When every team has its own reports, but nobody trusts the numbers

The symptom: more reports, less clarity

In many growing businesses, “data-driven” has come to mean something very specific: everyone has a different spreadsheet.

The finance team has its margin workbook. Operations has a capacity model. Sales has a pipeline dashboard. E-commerce and warehouse have their own reports from their platforms. Your ERP or accounting system produces yet another set of figures.

None of these are completely wrong. But together, they create a new kind of risk:

Leadership meetings that spend 30 minutes arguing about “which numbers are right”.
KPIs that quietly drift apart between departments.
Decisions delayed because nobody wants to bet the quarter on a spreadsheet they do not own.

When every team has its own report, the problem is no longer “lack of data”. It is the absence of a shared, decision-ready view.

The real problem is not visualisation, it is definition

The reflex response is often to buy a better dashboard tool, commission a new set of reports, or ask IT for “one view of the truth”.

Those tools can help, but they do not solve the underlying issue: your organisation does not yet share the same definitions and data backbone.

“Gross margin” might be defined three different ways across finance, sales, and product.
“Active customer” might mean last 30 days in one system and last 90 days in another.
“On-time delivery” might be measured from requested date in one report and promised date in another.

If these definitions are not aligned, no amount of visual polish will create trust. The issue is semantic, not cosmetic.

Start from the decisions, not from the dashboards

A more practical starting point is to work backwards from the decisions that actually move your business, such as:

Which customers and products are truly profitable after returns, rebates, and freight?
Where are we at risk of stock-outs that would hurt key customers?
Which marketing channels are generating customers who stay and buy again?

For each of these decisions, you can ask:

What is the minimum set of metrics we need to see, every week, to make this call?
Which systems do those metrics currently live in (ERP, WMS, e-commerce, CRM, spreadsheets)?
Where are we re-typing or re-calculating the same values in multiple places?

Once the decision is clear, the role of data becomes simpler: not “everything, for everyone”, but “the right signals, reliably, for this decision”.

Building a thin but strong data spine

Most mid-market organisations do not need a full-blown data lake to get started. What they need is a thin but strong data spine that connects the few tables and fields that truly matter.

In practice, that often looks like:

Identifying 2–3 critical flows (for example, order-to-cash, purchase-to-pay, and inventory movements).
Defining shared, documented metrics for those flows: how revenue, cost, margin, and service levels are calculated.
Engineering a small set of reliable data pipelines from your ERP, warehouse system, e-commerce platform and other key sources.
Exposing those pipelines into a handful of curated views or dashboards that management actually uses.

The goal is not to centralise every piece of data on day one. It is to build a backbone strong enough that other analysis can safely hang off it.

Where software engineering and analytics work together

This is where our background in both software development and data analysis comes together.

On the engineering side, we focus on:

Using APIs and integrations instead of fragile exports and uploads.
Version-controlling data transformations, so changes are traceable and reversible.
Embedding checks and alerts so anomalies are caught before they hit the board pack.

On the analytics side, we work with your finance and operations leads to:

Align metric definitions so “margin”, “on-time”, and “customer value” mean the same thing across teams.
Design reports that match how decisions are actually made, not how tables happen to be structured in the database.
Turn recurring questions into stable, maintained views instead of one-off spreadsheets.

The output is not just nicer charts. It is a small but growing collection of decision-ready numbers that people can rely on.

What about AI and predictive models?

AI and advanced analytics have a role here, but again, they are most useful once the basics are in place.

With a clean data spine and agreed definitions, we can start to:

Highlight orders or customers at risk based on past patterns.
Flag margin erosion before it shows up in month-end results.
Test simple “what if” scenarios on pricing, lead times, or reorder points.

The difference is that these models are built on top of numbers the business already trusts, rather than trying to “fix” bad data with more complex algorithms.

A more useful data conversation for leadership

For many leadership teams, the most valuable shift is not a particular tool or technology. It is a different kind of conversation about data.

Instead of asking:

“Can we get another report that shows X by Y by Z?”

The questions become:

“Which few numbers do we need to see every week to sleep better at night?”
“Where are we still making big decisions based on unverified spreadsheets?”
“What would it look like if our ERP, warehouse, and e-commerce systems all fed the same core metrics?”

If those questions resonate, the next step is usually not a new reporting tool. It is a focused effort to define shared metrics, engineer a reliable data backbone, and build a rhythm where decisions and numbers reinforce each other over time.