Same data, different names. Non-standardized source formats, variable schemas, repetitive manual work.
3 sources = 3 different formats
Manual mapping required every time
One format for all your data
All sources → One consistent format
Automated mapping • Saved as versions • Reusable forever
Getting from complexity to consistency requires...
Hours of Manual Work
Enterprise data solutions solve repeatable problems with consistent formats. Excel handles one-off tasks. But what about the gap in between—where you need the same output every time, but the inputs keep changing with different formats and schemas?
You consolidate data regularly—monthly portfolio reviews, quarterly acquisitions, every deal closing
But each time, the data comes from different counterparties, sellers, or sources with non-standardized formats and variable schemas
PII, financial data, loan details—compliance requirements make IT push this work back to business users
Custom ETL pipelines cost tens of thousands of dollars to build and maintain. The ROI calculation breaks down when each deal involves different data sources with variable formats—even if consolidation happens monthly.
Sensitive data creates organizational barriers. With PII, financial records, or health data, security and compliance requirements often mean the work stays with business users who have domain expertise and data ownership.
Enterprise platforms excel at solving repeatable problems with consistent data sources and standardized formats. Deal-specific work with variable inputs and non-standardized schemas falls outside their design paradigm—it's simply a different category of automation need.
The Result?
Business users own the gap. You're left manually consolidating non-standardized data in Excel for every deal, every acquisition, every monthly review—wasting hours on repetitive work with variable formats that should be automated.
Every deal involves a different counterparty. Each uses their own systems with non-standardized data formats and different schemas. You need standardized data for analysis and pricing—but custom pipelines aren't economical for variable data sources with inconsistent structures.
Acquiring portfolios from different fund managers. Each uses their own management system with variable data formats. You need unified data for valuation—but building acquisition-specific pipelines doesn't scale for non-standardized schemas.
Multiple sources submit data in their own non-standardized formats with variable schemas. You need standardized analysis-ready data—but compliance requirements for sensitive information create organizational barriers.
Every target company has different ERP systems, platforms, and software with non-standardized data formats. You need normalized operational data—but building pipelines for uncertain deals with variable schemas isn't a viable investment.
See how Datally transforms these challenges with non-standardized formats into automated workflows—no more manual consolidation, no more Excel hell.
See How Datally Solves These Challenges