Data Architecture: The First Step to Successful Data Management

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That is why, today, one of the greatest concerns and interests of how overseas chinese contribute to business in the usa companies is to find the best and most diverse ways to collect, store and analyze the large sets of data they possess, in an effective, efficient way and, above all, ensuring their security and integrity.

It is from this need that strategies, methodologies and tools such as Big Data , Data Science , data mining , data architecture , among others, arise, with which we seek to organize and get the most out of the data, transforming it into commercial information that benefits the development of companies, through new products or services, and the improvement and optimization of internal and external processes.

Data architecture is one of those methodologies that greatly contributes to the interpretation and processing of a company's data. In fact, it is considered the first step, and perhaps one of the most decisive, in the data management process .

If you want to know more about data architecture , its characteristics and principles, we suggest you continue reading this article.

Data architecture: what is it and what is its purpose in companies?
As we mentioned before, data architecture is defined as the methodology or set of methodologies responsible for modeling, defining and establishing the most appropriate and effective way to collect, store and organize a large volume of data that will later be integrated and used within a Database Management System .

In other words, data architecture seeks to define the techniques and plan for managing data and how it should flow or correspond between a company's different data storage systems .

The main objective of data architecture in companies is to be able to understand their business needs and requirements, and to establish policies, rules and standards so that they can be resolved through valuable information obtained by means of data collection and analysis.

Data architecture focuses on being able to model and relate the different data systems of a company, with the purpose of creating a framework for data management that allows the simultaneous interaction of data systems.

Principles and characteristics of data architecture
Within data architecture there are a series of principles and characteristics that allow the implementation of this methodology to meet its objectives in companies. Below, you can learn about each of them.

Data Architecture Principles:
Data expert Joshua Klahr, vice president of AtScale, has defined six principles that he believes form the basis of modern data architecture . Klahr recommends taking them into consideration when implementing data architecture in companies :

Data is a shared asset: this principle is based on the idea of ​​eliminating so-called data silos within a company's departments; instead, it promotes access to data for all stakeholders from a global and complete view.
Adequate access to data: In addition to eliminating data silos , it is important that the data architecture in a company also focuses on creating and providing appropriate interfaces that allow easy access to data by users.
Security as a priority: One of the most relevant principles indicates that data architecture must focus on establishing a design with a focus on security, which applies and allows data protection policies and guarantees automatic access controls.
Ensuring a common understanding: In addition to promoting data as a shared asset, this principle proposes that it should be understandable to all users. In other words, a familiar vocabulary must be used so that it is easily understood and the analyses are focused on the indicated objectives.
Data curation: It is important that within the data architecture, investment is made in data curation, carrying out tasks of identification, collection, verification, transformation, integration, display and maintenance of information. The implementation of this principle will allow data to have the highest possible quality, which will generate analysis and reports with the indicated information.
Data flows optimized for agility: This principle states that by reducing data movement, companies can achieve cost reductions in the implementation of data architecture. Additionally, it opens up the possibility of increasing data updates and optimizing agility in companies.