Here's how you can use problem solving skills to craft data architecture that aligns with business needs.

Businesses require data to run and they are not always well versed with architecture. For the DE team understanding the requirements and making the information available is prime objective. This needs to be done by keeping the following checks: 1. Ensure you are not duplicating data 2. Fastest way you can deliver the data 3. Have 2-3 approaches ready along with the budgeting aspect for approvals. 4. Have a continuous check with the stakeholders whether you are going in correct direction. Most important, set real expectations and if the project can be delivered in phases.

Engage stakeholders across departments to gather insights into data usage and requirements. Identify critical data and necessary integrations to align architecture with business goals.

Analysis of data before working on it is crucial. Be clear about the following: 1. What is the input format of data and how the output is expected (e.g. unstructured to structured OR semi structured to structured) 2. Before jumping in get the details about source i.e. how the connectivity will be established, what is the refresh frequency. 3. Ensure to capture meta-data 4. Quality of data needs to be assessed; for instance is there frequent changes in the incoming data 5. Select tools and techniques accordingly. 6. What is TTL for data and how many users/applications will use it. 7. Does the data contains any PII/ critical information.

Assess current data for patterns, inconsistencies, and gaps. Identify the types and volume of data your architecture needs to handle, addressing potential issues like data silos.

If you work in an organization where multiple business teams are present and most of them work on the similar data sources but derive different information from same data then have a 'Framework'. Setting up frameworks helps in making data available quickly to multiple teams in different environment. Try writing modularized code which acts as a jigsaw piece and can be fit into different environment.

Create a blueprint for data flow, storage, and access. Choose databases, warehouses, and tools, ensuring flexibility for future needs.

Set up and configure databases, data management software, and governance protocols. Address challenges during implementation.

Continuous Monitoring: After implementation, use analytics tools to monitor the performance of your data architecture. This helps in identifying bottlenecks or inefficiencies. Proactive Adjustments: Make necessary adjustments based on performance data to ensure that the architecture remains aligned with business objectives and operates efficiently.

Iterative Improvement: Commit to regularly revisiting and refining your data architecture to incorporate new technologies and adapt to changing business goals. Informed Decision-Making: Use your problem-solving skills to make informed decisions during the refinement process, ensuring that the architecture continues to meet the evolving needs of the business.