Don’t Ask Me to Boil The Ocean

The productivity killers no one talks about

To the leaders who kick off projects without clear objectives and the developers who treat enhancements like an annoying chore — listen up. You might not realize it, but you’re setting the stage for endless frustration and wasted effort. Let me explain.

Two Scenarios, One Truth

Working as a Data Quality Associate has been eye-opening. It’s a whole different world from being a Data Analyst, and I’ve learned this: how you approach the foundation of a project determines whether it thrives or collapses.

Now, imagine you’re in the middle of deploying a solution, everything’s going smoothly… until it’s not. Something comes up.

Scenario 1: You identify a loophole during testing or post-deployment. Maybe it’s a missed requirement or a testing oversight. No issues, right? In an agile environment, that feedback loop should fuel improvement.

Scenario 2: The project has to be reworked from scratch because the objectives were unclear from the beginning. Stakeholders weren’t aligned, the foundation was shaky from the start, and the purpose was as hazy as a foggy morning. Fixing this means starting over — a costly, frustrating mess for everyone involved.

If Scenario 2 sounds all too familiar, you’re not alone. It’s more than frustrating — it’s unacceptable and points to bigger flaws in the problem-solving process. And for Scenario 1, I can only imagine the uphill battle of convincing developers to revisit and improve their work. That’s not ideal either.

Get Ojo Funmilola’s stories in your inbox

Join Medium for free to get updates from this writer.

Remember me for faster sign in

This is where the Plan-Do-Check-Act (PDCA) Cycle comes in — a simple yet powerful problem-solving framework that could save your projects and sanity. Originally championed by W. Edwards Deming, the PDCA cycle offers a structured and iterative approach to drive continuous improvement within organizations. In simpler terms, the model ensures efficiency and successful project management outcomes.

Image from asq.org

You would think everyone knows this and applies it but here we are with the two scenarios described above. So, let’s walk through how you can use PDCA to tackle Data Quality (DQ) issues and improve processes:

PLAN

Everything starts with planning, and no, “We’ll figure it out along the way” is NOT a plan. This is where most problems start — or are avoided. The planning phase is your chance to lay a strong foundation.

1. Identify the problem. Don’t try to boil the ocean. Focus on the most critical issue that will have the highest impact. Conduct an in-depth analysis to identify the precise pressure points and start from there. This is where value identification happens — not later when you should be focusing on value realization.
2. Define clear objectives. Nothing works better than starting with clearly outlining the specific goals and objectives that the team aims to achieve. SMART objectives are what flow into the provision of a clear direction for the implementation of the DQ fixes. There will be a lot of distractions on the way, your objective is what keeps you in line and headed towards your goal.
3. Develop a roadmap, a comprehensive starting point plan that outlines actions, resources, timelines, and responsibilities within the scope of the objectives in sight. Solving DQ issues usually requires the input of many parts of the organization so, you must engage stakeholders early to ensure objectives and actions alignment.
4. Define success metrics. Know what “done” looks like. Define the relevant metrics, KPIs, and value realization benchmarks

DO

This is the execution phase of the problem-solving model where plans meet action. Execution should be the easiest step if planning is thorough. Stick to the plan, but stay vigilant. As a data quality personnel, don’t fold your hands when the solution or fix is being deployed, ensure full alignment with the plan, take note of inconsistencies during testing, and document challenges. These insights will feed into the next steps.

CHECK

Here’s the uncomfortable truth: no matter how good your execution is, like weeds, data quality issues have a way of resurfacing. This is this phase is critical. It involves monitoring outcomes, spotting gaps, and seeking answers to questions like-
❓ What didn’t work?
❓ What needs tweaking?

ACT (or ADJUST)

This is the phase where you refine, optimize, and prepare to start the cycle again if needed. Information from the “do” and “check” phases help identify issues with the process. These issues may include new problems, non-conformities, opportunities for improvement, inefficiencies, and other issues that result in less-than-optimal outcomes. This point is where you rinse and repeat with better insights and stronger foundations.

Why This Matters

Here’s the hard truth: Starting without clarity is like building a house on quicksand. No matter how fancy your tools and processes are, unclear objectives will sink your efforts.

So, before you embark on the next DQ issue resolution, ask yourself:

❓ Am I out to boil the ocean or there is a precise pressure point I am tackling
❓ Are the objectives clear to everyone involved?

Don’t skip the planning phase — it’s where clarity and success are born. Don’t be bullied into thinking you are asking for too much when you request for the enhancement of a solution. It is okay to request an enhancement.

Remember, starting over is far more expensive than doing it right the first time.