What's your approach to sharing data architecture designs with stakeholders?

In my view, when presenting Data Architecture designs, key stakeholders include: 1) Business Stakeholders: They seek insights into what type or category of data is being collected and the potential use cases that can be driven for customers/users using that data 2) Data Engineers/Developers: Their focus lies in data collection, tracking, source-to-destination data movement, data transformations, and connections to source and destination data stores 3) Compliance Teams: They need clarity on PII and sensitive data for compliance guidance 4) Platform/Infrastructure Team: Their role encompasses source data connections, data storage setup, data format comprehension, and compliance policy implementation following guidance from compliance team

Sharing data architecture designs with stakeholders involves effective communication and ensuring that technical information is presented in a way that's understandable and relevant to their needs. Below are key items, I would focuds Know Your Audience Focus on Key Components Fact based stories Having Interactive Sessions/Demonstrations: Address Concerns and Benefits Document and Distribute Seek Feedback and look for continuous feedback Follow-Up and Support Provide Context

Effective communication of data architecture designs with stakeholders begins with understanding my audience and tailoring my message accordingly. Stakeholders, ranging from business users to developers, have diverse technical knowledge and distinct priorities. Business users may seek insights into how data informs their decision-making, while developers are interested in data integration and access. Therefore, employing techniques like personas, user stories, and scenarios allows me to empathize with my audience, ensuring my message resonates with their specific needs and preferences. This personalized approach enhances the clarity, relevance, and impact of my data architecture communication efforts.

People always make mistake while communicating the data architecture to the stakeholders. Here I have suggestion if there are business stakeholders then only use the conceptual/business or logical architecture to explain your data architecture. If audience is technical then you can use the technical solution architecture among with operational architecture.

When presenting the data architecture design to business stakeholders, consider using two slides: one detailing data categories (e.g., on-site behavior, marketing) and use cases, and another with a conceptual data model depicting data connections from various sources. For the technical teams (platform and data engineering): 1) Develop a physical data model diagram to illustrate data flow and interconnections across systems 2) Prepare a document specifying data types (structured, unstructured, semi-structured) 3) Compile a document outlining the source system data schema and desired schema in the destination store For compliance team, it's essential to create a document detailing sensitive information available from each data source

Again do not confuse here about the physical format of your architecture. You could use below artefacts /deliverables to present your data architecture : Conceptual Data Architecture Component design and interface design Conceptual Data Model Logical Data Model Technical Component Design Infrastructure Architectural decisions Assumption , Issues , Risk and Dependency Log

As a first step you must create the Glossary of terms to get the definition of complex business terms in the business. For business people use the business language and for technical people use the technical jargons.

Using straightforward language is crucial when communicating data architectures. Jargon and ambiguous terms obscure understanding, especially for non-technical audiences. Using recognised terms like entity, attribute and relationship to depict data models boosts comprehension. I also leverage analogy and metaphor to relate concepts to everyday experiences my audience can grasp. Most importantly, I solicit feedback on clarity, then refine my language and articulation. Ensuring all stakeholders, regardless of background, can parse the architectures is key to driving engagement and action. Ideas and designs mean nothing if others can’t understand them. Plain language and inclusive communication will unlock the value of our data architectures.

I believe building a traceability across the design is best practise. Your business requirement references should be referred in the design , implementation and testing. Providing the high level context and description about decision is desirable.

Providing meaningful context is vital for stakeholder adoption. Not just what the architecture is, but why it is designed that way. I relate the rationale to business goals, pain points and capabilities to showcase how the architecture drives strategic value. Mapping designs back to functional and non-functional requirements demonstrates fit. Discussing alternative approaches weighed illustrates the thoroughness of analysis. Data flow diagrams are perfect for demonstrating the end-to-end movement of data. Metadata catalogues paint a picture of governance. Linking architectures to organisational objectives, initiatives and business processes grounds them in purpose.

Peer review and inclusion of review comments in the data solution architecture is key to the success of any data related project.

We miss opportunities when we present architectures as complete versus works-in-progress open to collaboration. Constructive criticism uncovers oversights and challenges assumptions. Surveys, workshops and open forums provide channels to gather diverse perspectives. Incorporating feedback iteratively allows architectures to evolve and improve. I use read-out sessions with users and technical teams to validate designs. Their questions and ideas often enhance my original thinking. Most importantly, these interactions build understanding and investment in the architecture. Stakeholders gain confidence knowing their voices shape the solution.

This is not one time process as I have seen you need to do adopt the iterative approach to finalise your deliverables. It requires the continuous improvement.

Feedback and peer reviews are integral part of architectural decisions. It is really important to discuss the decisions and reach to the conclusion after peer reviews only as its not impacting you only but overall solution and teams hence walkthru is mandatory.

Each organization should have a unique perspective on data. Data architects or individuals in similar roles should assess the data maturity level of each stakeholder at the outset, as this can serve as a starting point. To facilitate better communication, everyone should be on the same frequency regarding data.