Data architecture is the sum of data-related processes, from data collection, through screening, cleaning and processing to generate insights.
Every strategy, criterion, tool and practice used to manage data in the business context is within the scope of data architecture.
There is also database architecture, a subtype in which the focus is on the organization of data repositories , as well as the sources from which they are extracted and the way in which they are accessed.
How does data architecture work?
The main function of data architecture is to define how data is collected, stored, managed and used in an enterprise.
In its operation, a series of tools and practices are activated, with which data can be collected and processed.
In the collection part, the data architecture defines, for example, what the data sources will be and how the integration between these sources will be.
Moving on to storage, data architecture works through data lakes and data warehouses, where data is kept secure.
Data architecture also depends on consistent data governance and processing routines for its functioning.
Types of data models
A database architecture can be organized in different ways .
Because it is an abstract representation, data architecture can be oriented towards countless objectives.
It serves as a guide for the development and implementation of database systems, ensuring the consistency, integrity and accessibility of information.
Therefore, each company is free to decide how it wants to organize its data, according to the use it intends to make of it.
Conceptual data models
Every data architecture needs a starting point . That’s the role of a conceptual data model.
It serves as a scheme, which can be graphical, with which data is organized according to the current state, projecting possible future states.
In this kind of “model of models”, technical details are not yet taken into account , since the main objective is to form a data management base.
Logical data models
Moving forward in the construction of the data architecture, a company can, based on the conceptual model it has defined, develop a logical data model.
Its purpose is to translate the conceptual model into a more detailed structure , defining the entities, attributes, relationships and constraints of the data.
Another function is to specify how the data will be organized and stored in the database, without worrying about the physical storage aspects.
Therefore, it is normally used in the design phase of a database system, serving as a bridge between the conceptual model and the physical model.
Physical data models
In the third and most advanced stage of implementing a data model, we have the definition of the physical model.
This is where the concrete implementation of the logical model is carried out in a specific database system, defining the tables, columns, indexes and other physical elements.
Through the physical data model, the company details how data will be stored on a storage medium, considering aspects such as hardware, software and performance.
It is the model used in the implementation phase of a database system , guiding the creation of tables, indexes and other physical structures.
Benefits of data architecture for companies
As shown by a survey published, 100% of managers of large companies consider the creation of a navigable and easy-to-understand data repository to be essential .
This goal can be achieved when investing in database architecture, which as such brings unique benefits. After all, as the research also reveals, 43% of leaders believe that their repositories do not meet basic business requirements.
Data architecture can respond to this challenge, providing competitive advantages like the ones we will see from now on.
Reduction in redundancy
Data redundancy is when a data lake or data warehouse stores repeated data about the same object or this data expresses the same information.
A well-architected data and storage structure can help avoid this by automatically eliminating redundancies that will sooner or later arise.
By doing this, the company saves time and improves the quality of its data, facilitating subsequent processing processes.
Integration enablement
One of the ever-present challenges when dealing with data is ensuring that it can be accessed in an integrated manner .
It is very common for so-called data silos to form in companies, which makes management considerably more difficult.
Data architecture prevents this type of situation through the logical chaining of data structures and their access channels.
A good example of this is what ERP systems do , which have, among their purposes, integrating a company’s database into a single platform.
Improving data quality
In the Big Data context , the greater the volume of data used, the greater the chances of obtaining accurate insights .
On the other hand, quantity is not quality. The larger the data repository, the greater the chances of redundancies, inconsistencies or even false data.
When a company develops a data architecture that is compatible with its needs, it can better filter the data it stores. The result of this is more relevant data that, once processed, leads to much deeper insights.
Data Lifecycle Management
One issue that companies face in their data management processes is managing information according to its life cycle.
What to do, for example, when an email marketing database is out of date? What criteria should I adopt to clean this type of repository? This is another question that data architecture answers, generating competitive advantage.
Improved data governance and security
Data governance deals with practices related to data collected by a company, so that it meets legal requirements in this regard.
The General Data Protection Law (LGPD) is the main rule regarding everything related to the use of data generated online by individuals and legal entities.
To comply with the standard, companies need to define control, protection and confidentiality maintenance measures within their data architecture.
A solid architecture, therefore, represents an advantage in this aspect, protecting the business from possible leaks and legal proceedings due to improper or inadvertent use of data.
More accurate decision-making processes
In data-driven companies, data is always the basis of the decision-making process .
This is only possible when the data architecture is organized enough to facilitate access to data, as well as ensuring its treatment in accordance with strategic objectives.
Therefore, decisions tend to be made more quickly and, most importantly, with a greater chance of being correct .
An additional benefit that comes with an intelligent data architecture is the reduction of reliance on gut feeling in decision making and all the uncertainty that this entails.