Management Reporting Pack Automation

To an extent. If project updates include data (budget vs actual on projects, milestones achieved), that data feeds in automatically. The narrative around it – someone still needs to write those updates (unless you have them in systems we could pull from). Fully automating text is challenging (though possible with templates and language generation for repetitive parts). But even automating 80% of data-heavy content lets teams focus on writing unique narratives for that month and assembling special focus slides (like deep dives on particular issues). Those ad-hoc deep dives can be integrated – often based on analysis prepared in Keboola (like special cost analysis); you'd still craft it into slides, but having data ready cuts time significantly. Essentially, automation covers the routine backbone of packs; special topics remain somewhat manual but easier because core numbers are done. Over time, if certain ad-hoc content becomes frequent, incorporate it into automated packs. So it's iterative – start with repetitive sections (financials, KPIs) and free time for more qualitative parts. Many companies find once basics are automated, they can actually enrich packs with more insightful analysis – which might not be automated but now they have time for it. Quality goes up even if that part is manual.

Testing and controls. We test pack output for a few cycles in parallel with existing processes to ensure it matches. We implement validation checks – does balance sheet balance, do calculated ratios match underlying figures? If something's off, the system flags or even halts generation until resolved. Once stable, since it's the same logic every time, risk of human error is far lower than manual prep (no copy-paste mistakes). Any changes to templates or logic are version controlled and tested. You can include standard labels ("numbers in $ millions") that are always correct if set once. After initial testing, each run is consistent. Some teams initially double-check key numbers against GL or previous methods for a couple months, then gain confidence to rely on it. We keep logs of data sources and refresh times so you know data is current. If something fails to load (a system was late), pack generation notifies "Data incomplete" rather than silently giving wrong info – major improvement over manual when someone might not notice missing pieces. An automated pack can be made very robust with systematic checks and initial validation

It reduces tedious use of those skills, but finance professionals still use them – just more for final touches and analysis rather than raw assembly. Instead of being Excel jockeys consolidating data, they use Excel for ad-hoc analysis which is more valuable. Instead of spending hours in PowerPoint aligning numbers, they spend minutes customizing charts or adding clarifying annotations. Skills shift to higher-value usage. Some storytelling aspects (design of visuals) might still need human creativity – automation ensures data accuracy and consistency in style, but humans decide to highlight particular insights by adding infographics. Those skills don't vanish; they're applied more effectively. Importantly, finance teams can develop new skills – using BI/dashboard tools for interactive presentations, or interpreting data trends – because they're not bogged down in admin. It makes finance roles more analytical and less clerical, which is good for job satisfaction and value-add to the company. No one's job is lost – jobs are enhanced to focus on analysis, which is what most finance folks want to do anyway.