The reporting problem
Reporting effort often goes into assembly. Data is exported from several platforms, cleaned in spreadsheets, combined, charted and formatted, leaving little time to think about what it says. The result lists everything, explains little, and gets skimmed.
Automating the assembly
The first step is getting data to flow on its own:
- Connecting sources. Pulling data automatically from advertising and analytics tools, CRMs, finance systems, operational databases, surveys and spreadsheets.
- A single destination, often a data warehouse, where it can be combined and queried consistently.
- Cleaning and modelling. Standardizing names, dates and definitions so numbers from different systems compare honestly.
- Scheduling. Refreshing data on a regular cycle, or continuously.
- Monitoring. Alerting someone when a source stops sending data or numbers look wrong.
Reports and dashboards then update themselves.
Synthesis: finding what matters
With assembly automated, the effort shifts to meaning: turning a lot of data into a few useful conclusions.
- What changed, against last period, a target or a benchmark.
- Why it changed, drawing on the data and the people involved.
- What it means. Whether the change matters, and for whom.
- What to do next. The decision or action it points toward.
Readers often need one to three findings: a plain-language headline, with the detail there for anyone who wants it.
Where AI helps
AI is well suited to parts of this work:
- Drafting summaries of a period’s data in plain language.
- Spotting changes and anomalies across more metrics than a person could check.
- Combining the numbers with qualitative sources such as survey comments, support tickets or call notes.
- Tailoring the same findings for different audiences, from executives to front-line teams.
- Answering follow-up questions about the data, grounded in the actual numbers.
People review what goes out where it matters, and choosing which findings to lead with stays a human call, with an audit trail where it’s needed. For a media agency, we built a pipeline that gathers campaign data from every source into one warehouse, builds the dashboards itself, and has AI draft each month’s summary for a person to review before it’s sent.
From reports to something people read
A monthly report can become a short, magazine-style issue with a few findings, context and a recommendation. A static PDF can become a live page people explore, or a briefing that arrives where people already work.
Getting definitions right
Reporting confusion often starts with definitions: two teams report different numbers for the same metric because they calculate it differently or pull it from different systems. We document what each number means, where it comes from and how it’s calculated, and build that into the data model.
Privacy and access
Reporting often touches sensitive information about customers, employees or finances. Each audience sees only what it should, personal data follows the organization’s policies, and data sent to AI tools is limited to what the task requires.
Working back from the decision
We start with the decisions a report supports and the people who read it, work back to the data, design the synthesis with whoever produces the report today, and test new formats with readers until it’s something they use.