How can you define data requirements for effective data architecture?

Data Architecture is as good as Business Requirement! Business Requirement becomes foundation for Data Architecture so enough details and clarity should be available in BR to build DA. There are key facets of DA, irrespective of BR, like Sustainability, Maintainability, Auditable, Accommodative, Flexible, Secure and Timeliness etc... Profile the sources, understand, structure & grain of Source Data. Know integration point and highlight potential Data Quality issues in Source. Governance perspective, define PII, CDE and Tag them early on. Frequency of data pull request defines processing time window and performance requirements. Know key consumer & pull data that is required so it is less risk prone. Delete when purpose is done with.

Provide enough flexibility in the cataloging phase for sourcing/ingestion requirements as things evolve / change or get added. Your ingestion pattern should be flexible enough to accomodate the right kind of changes/additions and decline requests that probably duplicates effort or create ambiguity in the SOT pool

Beyond identifying data sources, it's crucial to certify them as authoritative. Promote these certified sources throughout the organization, and ensure that everyone uses only these validated data sources. If someone needs to use an uncertified source, prioritize its certification to confirm its accuracy. Maintaining this discipline is essential for advancing your data strategy effectively.

Data is the new oil, but without the right architecture, it's just crude. Start with your sources, define your needs, and build a governance framework that protects and empowers. Then, watch as your data transforms from a mess of 1s and 0s into the fuel that drives your business forward. It can become a powerful, game-changing approach.

As part of Data Governance best practices, Data domain curation is one of the key focus during the initial stages to understand the business defined data domains and it’s respective systems. Take stock of your existing data sources. Document what data is currently available, where it's stored, and how it's structured. This will help you avoid duplication and identify gaps. Remember that good data architecture is not a one-time effort but an ongoing process that evolves with your organization's needs. Regularly revisit and update your data requirements to ensure they align with changing business objectives and technological advancements , your technical stack, updated metadata and over data landscapes.

Entities and attributes. They're the DNA of your data. Customers, products, orders - these aren't just words, they're the building blocks of your business reality. Name them. Define them. Standardize them. If you do it right, you've got a language that speaks across your entire organization. And now you can get true insights. Mess it up, and you're still moving at the speed of sloth…

When establishing data relationships and rules, it is also important to define the data separations and segregations - as they also define the data governance compliance. For example, the purpose of the data and how it is linked to other data is a critical analysis that has to be performed.

Specify how different data sources will be integrated. Define data transformation, cleansing, and ETL (Extract, Transform, Load) processes. Ensure compatibility between data formats. Assess the need for scalability and performance. Plan for growth by designing an architecture that can handle increased data loads and user demands. Establish DG best practices, including data ownership, data stewardship, and data lineage. Define roles and responsibilities for data management.

Designing Data Models is a forgotten step in analytics world. Its for two reasons, one emergence of Data Lake in the face of Semi-structured and unstructured data and second, storing data in one BIG table. After nearly decade long silence, Data Model, really picked up its importance. Data Lake were not structured and became swamps. One BIG tables became ineffective for history maintenance. Data Domain & Integration leads to Conceptual Model as first step to validate Business understandings. Second, Logical model elaborates each concepts with addition of more descriptive fields. Lastly, Physical model is close to database. Data Types, Merging few logical entities etc... These steps assures everyone follows one notation.

Data Models are the blueprints for any applications. Business requirements provide data elements and data elements are used for designing logical models and further normalized into physical models. While conceptual starts at business level, wherein non-technical people can understand, Logical focusses on semi-technical semi-business level and Physical, stays at technical level. Data Modeling is a fine art, that I remember thoroughly enjoyed doing yester years and still.

Build for purpose - do not try to build a Ferrari if you only need a Toyota . When you’re publishing a roadmap for architecture, think “if I was the person using it daily and my job depends on it, can I trust to depend on it or do I just build my own hack? “

Data architecture without business n process architecture to support that makes this capability difficult to maintain. Those bodies of knowledge may exist under the guise of other names, but in essence will guide this architecture. Information architecture is an evolved state that surrounds this architecture as a wrapper to business n process architecture. The intention of adding the info architecture along with data architecture is to highlight their difference, and their unique offerings.

Skip most of old steps , not possible in today's complex changing environment, example if aws , use their best practices and tools ect continue to each environment with releases monthly new changes to control governance, frameworks from the 70s still in use is pie in sky stuff, pick a few relevant ideas from the old scrolls