How to Document Your "Digital Backbone" Data Platform of the Future

During my information architecture mentoring and workshop sessions, I frequently encounter questions around knowing what topics to cover when trying to flush out the details of a modern data platform. That can be a comprehensive explanation. So for these types of topics it really needs to be thought out well and analysis performed on the current and future state views. To do this we use a form of an architecture document for that platform.

This architecture document for implementing a digital data backbone platform should cover at least these 8 sections. Of course, modify for your needs and add and subtract where needed. Remember to only document the level of information that makes business sense, be careful to not over engineer.

Digital Backbone Platform Architecture Template

1. Executive Summary. Context and background information, outlining the need for a digital data platform that is linked to strategically relevant business objectives.

2. Long-Range Strategy Alignment and Principles. Alignment and rationalization of the corporate strategy into

3. Approach to Information Management Governance. Defined approaches of an operating discipline for creating standardization, making unified decisions, and ensuring data and technology life cycle management is achieved. Governance bodies would also provide risk management through the identification and mitigation strategies for potential risks.

4. Develop, Refine, or Recommend Information Architecture Standards.

   1. Data standards

   2. Platform and patterns standards

   3. Technology standards

5. Existing Challenges and Identified Gaps. Key decisions points needed to overcome any barriers to implementation found in the current state information landscape.

6. Proposed Digital Backbone Platform Solution Architecture. A digital backbone platform typically refers to a tailor-made architecture offering reusable data services, data pipelines, semantic layers, or APIs. This is achieved through a blend of various data integration methods such as bulk/batch processing, message queuing, virtualization, streaming, event handling, replication, or synchronization, all orchestrated systematically.

   1. Architecture Principles. Statements that drive architecture decisions along with design considerations across various digital backbone platform concepts.

   2. Architecture Quality Attributes. Non-functional requirements of the architecture that include but not limited to: reliability, extensibility, scalability, and performance.

   3. Link of Information Assets and Business Capabilities. Detailed analysis of business needs and objectives of the data platform aims to address. Typically addressed through a Business Information Model (BIM).

   4. Data Fabric Platform Architecture Definition. A data fabric is generally a custom-made architecture that provides reusable data services, pipelines, semantic tiers or APIs via combination of data integration approaches (bulk/batch, message queue, virtualized, streams, events, replication or synchronization), in an orchestrated fashion.

   5. Intelligence and Analytics Architecture Definition. Analytical capabilities that support building and running business intelligence, data science, machine learning and artificial intelligence applications

   6. Architecture trade-off analysis. Deliberate approach to make structured choices in the architecture.

7. Data Security and Compliance Guidelines. Definition of the guidelines for Security measures, data protection policies, and compliance with regulations.

8. Technical Investments and Decisions Needed. Based on the business outcomes to be achieved outline the software, hardware, and network infrastructure, etc. required.

9. Appendices. Any additional material like technical diagrams, data schemas, etc.