practices that should be followed across the organization. But hold on - who is actually doing the job here? Well when we build a house we have construction workers, and in data, we have Data Engineers who follow the practices dictated by Data Architects to start building the systems (or data pipelines) that will allow the organization to make a good use of their data.

Why they are both critical components for the data-driven organization

All levels and roles from the organizational hierarchy stand to benefit from a well-designed data ecosystem, enabling them to perform their day-to-day tasks more effectively and have a single source of truth from which to retrieve answers for their business specific questions.

How then, can well-structured data modeling and architecture impact a business and its stakeholders?

Data Quality Improvement: Data modeling helps organizations identify the data they need to collect, store and analyze. This ensures that data is accurate, consistent, and relevant, leading to better quality insights. This will help Data Analysts and Data Scientists to conduct research, generate insights and develop consistent predictive models.

Better Decision Making: By creating a structured model of data, organizations can gain a better understanding of their data and use it to make more informed decisions. This enables companies of all sizes to respond quickly to changes in their industry or market, and stay ahead of the competition. Business Analysts would be the main beneficiary, allowing them to understand which is the right business strategy for the given moment and which is the right time to implement.

Increased Efficiency: A well-designed data infrastructure can streamline data collection, storage, and retrieval processes. This can reduce the time, friction and resources required to analyze data by allowing organizations to focus on more important tasks. Thanks to that, Data Engineers would be able to create plans on how to further develop and improve the data ecosystem instead of dealing with bugs and inconsistencies.

Scalability: As organizations grow, their data needs grow too. A well-designed data infrastructure can scale to accommodate increasing amounts of data, ensuring that the organization can continue to make informed decisions as it grows. IT and technology managers need to understand and analyze as much as possible data to make informed decisions about technology investments, data governance policies, and data security measures.

Cost Savings: By creating a well-designed data infrastructure, organizations can avoid the costs associated with poor data quality, such as incorrect decision-making or the need to re-collect data. Additionally, a well-designed data infrastructure can reduce the need for manual data processing and analysis, resulting in cost savings over time. Optimizing the work on all levels and automating processes helps the entire company to direct its resources to the right place.

Wrapping Up

At this point, I hope it’s clear that well planned and designed data modeling and data architecture are essential for anyone who works with data across an organization. From data analysts and scientists, to business leaders and IT managers, all stand to benefit in various ways.

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