One of the trickiest situations I often encounter is when a stakeholder asks for a dashboard or report but doesn’t give much detail on what exactly they want to track or how they want it presented. For example, I was once asked by a client in the commercial real estate sector in New York City to create a dashboard listing business opportunities coming through a specific sales channel. They gave me very little direction beyond that, no specific KPIs, no format, no priority.
This is a perfect example of how I approach report creation: breaking down vague requests into clear deliverables using an Agile mindset,delivering value in pieces that build into a scalable, maintainable solution.
Step 1: Understand the Business Context and Purpose
The very first thing I do is pause and ask myself:
- What problem is this dashboard supposed to solve?
- Who will actually use this dashboard, and how?
- What decisions should it help them make?
In the commercial real estate example, I didn’t start by opening Power BI and adding charts. Instead, I reached out and asked:
- “What do you mean by ‘business opportunities’ exactly? Is it leads, deals, proposals, or something else?”
- “What’s the current process for tracking these opportunities?”
- “What’s the main goal for this dashboard? To monitor volume, quality, conversion rates?”
- “Who needs to act on this data – sales managers, executives, or marketing?”
This helped me realise that the primary focus was on monitoring the flow of opportunities to understand pipeline health and spot bottlenecks in a channel that was underperforming. They also wanted something that could be updated frequently and easily shared with stakeholders.
Step 2: Break Down the Report Into Agile Deliverables
Instead of building a massive dashboard all at once, I planned an iterative approach:
- Sprint 1: Deliver a simple overview showing total opportunities by stage (new, qualified, proposal, won, lost) for that channel.
- Sprint 2: Add trends over time, how many opportunities enter each stage weekly/monthly.
- Sprint 3: Drill down into opportunity attributes – property types, deal size, source contact.
- Sprint 4: Include a “red flag” section highlighting stalled or high-value opportunities.
This way, I could deliver value early with the basics and evolve the report based on feedback.
Step 3: Start Small, Focus on Data Quality and Logic
Even though the final dashboard would be visual-heavy, I spent most of my time in the early stages on:
- Verifying the source data was reliable and fit for purpose (checking for duplicates, missing values, consistent stages).
- Building the data model to reflect the opportunity stages and their lifecycle clearly.
- Defining clear calculation logic for KPIs like conversion rates, average deal size, time in stage.
This foundation meant that each new visual added later would be trustworthy and reusable.
Step 4: Communicate Progress and Gather Feedback Early
Once Sprint 1 was ready, I shared a simple report with the stakeholders. This was never the “final” version — but it gave them something concrete to react to. The feedback helped me learn:
- They wanted to see opportunities grouped by region within NYC boroughs, something I hadn’t considered initially.
- The team was more interested in lost opportunities analysis than I expected.
- Some users preferred tables to charts because of their familiarity.
These insights shaped the next sprints and kept the project aligned with business needs.
Step 5: What to Watch Out For
From my experience, here are some common pitfalls in these vague request scenarios:
- Overbuilding before knowing what matters: It’s tempting to add every possible metric, but this creates clutter and confusion.
- Ignoring data limitations early: If the source data is messy or incomplete, don’t pretend it’s perfect. Highlight issues upfront.
- Lack of iteration: Delivering one big dashboard at the end often means it doesn’t hit the mark and needs rework.
- Assuming one size fits all: Different users may need different views or levels of detail.
Why This Approach Works
By breaking the problem down, staying close to the business goals, and iterating in small steps, I was able to deliver a scalable solution that evolved with the client’s needs, not a static report that was obsolete as soon as it launched.
Summary: Agile Thinking in Report Creation
- Start by asking questions to frame the why behind the request.
- Break the work into manageable chunks that deliver value quickly.
- Build strong data foundations before visual polish.
- Share early versions and use feedback to adjust course.
- Expect and plan for change, it’s part of real-world BI.