What do you do if you're faced with a complex data architecture project?

Identify Business Problems or Opportunities: Understand the specific challenges or opportunities that the project aims to address. Stakeholders and Users: Identify the stakeholders involved in the project, including decision-makers, end-users, customers, and any other relevant parties. Data Sources, Formats, and Quality: Determine the sources of data required for the project, such as internal databases, third-party datasets, or real-time streams. Data Usage, Analysis, and Reporting: Define how the data will be used, analyzed, and reported to derive actionable insights. Expected Outcomes and Benefits: Clearly articulate the expected outcomes and benefits of the project in terms of tangible results and business impact.

As per my experience most of the data platform related projects fail because business doesn't know what exactly they want and how to use the platform. Hence early engagement of business stakeholders is a must for the successful data project.Also many data architects forget about the non-functional requirements which becomes a monster after some time.

When tackling a complex data architecture project, consider the following steps: Requirements Gathering Data Profiling and Exploration Design High-Level Architecture Data Modeling ETL (Extract, Transform, Load) Data Security and Privacy Scalability and Performance Optimization Monitoring and Maintenance Documentation and Communication Testing and Validation Collaboration and a deep understanding of business requirements and technical constraints are essential for success in complex data architecture projects.

The architecture strategy depends on many variables - 1) Goal and vision of the organization 2) Primary purpose of the project/product 3) Timelines 4) Current and futuristic needs 5) Maturity of the organization 6) Cost 7) skilled resource availability in the market 8) User Base 9) Volume and quality of data 10) scalability

Here you need to understand the goal and objective of your organisation towards data platform , otherwise you will build something and that will remain unusable. Hence both short term and long term objectives must be identified.

The data architecture solution is based in how organize their components to work togheter and support all business activities. This components will face 2 points: > How work internally specific assigned job; > How collaborate with another components: how receive and give data each other. The modularity, scalability, performance, fault tolerance, available, maintenance, etc depend these points; and your solution must address it from design perspective.

The design should address the current and future requirements. Example : When you implement/create the data models , try to include both generalization and specialisation in the model so that they are flexible enough for the future changes. At the same time data management , data governance and data compliance are the integral part of data architecture and design so you must focus on overall data strategy.

When you implement the data solution architecture in physical environment make sure you include all the components and address all the functional and non functional requirements.

Prototype, Test, Refine: Develop a minimal viable product (MVP), test rigorously, and continuously improve based on feedback. This ensures efficient resource allocation and data quality, crucial in sectors like healthcare. Technology Expertise: Utilize data warehouses for structured data analysis (e.g., insurance), data lakes for diverse data sources (e.g., media), data pipelines for automation (Qlik Replicate, Talend), and data analytics tools (Qlik Sense) for user insights across industries (marketing, finance, operations). Data Quality Focus: Implement data validation rules, conduct verification checks, and leverage data monitoring tools to ensure data accuracy, integrity, and overall architecture health.

I would advise the below approach to tackle a complex data architecture project: - Conduct a thorough needs assessment to align with business objectives. - Break down the project into manageable phases with clear milestones. - Leverage proven methodologies like Agile for iterative development and feedback. - Adopt modularity in design to accommodate future scalability and changes. - Use data modeling effectively to visualize and address the complexities of data relationships. - Prioritize data governance and compliance throughout the architecture. - Foster collaboration across cross-functional teams for diverse insights and expertise. - Incorporate automation and CI/CD pipelines to streamline development and deployment.

Team is very important aspect and must be considered. Assess the availability of required skills both within and outside the organization. and build your hideen team