Hi everyone,
I’m a student learning data reporting on my own and trying to build good habits early, not just make reports that “look right.”

I’m comfortable with basic dashboards and charts, but I get stuck on questions like:

• How do you decide what actually matters to report vs what’s just noise?
• How do you think about structuring reports for different audiences?
• What mistakes should beginners avoid so reports stay clear and useful as data grows?

Would really appreciate how experienced folks approach reporting thinking, not just tools. Trying to learn the right mindset early.

Thanks in advance.

I’m running into a recurring issue where two of our internal dashboards show conflicting numbers for the same KPI. One pulls from a cleaned reporting layer, and the other queries the raw tables directly. Both were built by different teams at different times. When stakeholders ask which one is correct, I genuinely don’t know how to explain the gap without sounding like “it depends.”
How do you approach resolving these mismatches and establishing a single source of truth without forcing the entire org to rebuild everything from scratch?

I struggle with finding the balance between being too high-level and too detailed. If I keep things concise, leaders ask for more breakdowns. If I add breakdowns, they say it’s too much information.
How do you define the ‘minimum viable insight’ for executive reporting so the report stays useful without becoming a 20-page dump?

For years, organizations relied on weekly, monthly, and quarterly reports to track performance. These reports were meticulously prepared, QA-checked, and circulated across teams as the single source of truth. But the landscape is changing.

With real-time dashboards, auto-refreshed pipelines, and AI assistants capable of generating on-demand summaries, many business users no longer wait for formal reports. They want instant answers, contextual explanations, and insights that adapt as quickly as the business does.

Most organizations rely heavily on dashboards but too often, they only describe what happened rather than guide what to do next. As analytics matures, the goal is no longer just tracking KPIs; it’s creating

decision intelligence reports that connect data insights directly to business actions.

Modern reporting should be dynamic, interactive, and predictive  highlighting not only trends but also root causes, risks, and recommendations.

Yet, achieving that requires more than new tools; it demands better data pipelines, clear metrics alignment, and a shift in how teams consume insights.