How to Improve Communication During the Software Development Lifecycle

"Do you understand the requirements?" "Yes!" = "I don't know" "No!" = "I don't know" *silence* = "I definitely don't know" After 20 years of building engineering teams, I've learned the hard truth: binary questions kill collaboration. They're a false comfort that masks deeper communication gaps. The real problem? Both "yes" and "no" often mean the same thing: "I'm not comfortable having a deeper discussion about what I don't understand." This silence tax is expensive: • Features get built based on assumptions • Teams work in isolation, duplicating effort • Architecture decisions made without crucial context • Integration issues discovered way too late • Deadlines missed because the real complexity stayed hidden Game-changing questions I now use instead: 1. "What parts seem unclear or could use more detail?" 2. "How would you approach implementing this?" 3. "What other systems do you think this might affect?" 4. "Walk me through how you'd test this" 5. "What concerns you about this approach?" Because here's the truth: Great software isn't built on yes/no answers. It's built on collaborative problem-solving and shared understanding. The most valuable conversations start when we move past "Do you understand?" to "Let's understand this together." What questions do you use to spark real technical discussions?

The only way to prevent data quality issues is by helping data consumers and producers communicate effectively BEFORE breaking changes are deployed. To do that, we must first acknowledge the reality of modern software engineering: 1. Data producers don’t know who is using their data and for what 2. Data producers don’t want to cause damage to others through their changes 3. Data producers do not want to be slowed down unnecessarily Next, we must acknowledge the reality of modern data engineering: 1. Data engineers can’t be a part of every conversation for every feature (there are too many) 2. Not every change is a breaking change 3. A significant number of data quality issues CAN be prevented if data engineers are involved in the conversation What these six points imply is the following: If data producers, data consumers, and data engineers are all made aware that something will break before a change has deployed, it can resolve data quality through better communication without slowing anyone down while also building more awareness across the engineering organization. We are not talking about more meaningless alerts. The most essential piece of this puzzle is CONTEXT, communicated at the right time and place. Data producers: Should understand when they are making a breaking change, who they are impacting, and the cost to the business Data engineers: Should understand when a contract is about to be violated, the offending pull request, and the data producer making the change Data consumers: Should understand that their asset is about to be broken, how to plan for the change, or escalate if necessary The data contract is the technical mechanism to provide this context to each stakeholder in the data supply chain, facilitated through checks in the CI/CD workflow of source systems. These checks can be created by data engineers and data platform teams, just as security teams create similar checks to ensure Eng teams follow best practices! Data consumers can subscribe to contracts, just as software engineers can subscribe to GitHub repositories in order to be informed if something changes. But instead of being alerted on an arbitrary code change in a language they don’t know, they are alerted on breaking changes to the metadata which can be easily understood by all data practitioners. Data quality CAN be solved, but it won’t happen through better data pipelines or computationally efficient storage. It will happen by aligning the incentives of data producers and consumers through more effective communication. Good luck! #dataengineering

One of the most impactful changes I've seen in quality happens when you implement one specific process: a 30-minute QA-Dev sync meeting for each feature before coding begins to discuss the implementation and testing strategy. When I first bring this up with a client, I get predictable objections: Developers don’t want to "waste" their time. Leadership doesn’t want to "lose" development time. Testing is necessary anyway, so why discuss it? Our QA doesn’t couldn't possibly understand code. The reality is that the impact of effective testing can be remarkably hard for an organization to see. When it goes smoothly, nothing happens — no fires to put out, no production issues. As a result, meetings like this can be difficult for leadership to measure or justify with a clear metric. What confuses me personally is why most engineering leaders say they understand the testing pyramid, yet they often break it in two, essentially creating two separate pyramids. Instead, you should have a collaborative session where QA and Dev discuss the entire testing pyramid — from unit tests to integration and end-to-end tests — to ensure comprehensive and efficient coverage. Talking through what constitutes effective unit and integration tests dramatically affects manual and end-to-end testing. Additionally, I'm continually impressed by how a QA who doesn’t "understand" full-stack development can still call out issues like missing validations, test cases, and edge cases in a method. QA/Devs should also evaluate whether any refactoring is needed, identify potential impacts on existing functionality, and clarify ambiguous requirements early. The outcome is a clear test plan, agreement on automated and manual checks, and a shared understanding that reduces late-stage bugs and improves overall product quality. #quality #testing #software