Data Architecture

Data Architecture and why is it important?

Data Architecture refers to designing and organizing a company's data infrastructure, including databases, data models, processes, and policies. It provides a solid foundation for data storage, integration, and analysis, ensuring data is accurate, secure, and easily accessible.

Data Architecture enables businesses to break down silos, integrate disparate data sources, and create a single source of truth for better decision-making.

Data Architecture Principles

Read about Principles of data architecture here.

Every data concept has a principle. The concept principle is the enforced way a concept works and delivers results.

The concept principle is the enforced way a concept works and delivers results.

Data architects, therefore, select concepts based on their principles. They understand that an architect can only select concepts with which they are familiar and principles that they understand.

Data Architecture Reference Model Example

This model is a public domain Data Architecture Reference Model.

You can use this reference model to build your data architecture model for your organization.

Data Architecture Reference Model.

Auto Data Processing to Help Execute Business Strategies

A business strategy differs from a plan in that you have to change multiple things simultaneously in your organization at various places, whilst keeping everything going.

One way to stay in control at all times of the situation is to generate a data landscape on Dragon1.

	Scenario 1	Scenario 2
Problem	No common insights and overview	Projects causing impact on service delivery
Impact	Loosing time in discussions and executing strategies	Spilling time and budget, letting the competition catch up
Solution	Generate a data landscape on Dragon1 to create common insights and an overview, and support timely, fruitful discussions.	Generate a data landscape to support predictions and analyses on the impact of change.

Did you know that a Data Architecture Landscape like this, with views replaced over 10 separate report documents with other organizations.

Impact Analysis of Changes to the Data Architecture Landscape

Take applications offline and back online, to see the impact on data flows in this data fabric and master data management.

What if in a future situation, certain applications are not there anymore to facilitate a certain data stream? Then what is the impact of that change?

A strong Data Architecture sets the foundation for artificial intelligence implementation.

Create a Data Architecture blueprint or Data Architecture landscape for your AI implementation project.

Here is a nice Guide to the Data Landscape, if you want to learn more about this field.