What data architecture tools do you use to measure your value and impact?

One significant advantage of adopting a data architecture framework is its ability to facilitate smoother cross-departmental collaboration. By providing a unified language and a set of shared standards, different teams can more easily integrate their data-related projects. This cohesion has led to more efficient data-driven initiatives in my past roles, reducing redundancy and ensuring that each department's efforts harmoniously contribute to the larger organizational objectives.

I would consider the following to mark the impact of a successful data architecture - Flexibility for future - Improvised data integration design - Optimized data loading - possible Automations - No code. low code approach - How best the bigger dataset could be managed.

In my experience it is not a discussion about which framework you adopt, but rather that you do adopt one and are aware of the strengths and disadvantages of the chosen framework. Think E2E when choosing. A framework should help you design, maintain, operate and deprecate capabilities and features. Don’t invent the wheel, but don’t get religious about a framework either. Adapt and evolve, no business/governance/culture is 100% the same.

When it comes to assessing the value and impact of the data architecture, I utilize a balanced mix of qualitative and quantitative indicators, including Cost Effectiveness, Strategic Alignment, Capability Enhancement, and Technical Debt. 1. Cost Effectiveness - How efficiently resources (people and tools) are utilized. 2. Strategic Alignment examines how well the architecture supports and propels the business objectives forward. 3. Capability Enhancement measures how the architecture enhances the capabilities it influences, such as reporting, analytics, and data quality. 4. Technical Debt - Insights into potential future costs, guiding us to make sustainable and strategically sound technology decisions.

Looking at the journey software development has been on, it is clear that we need to talk about non functional and functional requirements when it comes to data and data architecture. Don’t make KPIs that aren’t realistic and a good rule of thumb is to follow the SMART goal guidelines. Remember that most of these KPIs often are directly transferable to operations of the platform(s), making them even more important.

Data architecture KPIs are crucial for assessing system effectiveness. Focus on data quality, ensuring accuracy, completeness, and consistency. Evaluate data accessibility, ensuring users can easily retrieve and interact with information. Assess data integration effectiveness across diverse sources. Measure scalability, ensuring the architecture handles increasing data volumes. Evaluate performance speed and responsiveness. Monitor security measures to protect data confidentiality and integrity. Assess cost efficiency in terms of storage and processing. Ensure compliance with data governance policies and regulations. This comprehensive approach provides a holistic view of data architecture performance.

Documentation gets outdated the moment you write it, so avoid the typical redundant “what” explanations. The implementation often explains what in detail, and comments should explain “why” you did the implementation as you did. Architecture drawings are crucial and from my experience having these in a Docs-as-code format actually reduces the risk of them getting outdated. Draw.io can make diagrams as editable png’s thus ideal for git. Other tools could be mermaid.js or structurizr.

The biggest piece I see missing from data architecture documentation is typically the decision record. I can typically figure out what its doing from the model itself, but why it's doing it that way is whats critical. Leaving a decision record behind about why a specific design was chosen. That doesn't go out of date, and it helps whoever come behind understand the factors that were in play when a given decision is made.

Niklaus Wirth's book Data Structures + Algorithms = Programs clearly emphasizes the interconnectedness of data and operations on data. According to my experience, it is quintessential to establish control over the software landscape that processes data. Internal developer portals are indispensable here, as they help in documenting and visualizing relationships between software systems and how they contribute to business capabilities of an enterprise. Through specifying APIs, it is possible to depict data flows, see what happens to data, and reason about orthogonal perspectives bundled under the quality attribute of dependability. There are both open-source (like Backstage) and commercial (like Port) platforms for crafting developer portals.

Using a Data Architecture Framework makes the process more comprehensive using a set of principles, standards, and guidelines that outlines how data is to be organized, managed, and utilized within an organization. It encompasses everything from the underlying models and structures, such as conceptual and logical data models, to data flows, quality, and lifecycle management. It also defines the governance, including roles and responsibilities, and the technology stack required to support the architecture. This framework is crucial for ensuring data meets quality standards and is leveraged effectively to support the organization’s objectives and strategic data usage.

Operation of data products and components in general are often overlooked in the design phase of data architectures. Without making an architecture that you are able to maintain and properly operate, the systems will get bottleneck’s quickly and you will most like end up redesigning the systems within a short amount of time. Think E2E, and ensure you both cover consumers, developers, operators etc.

Analyst Review and Comparative Analysis As Technologies evolve at an exponential pace, architectures to embrace these promptly to leverage the capabilities and features. Hence periodic reviews with Industry analysts and doing comparative benchmark analysis with the peers in the industry is an essential practice if not a tool. These practices are indispensable for accurately measuring, assessing the impact, and keeping the architecture function efficient and aligned with business objectives.