Every report and every automation is only as sound as the data beneath it. This category covers master data maintenance, duplicates, inconsistent spellings and the question of which system leads for which field. Alongside that runs the path from paper to case file: capture, text recognition, indexing, linking to the order, access rights and retention. And finally the reporting itself — which key figures genuinely help a mid-size company, where they come from, how often they need refreshing and where their limits lie. Putting data in order usually frees more time than the next automation project.
Which section of a personnel file carries which retention period, who may access it, what gets logged, and how inspection and access become routine cases.
Capturing head knowledge, documenting critical workflows and testing the stand-in before an experienced colleague leaves: schedule, metrics and sources.
Monthly report still assembled by hand? How to replace it: fixed data sources, agreed metric definitions, a scheduled run and a controlled distribution.
Five metrics that genuinely steer a mid-sized company: lead time, on-time delivery, rework, utilisation, open receivables — source, refresh cycle and limits.
Retention duties and deletion duties only appear to conflict. How to build a filing concept with periods per record type, legal holds and documented runs.
Immutability, traceability, machine analysability: what the German GoBD mean for digital document storage and what belongs in the process documentation.
Text recognition realistically assessed: clean sources versus carbon copies, stamps and handwriting, measuring quality, fields to extract, effort per type.
From a folder of PDF files to a searchable archive: indexing, linking to the case, versions, access rights, retention and how to handle the existing backlog.
Inventory, field mapping, trial runs and totals checks: how to move data out of a legacy system and how to evidence that the transfer really was complete.
Duplicate customer and product records: how they arise, how similarity matching and address normalisation find them, and how to merge without losing history.
Customers, articles, suppliers: which system leads, which fields are mandatory and how to clean up master data without halting your daily operations.