Data architecture: What is it?

Data architecture: What is it?

Data structure is a discipline that provides a strategy for managing data as well as documentation of an organization’s information assets and a detailed map of how data flows through its systems. Usually, the goal is to make sure that information is managed properly and satisfies business information demands.

The fundamental data environment for business intelligence (advanced analytics and bi) efforts is most notably defined by data structure, even if it can also enable functional usage. Its output includes standards and specifications for gathering, integrating, storing, and manipulating data, as well as a multilayer foundation for information platforms and information management tools.

Ideally, the information management process would start with data architecture design. However, this is typically not the case, leading to inconsistent environments that must be harmonized and included into a data architecture. Furthermore, despite the fact that they are fundamental in nature, data structures should be updated when new information and business requirements emerge. Therefore, they continue to be a challenge for information management teams.

Data modeling, which creates diagrams of information structures, business rules, and interactions between information items, goes hand in hand with data architecture. However, they are distinct data management disciplines. David Loshin, president of the consulting firm Knowledge Integrity Inc., made a distinction between data modeling’s micro focus on information assets and data architecture’s more expansive macro perspective in articles on how data modeling and data architecture differ from one another.

This guide on data architecture goes into further detail about what it is, why it is important, and the benefits it may provide to a business. Additionally, information on data architecture frameworks, best practices, and other topics will be available. There are links to related pages that go into greater detail about the themes throughout the manual.

How have data architectures changed throughout time?

Many data designs were simpler in the past than they are now. They mainly involved relational databases that contained structured data from transaction processing techniques. A data warehouse, occasionally with smaller data marts established for particular business units, as well as an operational data store, which served as a staging area, made up analytics environments. Using standard extract, transform, and load (ETL) techniques for data integration, the transaction data was prepared for analysis in batch tasks.

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Semistructured and unstructured types of information were incorporated to many designs starting in the middle of the 2000s as a result of the development of big data technology in enterprises. That led to the implementation of data lakes, which often maintain raw data in its native format rather than filtering and then modifying it up front for analysis — a significant departure from the data warehousing process. ELT data integration, an alternative to ETL that reverses the load and change phases, is being used more widely under the new strategy.

Stream processing systems are being used increasingly frequently, which has also led to the inclusion of real-time data in many more data architectures. Along with the fundamental BI & reporting powered by data warehouses, many systems today offer artificial intelligence and machine learning programs as well. The complexity of data structures is further increased by the move to cloud-based solutions.

The information fabric is a new architecture concept that aims to make information management and integration procedures simpler. There are countless potential use cases in data environments.

Why are data structures so important?

The information management process relies heavily on a well-designed data architecture. Along with information engineering and data preparation, it supports programs for data integration and data quality enhancement. It also makes it possible for effective data governance and the advancement of internal data standards. In turn, those two points assist businesses in ensuring the consistency and accuracy of their data.

A data architecture also serves as the foundation for a data strategy that supports organizational priorities and corporate objectives. Donald Farmer, the founder of the consulting firm TreeHive Strategy, stated that “a modern business method is dependent on data” in articles on essential information strategy components. According to Farmer, this makes analytics and data management far too important to keep secret. A corporation must develop a comprehensive information strategy, supported by a strong data architecture, in order to manage and utilize information effectively.

Major stages of developing an information strategy

According to Donna Burbank of the consulting company Global Data Strategy, there are four main stages to developing a data strategy.

What features and components make up a data architecture exactly?

Farmer emphasized the advantages of things like each data governance and regulatory compliance procedures as well as the growing necessity to support several cloud environments in writings on the ideas of contemporary data architectures. He came to the realization that if a data structure prevents the usage of data for analytics, its potential economic value will be lost.

The idea that information is a corporate asset is a cliche in modern information management, according to Farmer. “But information that is just sitting there is just a cost center, requiring maintenance without providing any value to the business.”

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The following are additional traits that are typical of well-designed data architectures:

Business-driven focus that is in line with organizational data and strategy requirements; scalability and flexibility to support various applications and accommodate the information needs of emerging businesses; and effective security safeguards to prevent unauthorized data entry and improper use of information
From a purist’s perspective, alternative tools and platform solutions are not considered to be part of data architectural parts.

A data structure is instead a conceptual framework that is explained through two texts and illustrations. Teams that handle data use them to direct the use of technology and the management of information.

The following are a few excellent illustrations of the components, or perhaps artifacts:

Various other documents that describe company objectives, functions, and concepts to help you align data management initiatives with them; policies and standards that govern how information is handled; data models, common vocabularies, and data definitions for data elements; data flow diagrams that show how information moves through applications; and systems documents that map data usage to business processes, like a CRUD matrix.
5 crucial data architecture tenets Follow these guidelines to help you put your data architecture on the right course.

What benefits do data architectures offer specifically?

Ideally, a corporation may create effective data analytics platforms that give insights and meaningful information with the help of a well-designed data architecture. These insights help firms make better operational and strategic decisions, which could result in improved business performance and competitive advantages. They also help with several other applications, such as the analysis of medical and scientific circumstances.

Along with other benefits, data architecture also improves data quality, streamlines data integration, and reduces the cost of data storage. According to Peter Aiken, a data management expert and associate professor of information systems at Virginia Commonwealth University, it accomplishes this by adopting an enterprise viewpoint as opposed to domain specific data modeling or even by focusing on structure at the database level.

How likely is it that a data architecture design will be flawed?

Overly complex data architecture is a common pitfall. The feared “spaghetti architecture,” which has a maze of lines that stand in for various information flows and point-to-point connections, is evidence of this. As a result, there is a disorganized data environment with conflicting data silos that is challenging to integrate for analytics purposes. Ironically, most data architecture projects aim to arrange already chaotic systems that have grown naturally. They can, however, produce issues that are similar if not handled appropriately.

Getting everyone to agree on standardized data definitions, needs, and formats is another challenge. It is challenging to develop a highly effective data architecture without such. The same is true when putting information in a professional setting. According to Aiken in the Dataversity webinar, effective information architecture “captures the company relevance of the information necessary to manage the organization.” However, if this is not done, there may be a gap between the structure and the strategic data needs it is intended to address.

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Data modeling versus data architecture

The focus of data modeling is on the specifics of certain data assets. It creates a visual representation of information entities, their characteristics, and the relationships between distinct entities. a technique that is done from an advancement of conceptual, physical, and logical data models, which aids in scoping the information demands for systems and applications, followed by creating database structures for all the data.

Data architecture creates a framework for information management and use by taking a far more comprehensive view of an organization’s information. However, as consultant Loshin noted in his piece contrasting the two, data modeling and data architecture work best when used together. According to Loshin, who is also the director of the Master of information Management program at the University of Maryland’s College of information Studies, data versions are a crucial component of data architectures and simplify data modeling.

What precisely are the different design and development roles for information structures?

Data architects typically take the lead in efforts involving data architecture. They need a variety of technical abilities as well as the ability to communicate and interact with business users. A data architect spends a lot of time working with end users to define how current data is used by business processes, as well as how much new data is needed.

Information architects oversee other modelers’ work while also creating their own data models, which is more complex. They also create diagrams, other artifacts, and plans for data architecture and information flow. Outlining data integration procedures and managing the enhancement of information definitions, business glossaries, and information catalogs could be some additional duties. In some companies, information architects are also in charge of choosing and assessing technologies, building data platforms, and establishing data architectures.

The following are some other data management experts who typically participate in the data architecture process:
data analysts.

They also work with businesspeople to assess data requirements and examine corporate procedures. They then create data models using the information they have obtained.

developers of data integration.

They are tasked with developing ELT and ETL jobs to incorporate data sets when the architecture is put into place.

data scientists.

To distribute data to other analysts and data scientists, they create pipelines. They also assist data science teams with all aspects of data preparation.

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