Data architecture is the design and structure of an organisation’s data assets, including databases, data warehouses, and data lakes. It involves defining how data is collected, stored, integrated, and accessed to meet the organisation’s needs.
Key steps in data architecture include identifying data sources, designing data models, establishing data governance policies, and implementing data management strategies.
Organisations can improve decision-making processes and drive business success by understanding the principles of effective data organisation and management.
Let us now go over the definitions, frameworks, and key steps involved in designing a successful data architecture.
What is Data Architecture?
Data architecture is the discipline that documents an organisation’s data assets, maps how data flows through its systems, and provides a blueprint for managing data.
It is a foundational component of data management and is crucial for ensuring that data is managed properly and meets business needs for information.
Business intelligence (BI) and advanced analytics initiatives rely heavily on data architecture, which defines the underlying data environment for these projects.
Data architecture includes a multilayer framework for data platforms and data management tools, as well as specifications and standards for collecting, integrating, transforming, and storing data.
Typically, data architecture is the first step in the data management process. However, this is not always true for every scenario, as different businesses may have different data requirements.
While data modelling and data architecture are two different data management disciplines, they work closely together to produce diagrams of data structures, business rules, and relationships between data elements.
Data architecture has evolved over time to include not just structured data from transaction processing systems but also unstructured and semistructured forms of data.
This has led to the deployment of data lakes, which often store raw data in its native format instead of filtering and transforming it for analysis upfront.
Organisations can enhance strategic planning and operational decision-making, potentially resulting in improved business performance and competitive advantages, with the aid of data architecture, which is crucial for the development of efficient data analytics platforms. In addition, it supports a number of other uses, including scientific research and medical diagnosis.
Security is an important aspect of data architecture. That is why, in order to keep your data secure, you must implement a risk mitigation strategy.
Risk mitigation is the process of identifying, evaluating, and implementing strategies to reduce the likelihood and severity of cyber threats.
Learn more about it in our post, ‘Risk Mitigation: Definitions and Steps for Protecting Your Company’s Data from Cyberattack’.
What Are the 3 Frameworks of Data Architecture?
While there are many different types of data architecture frameworks, DAMA-DMBOK, Zachman Framework for Enterprise Architecture, and The Open Group Architecture Framework (TOGAF) are the most popular ones.
Here’s an overview of some of the most widely used data architecture frameworks today, according to Rivery blog post.
DAMA-DMBOK (DAMA International’s Data Management Body of Knowledge)
This framework is specifically designed with data management in mind. It defines terms for roles, deliverables, and functions related to data management as well as explains the guiding principles of the field.
Zachman Framework for Enterprise Architecture
John Zachman developed an enterprise structural framework at IBM in the 1980s for the purpose of organising information. There are multiple layers in the data column. It also includes physical data models, enterprise data models, semantic models, architectural standards, and real databases.
The Open Group Architecture Framework (TOGAF)
This approach provides a high-level framework for creating enterprise software packages and applications. It is an enterprise architecture ontology. It arranges the development process in a methodical manner. In order to achieve desired outcomes, this strategy focuses on reducing errors, controlling timelines, guaranteeing cost-effectiveness, and coordinating information technology with business units.
9 Key Steps for Creating a Data Architecture Plan
Creating a data architecture plan will require the data architect to engage in intense communication with senior leaders and collaborative work with other parts of the company.
It starts with defining the business requirements and goals for data management. This involves identifying the key stakeholders, understanding the data sources and types, and mapping out the data flow within the organisation.
Aside from that, TechTarget mentions that there are 9 important steps that you need to follow when creating a data architecture plan. Here are the details:
- Socialise with senior leaders: C-suite executives need to be convinced of the benefits of creating a data architecture, just like they would be of any other strategic technology project. Create a message that highlights the advantages that an enterprise data architecture offers. To win over important stakeholders, identify them and engage with them.
- Identify the data personas: Data consumers’ information needs shape an organisation’s technological environment. Application system custodians are responsible for the data sets generated and utilised by their applications. Determine who within the organisation creates, stores, updates, reads, and handles data in any other way. Determine the stereotyped individuals and describe them based on the data touch points they correspond with.
- Determine data requirements: Ask the people who use the data what they need for their business and engage them in understanding their approach. Record how those requirements connect to the discrete data sets that these customers either currently use or anticipate needing, as well as the abstract data domains like “customer” or “product” data.
- Evaluate information risks: Identify and interpret data governance directives, including how they apply to data handling, management, and protection.
- Analyse the data landscape: Examine and record enterprise data sets’ names, locations, producers, owners, consumers, and contents. Sort each data set into categories based on sensitivity and usage scenarios, then compile this information into a data catalogue.
- Look over the data life cycles: Analyse the path that data sets take from their starting points to their end points. Record the data pipelines’ data lineage mapping.
- Evaluate the data infrastructure: Do a comprehensive documentation of the system, database structures, data warehouses, data marts, and operational data stores that are in use, whether they are on-site or in the cloud, and if the latter, identify the cloud service providers. You should also record the current state of data management in the organisation.
- Do a SWOT analysis: Combine all the knowledge you gathered using the strengths, weaknesses, opportunities, and threats (SWOT) analysis framework. Using that, you can determine which areas have the most room for improvement.
- Create a blueprint and roadmap: Draft a blueprint for the enterprise data architecture that highlights the suggested deployment projects and provides an overview of the information gathered. Describe the proposed projects’ scope for the short-, medium-, and long-term horizons.
Important Factors to Consider when Designing Data Architecture
When designing a data architecture, there are three important things to consider.
The first thing is data storage. You need to choose the appropriate type of database based on the data you need to store. Consider factors such as structured vs. unstructured data, permanent vs. temporary storage, and access patterns (random vs. sequential). The type of database you select will dictate its capabilities and performance.
Second is scalability. You need to plan for scaling from the start. This will ensure that your data architecture can handle the growing needs of your business. Consider global vs. regional data availability, regional restrictions on data storage, and the need for data synchronisation between regions. Scalability is crucial for maintaining performance, availability, and legal compliance.
And the third is data governance. Make sure to establish policies and processes for managing and governing your data. This includes data governance, data quality, data privacy, and data security. A solid data governance framework is needed to ensure that your data architecture aligns with your business objectives and supports your overall data strategy.
That is all you need to know about the data architecture concept. Working on the data architecture may require you to obtain a security clearance.
Security clearance is a determination by an organisation, usually a government agency, that a person has permission to access classified information or restricted areas.
Learn more about it in our previous article, ‘Security Clearance: What It Is, How to Get It, and the Different Types’.
Conclusion
Modern data-driven organisations rely heavily on data architecture to help them fully utilise their data assets for strategic decision-making, innovation, and competitive advantage. Businesses can effectively optimize their data ecosystems to support business objectives in an increasingly complex and dynamic digital landscape by comprehending the fundamentals, types, stages, and important factors of data architecture.
Ready to take your data architecture to the next level?
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