The biggest risk with automated reporting isn’t an obviously wrong number. It’s a wrong number that looks completely right.
As businesses automate more of how they collect, process and report information, errors can increasingly pass through systems without attracting attention. By the time questionable data reaches a dashboard or board report, it may have been categorized, calculated and aggregated several times over, giving the final figure a level of credibility the original input never deserved.
Co-Founder and Chief Product & Strategy Officer at Thrive.
The answer is to scrutinise the data much earlier in the process. Rather than relying on checks once a final figure has been produced, technology can help businesses interrogate the quality of data as it enters the reporting process, before an initial mistake has the chance to become an authoritative-looking result.
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Building quality into the process
The move away from manual reporting has solved plenty of problems. Information that once had to be pulled together from spreadsheets, emails and different teams can now be collected, calculated and reported far more efficiently. That is hardly surprising, but the quality of the underlying data remains a major stumbling block. Automation does not resolve inconsistent interpretation; it can often scale it.
In fact, 77% of UK companies say data is their biggest challenge to using AI effectively. As automation and AI play a greater role in how businesses handle and interpret information, getting those foundations right becomes even more important.
A person manually compiling a report may spot something that looks unusual and question it. Automation without validation can remove some of those natural moments of scrutiny, processing information exactly as instructed without necessarily recognizing that something is missing, inconsistent or simply implausible.
An incorrect input can become even more inaccurate as it moves through subsequent calculations. If the wrong quantity is entered, then mapped to the wrong metric or valuation, the resulting impact figure can be materially overstated. Catching the error at the point of entry prevents it from compounding further down the line.
Why social value proves the point
Social value measurement provides a useful example. As organizations face growing pressure to prove the impact they create, the quality of the data behind those claims matters. Yet while 95% of senior sustainability professionals consider high-quality data important, only 27% say they currently have it.
Reporting has historically been less mature than the delivery itself. Social value activity can happen across different parts of an organization, with practitioners understandably focused on creating impact first and capturing the information behind it afterwards. The result is that organizations can be doing genuinely valuable work while still struggling to demonstrate it accurately. Poor evidence of impact is not necessarily evidence of poor impact, but externally, the distinction can be difficult to make.
The challenge is turning that activity into reliable, consistent data. Information may have been captured differently across teams, recorded against the wrong measure, or lack the evidence needed to substantiate it. That could mean an FTE job figure being claimed incorrectly, missing evidence, outdated definitions, or someone selecting the wrong metric because its name looks superficially similar to the right one.
For smaller businesses, navigating social value requirements can already be challenging, with 73% of SMEs reporting difficulties formulating social value commitments for government tenders. Making reporting more robust therefore cannot simply mean adding more complexity. Technology needs to make it easier to capture the right information in the first place.
This matters increasingly as social value data carries greater weight. Figures can inform bids and contract performance, and organizations are increasingly expected to substantiate the impact they claim to create. Getting the data right is therefore becoming a question of organizational credibility, not simply reporting accuracy. If leaders cannot trust the evidence behind a figure, they cannot confidently use it to evaluate performance, allocate investment or substantiate claims externally.
From automated reporting to intelligent validation
So, what does better automation look like? Impact data must be structured, validated and understood from the start, rather than just relying on audit or manual checking at the end to uncover problems. That means using technology to test information as it is captured, before errors become embedded in the reporting process.
Questionable inputs are far easier to resolve at the point of entry, while the person entering them still understands the context, rather than months later when teams are trying to reconstruct what happened. Technology can help by flagging information that looks incomplete, inconsistent or implausible before it becomes embedded in a wider calculation.
Consistency across teams matters too. Building clear definitions and requirements into the reporting process can reduce that room for interpretation and help ensure information is being captured in the same way across an organization.
Businesses also need to think about what happens to the evidence behind a number as it moves through the system. A final figure should still be traceable back to its source, so teams can understand where it came from, how it was calculated, and what supports it if challenged later. Maintaining that traceability becomes increasingly important as more stages of the reporting process are automated.
None of this means removing human judgement. The aim is to use automation to focus that judgement where it adds most value, rather than asking people to manually check everything or clean up problems at the end.
Automation has already made reporting faster and more efficient. The next challenge is ensuring it also makes reporting more reliable. The quality of the checks built into the process will become just as important as the speed at which information can be processed. Confidence in the final number should come from knowing the data behind it can stand up to scrutiny.
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Benjamin Fellowes
