Designing a Purpose-driven Data Analytics Architecture

Many organizations aspire to streamline their data analytics architecture. Many look to the latest technological solutions that may have the magic formula to solve all their data challenges. However, I have come across few organizations that create a purpose-driven data analytics architecture. What I mean by purpose-driven is a data architecture that is designed to suit the specific organization data needs and is well suited to meet all their unique business needs. This is very different from implementing a solution provided in Microsoft reference data architecture documentation like the below.

Source: Azure Modern Data Warehouse

Characteristics of a Poor Data Analytics Architecture

1. Poor data quality: If my data is sourced from the wrong source, you can guess the quality of data.
2. High Data Duplicity: If I store different versions of the same reference data in my warehouse, you can imagine the chaos it creates for the downstream processes.
3. High Latency between Source System and Downstream analytical system: If multiple ELT processes are hitting the same source system(relational DB not optimized for scale and analytics) for the same data, you can imagine how slow the data ingestion process will be.
4. High cost of new data analytical initiatives: considering all the above points, clearly any data analytics project will most likely take a long time and become more complex.

The Latest and Greatest Tech stack does not guarantee Good Data

Prerequisites to creating a good purpose-driven data architecture

1. Dedicated Employees: It is not enough to have the brightest data engineer that knows 10 programming languages or an expert SQL/Python developer as your contractor. You need folks that understand the business model, the data requirements and are empathic to the business needs of your organization. You get these folks by investing in your employees, retaining them, promoting them, challenging them, etc. These employees that care will eventually come up with the best technical solutions that are tailored to your business needs. It is also important to have data architects that were former developers and truly understand the unique business challenges.
2. Good Data Governance…