Overview
In this episode of The Dashboard Effect, Brick Thompson and Greg Brown make the case that most business reporting is built around the wrong question. Income statements and financial reports tell you what happened. What most organizations actually need is visibility into what is happening, early enough to do something about it before the month closes and the window to act has passed.
The conversation covers the distinction between leading and lagging indicators, why automation is the foundation that makes real-time operational visibility possible, and how BI creates the most value when it reaches across the organization rather than stopping at the finance team. See how Blue Margin’s Managed Analytics & Insights helps organizations build the automated, custom reporting environments that surface leading indicators in time to act on them rather than after the results are already locked in.
What This Episode Covers
The Limitation of Financial Reporting (0:58 – 2:07)
Financial reports are essential and insufficient. Income statements and similar outputs are rearview mirror metrics: accurate accounts of what has already happened, delivered days or weeks after the period they describe. By the time the data is ready, the opportunity to influence the outcome it reflects has usually passed. Organizations that rely solely on financial reporting are perpetually reacting rather than adjusting.
Leading vs. Lagging Indicators (2:09 – 4:51)
Lagging indicators confirm what happened. Leading indicators provide visibility into what is happening now and what is likely to happen next. The hosts make the case for building operational metrics into BI environments alongside financial reporting, giving leaders a view of the business as it unfolds rather than as it was. That visibility is what creates the agility to change behaviors and outcomes before they become history.
The Need for Automation (6:17 – 8:48)
Manual reporting processes, whether in spreadsheets or partially automated pipelines, introduce human error, create key-man dependency, and limit how frequently data can be refreshed. The hosts emphasize that the pipeline from transactional systems like ERPs, CRMs, and timekeeping tools to actionable dashboards should be fully automated. Automation is not just an efficiency gain. It is what makes consistent, trustworthy, real-time data possible at scale.
Operational Alignment Across the Organization (9:52 – 10:21)
BI has its greatest impact when insights reach beyond the leadership team to the employees whose daily actions drive the metrics leadership is watching. When people understand the connection between what they do and the numbers that matter to the business, organizational alignment improves in a way that financial reporting shared only at the top cannot produce.
Beyond Standard Reporting Modules (10:22 – 11:48)
The reporting functionality built into most ERP systems reflects generic business logic rather than the specific value chain of any particular company. Creating metrics that genuinely reflect how a business operates often requires combining data from multiple sources, pairing ERP data with timekeeping systems or CRM data, for example, to build custom measures that standard modules cannot produce on their own.
Who It’s For
This episode is worth your time if you are a CEO, COO, or operations leader who has felt the frustration of making decisions based on data that is already weeks old, a finance or FP&A professional trying to complement financial reporting with operational metrics that give the business more room to respond, a BI or data team evaluating how to build dashboards that drive behavior rather than just describe outcomes, or any organization that is ready to move beyond what its ERP reporting module can produce and build something that reflects how the business actually works.
Why It’s Worth a Listen
The leading versus lagging indicator distinction sounds conceptually simple, but most organizations are significantly more invested in lagging indicators than they realize. This episode makes that imbalance visible and offers a practical argument for why correcting it produces better decisions rather than just more data.
The automation discussion is particularly grounded. The hosts do not treat automation as an aspirational goal but as the baseline requirement for operational BI to be trustworthy. Manual steps in a reporting pipeline are not just inefficiencies. They are reliability risks that undermine the credibility of the data every time something is entered incorrectly or a refresh is missed.
And the point about reaching beyond the leadership team resonates with anyone who has seen a well-built dashboard sit unused outside the finance department. Data that helps frontline employees understand how their work connects to the numbers leadership cares about is data that changes behavior, which is ultimately what makes a BI investment worth making.
