How do you design data architecture for a new project?

1 Assess the project scope and objectives

The first step is to understand the scope and objectives of the project. What are the main questions or problems that the project aims to solve or answer? What are the expected outcomes and deliverables? What are the key performance indicators (KPIs) and metrics that will measure the project's success? These questions will help you define the business and technical requirements of the project and the data needs and expectations of the stakeholders.

2 Identify the data sources and types

The next step is to identify the data sources and types that will feed into the project. Data sources can be internal or external, structured or unstructured, static or dynamic, and so on. Data types can be numerical, categorical, textual, spatial, temporal, and so on. You need to evaluate the availability, quality, and relevance of each data source and type for the project. You also need to consider the data volume, velocity, and variety that the project will handle and how they will impact the data architecture.

3 Choose the data models and formats

The third step is to choose the data models and formats that will represent and store the data in the project. Data models are the logical structures that define how data is organized, related, and accessed. Data formats are the physical representations of data that determine how data is encoded, compressed, and serialized. You need to select the data models and formats that best suit the project's data sources, types, and requirements. Some common data models are relational, hierarchical, network, document, graph, and key-value. Some common data formats are CSV, JSON, XML, Parquet, Avro, and ORC.

4 Design the data flows and pipelines

The fourth step is to design the data flows and pipelines that will move and transform the data in the project. Data flows are the sequences of steps that define how data is ingested, processed, analyzed, and consumed. Data pipelines are the systems that implement and automate the data flows. You need to design the data flows and pipelines that will ensure the data quality, integrity, and security throughout the project lifecycle. You also need to optimize the data flows and pipelines for performance, efficiency, and scalability.

5 Implement the data governance and quality rules

The fifth step is to implement the data governance and quality rules that will monitor and control the data in the project. Data governance is the set of policies, standards, and roles that define who can access, use, and modify the data and how. Data quality is the measure of how fit the data is for its intended purpose. You need to implement the data governance and quality rules that will ensure the data compliance, accuracy, completeness, consistency, and timeliness in the project. You also need to establish the data audit, review, and feedback mechanisms that will enable the data improvement and maintenance.

6 Test and validate the data architecture

The final step is to test and validate the data architecture that you have designed for the project. Testing and validation are the processes of checking and verifying that the data architecture meets the project's requirements and expectations and that it works as intended. You need to test and validate the data architecture using various methods and tools, such as data profiling, data sampling, data cleansing, data integration, data analysis, and data visualization. You also need to document and communicate the data architecture design, implementation, and results to the stakeholders and users.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?