Data is the key to get ahead of rivals in today's data-driven marketplace. Companies all over the world are turning to their increasingly rapidly growing data volumes to make strategic business decisions. But with data being everywhere, business leaders must be able to sift through unstructured and often erratic data and make it workable so that they can solve complex business problems. This makes data architecture all the more important. Data architecture describes how data is collected, stored and used in an information system.
What is Data Architecture?
Data architecture is the foundation of an effective data strategy. According to data architecture definition, it is a framework of models, policies, rules and standards that an organization uses to manage data and its flow through the organization. Within a company, everyone wants data to be easily accessible, to be cleaned up well, and to be updated regularly. Successful data architecture standardizes the processes to capture, store, transform and deliver usable data to people who need it. It identifies the business users who will consume the data and their varying requirements.
A good approach to data architecture is to make it flow from data consumers to data sources, not the other way. The goal is to transform business requirements into data and system requirements. Companies need to have a centralized data architecture that aligns with business processes and provides clarity about all aspects of data. The individual components of data architecture are the outcomes, activities, and behaviors.
Data architecture is the purview of data architects. A data architect builds, optimizes, and maintains conceptual and logical database models. They determine how to source data that can propel the business forward and how that can be distributed to provide valuable insights to decision-makers.
Data Architecture Principles
Data architecture principles include the set of rules that pertain to data collection, usage, management, and integration. These principles form the foundation of the data architecture framework and help build effective data strategies and data-driven decisions.
Validate All Data at Point of Entry
It's important to improve the overall health of organizational data by eliminating bad data and common data errors. Design your data architecture to flag and correct errors as soon as possible. A Data Integration Platform can help do that – validate data automatically at the point of entry. This will also help minimize the time taken to cleanse and prep data.
Strive for Consistency
Using a Common Vocabulary for data architecture will help users on the same project to collaborate. Shared data assets like product catalogs, fiscal calendar dimensions, etc. must use common vocabulary regardless of the application or business function. Users of such shared data must work from the same core definitions to maintain control of data architecture and data governance.
Everything Should be Documented
Get into the habit of documenting all parts of your data process so that data visibility and data remain standardized across the organization. Documentation should help you keep a tab on how much data is collected, which datasets are aligned, and which applications need to be updated. Consistent documentation should work seamlessly with data integration.
Avoid Data Duplication and Movement
Every time data is moved, it impacts cost, accuracy, and time. Modern data architectures should reduce the need for additional data movement to reduce cost, improve data freshness and optimize data agility. Modern data architecture views data as a shared asset and does not allow departmental data silos. This makes it simpler to universally update data, and everyone can operate from a single version of the data.
Users Need Adequate Access to Data
Data architecture books state that users must be provided the right interfaces to consume data using designated tools.
Security and Access Controls Are Essential
The emergence of data security projects has made it easier to ensure unified data security. Data architectures must be designed for security without compromising access controls on the raw data.
Data Architecture Framework
There are multiple enterprise architecture frameworks that are used as the foundation for building the data architecture framework of an organization.
This refers to DAMA International's Data Management Body of Knowledge – a framework designed specifically for data management. It includes standard definitions of data management terminology, functions, deliverables, roles, and also presents guidelines on data management principles.
Zachman Framework for Enterprise Architecture
John Zachman created this enterprise ontology at IBM during the 1980s. The 'data' column of this framework includes multiple layers like key architectural standards for the business, a semantic model or conceptual/enterprise data model, an enterprise or logical data model, a physical data model, and actual databases.
The Open Group Architecture Framework (TOGAF)
TOGAF is the most used enterprise architecture methodology that offers a framework for designing, planning, implementing, and managing data architecture best practices. It helps define business goals and align them with architecture objectives.
Data Architecture Diagram Tools
The flow of correct and consistent data is critical for successful data architecture. For best results, the flow and relationships of data need to be defined and structured. This is where the Data architecture diagram comes in. Data architecture diagrams demonstrate how and where data flows, gets processed, and is utilized. A data architecture diagram can help decide how to update and streamline data storage resources. Since data is constantly being collected and used, you will need to frequently revise and update the data architecture diagram.
A data architecture diagram should contain following details:
- Illustrate how data processing takes place
- Show how and where data is stored
- Display estimated rate of data increment
- Signify components that would add to future growth
Specialized software or cloud-based intelligent diagramming apps are available for high-tech data architecture diagrams. Prominent data architecture tools to create and share architecture models include:
What is a Data Architect?
The data architect is the data management professional who translates business requirements into technical requirements and defines data standards and principles. The data architect role is a critical link between business and technology; hence, qualified data architects are highly sought after by recruiting companies.
What Does a Data architect do?
As the mastermind behind data architecture, the data architect creates blueprints for data flow and data management. They assess the organization's potential data sources, and devise plans to centralize, integrate, protect and sustain them. Thus, employees can access critical information wherever they want, whenever they want.
The role of a data architect requires:
- Collaboration with IT teams to devise data strategy
- Build data inventory needed to implement architecture
- Research data acquisition opportunities
- Identify and evaluate data management technologies in use
- Develop data models, etc.
How to Become a Data Architect?
Since Data Architect is an evolving job role, there is no specific training or certification course on how to become a data architect. Typically, professionals like data engineers, data scientists, or solutions architects specialize in data designing and data management; they work their way up the career path to become data architects.
Highly skilled data science professionals are in high demand in the global job market. Companies across industries are hiring data architects who can straddle the business and IT worlds.
If you are looking to build a career in data science or data architecture, or feel the need to upskill yourself, consider the Caltech Post Graduate Program in Data Science offered by Simplilearn, one of the leading online certification training providers in the world.