Problem
At a Big 4 Consulting Firm, analysts spent a lot of time preparing raw, inconsistent client data before analysis could even begin. Simple but essential tasks like renaming columns, deduplicating entries, or creating derived fields required SQL, macros, or scripts. During peak seasons (like year end closings), data prep backlogs slowed down compliance and client reporting. Inconsistencies crept in as different teams applied slightly different rules, eroding confidence in outputs. The missing link was speed, repeatability, and consistency in turning raw client data into analysis-ready form.
Cleaning by Conversation
Prompts like “Drop rows where compliance_flag is null” applied rules instantly and consistently.
New Columns On Demand
Analysts generated derived fields with natural language, e.g., “Create overdue_months as months between today and due_date.”
Chainable Playbooks
Analysts could chain steps into repeatable playbooks: cleaning nulls, creating columns, deduplicating, and exporting to Excel. Playbooks ensured that the same rules applied consistently across different projects.
Impact
From Manual Prep to Instant Readiness
Cimba transformed data preparation from a bottleneck into an automated, repeatable workflow.
50–70%
Time saved on data cleaning, as routine prep collapsed from hours to minutes.
~40%
Fewer errors in reports, with standardized playbooks eliminating inconsistencies across teams.
