- Data modeling focuses on the representation of the data while data architecture is concerned with what tools and platforms to use for storing and analyzing it.
- Data modeling is all about the accuracy of data while data architecture is about the infrastructure housing that data.
- Data modeling is concerned with the reliability of the data, while data architecture is concerned with keeping the data safe.
- A data model is an attempted representation of reality, while data architecture is a framework of systems and logistics.
- A data model represents a limited set of business concepts and how they relate to one another. Data architecture covers the data infrastructure of the entire organization.
As businesses have access to ever-growing amounts of data, data modeling and data architecture are becoming increasingly important concepts. We all want to base our business decisions on data. But what data? And how?
For your company to be able to leverage business data in the best possible way, you need to organize it and make it accessible to relevant stakeholders. This is where data models and data architecture comes in.
Table of Contents:
- What is Data Modeling?
- The 3 Levels of Data Modeling
- What is Data Architecture?
- Data Modeling vs Data Architecture: Key Differences
- Defining the Business Use Case
- In Conclusion
What is Data Modeling?
As companies aim to run a more data-driven operation, cleaning and modeling the data is often the first step. The focus in data modeling is on the selection and organization of the data, rather than on how you will eventually use the data.
Your data is the key to wise business decisions. And your data model is the key to that data.
A data model will enable your organization to understand, analyze, and communicate around your data assets. It serves as a single source of truth, helping you make sure there is consistency in things like rules, language, and default values.
Below are some examples of what a data model can include:
- Entity types
- Naming conventions
Data modeling will help you create relational tables and procedures and provide you with a clear picture of your base data. A smart and well-structured data model will help you identify data gaps and redundant data points.
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The 3 Levels of Data Modeling
Construction of a data model happens on three different levels, all building on each other.
Conceptual Level Data Model
A conceptual data model focuses on what data should be in the system. Data architects are often involved in creating this model, but the input from relevant business stakeholders is fundamental. The purpose of the conceptual data model is to organize and define business concepts and rules.
Logical Level Data Model
The logical data model defines how to implement the system, regardless of what DBMS (Database Management System) you will eventually use. This model is often created by data architects, working closely with business analysts and the relevant business stakeholders. The logical level is where you develop a technical map of all the rules and data structures.
Physical Level Data Model
Once there is clarity regarding what data should be in the system, and how to treat that data, it's time for the physical data model. This is the model describing how to implement the system in the specific Database Management System. This is often where database analysts and developers enter the process, to drive the implementation.
Creating a data model may appear time-consuming, but rest assured that this is time well spent. Going forward, it will make upgrading and maintenance of your databases and IT infrastructure quicker, easier, and cheaper.
What Is Data Architecture?
While data modeling focuses on the representation of the data, data architecture is more concerned with what tools and platforms to use for storing and analyzing the data. The data architect is the one choosing and implementing your tech stack to create an ecosystem based on your organizational structure and business goals.
Should you migrate to the Cloud? What security concerns do you need to address? What tools and platforms do you need? These are the questions of data architecture.
Without proper data architecture, things tend to grow increasingly chaotic over time as you add new tools and tech solutions on an ad-hoc basis. Letting this go on without a clear structure will mean not only loss in productivity and control but usually also high costs.
Below are some examples of what data architecture concerns:
- Cloud storage
- Hardware virtualization
- Data processing
Data architecture forms an integral part in the overall enterprise architecture, defining the structure and operation of the whole organization. The mission of the data architect is enabling stakeholders to access business-critical data – regardless of where it originally comes from – and make it easy for them to use and understand it from their unique perspectives.
Data Modeling vs Data Architecture: Key Differences
1. Entities and Rules vs Solutions and Tools
Data modeling is about the relationship between data entities. It creates rules for these relations and links and outputs based on these rules.
Data architecture, on the other hand, looks at the entire database, and the tools and solutions needed to store, process, and analyze the data. This also includes hardware and administration.
2. Business Concepts vs Infrastructure
The purpose of a data model is to create as accurate a representation as possible of the business concepts and how they relate to one another. That is what a model is; it's an attempted representation of reality.
Data architecture is concerned with the data infrastructure of the entire organization, in which the data models exist. It is an all-encompassing framework of systems and logistics, where the data models are an essential component.
3. Reliability vs Security
Data modeling is all about the accuracy of data. What data points to use? How to make sure the data is clean, up to date and accurately represented? If we use a house as an analogy, the Data Modeler is concerned with the inhabitants of the house: the data points. What to name them, how to make sure they are who they say they are, and how they should interact with each other.
Data architecture is about building the house itself. Data architecture has a strong focus on how to keep the data safe. How to store it? What parts need to be encrypted? Who has access to what system, and what passwords and security systems are required? Those are the focus areas of the Data Architect.
Defining the Business Use Case
A common mistake is to rely too heavily on data scientists for data modeling. The risk that comes with doing so is that the person building the model might not be familiar enough with the business reality, where you will actually use the model. It's vital to define the business use case for a model, before starting to build it.
Let's say, for example, that your customer service team is struggling to reduce churn, and need data-driven insights to act on. Then the model showing when customers are likely to churn is different than a model telling you why they're churning. To know what model you need, you have to start by defining the use case.
Inferior Data Modeling Will Affect Data Architecture
Poorly designed data models can cause severe analytic failures and damage your business. You will not be able to find the right data, nor will you be able to know what the data you have means to your company. Without well-functioning data models, you risk basing decisions on things like gut feeling and guessing instead of facts. You will also not be able to benefit from your paid tools for analytics and research.
While different programs and systems can change within a company, the data is a very stable parameter. Therefore, taking the time to get it right from the beginning is well worth the effort.
Data modeling is about creating a representation of the enterprise's data in the form of a model. This model entails the business concepts, how they are related to each other, and it defines rules, default values and naming conventions.
Data architecture, on the other hand, is the overall infrastructure in which the data and the data models exist. The focus of data architects is to keep the data safely stored yet easily accessible, by creating the environment for the data in terms of tools, platforms and solutions.
How Xplenty Can Help
We believe that everyone should be able to manage their data, regardless of their tech experience. That's why we offer no-code and low-code options so that you can add Xplenty to your data solution stack with ease.
Xplenty offers a complete toolkit for building ETL data pipelines, making it easy to implement an ETL or ELT solution using an intuitive graphic interface. With Xplenty's workflow engine, you can orchestrate and schedule data pipelines. With our rich expression language, you can implement complex data preparation functions and integrate with other data repositories and applications.
Data modeling and data architecture can feel daunting, but Xplenty will be there with you along the way to tackle these challenges head-on. With 24/7 email, chat, phone, and online meeting support, we've got your back.
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