Enrich
Create repeatable workflows that perform simple to complex data transformations on data of any size along with insight generation without writing any code.
Data Transformation Tools Designed for Finance and Operations Workflows
Most data transformation tools require data engineers to build and maintain the pipelines that finance teams depend on. PlaidCloud's Enrich environment inverts that model: point-and-click data preparation tools let finance analysts configure complex extraction, transformation, and loading workflows directly. Without writing code or submitting IT tickets. As purpose-built data processing software for financial operations, Enrich handles everything from basic CSV cleaning and ERP data normalization to multi-step allocation models running across billions of rows. Every transformation is version-controlled, auditable, and reusable. Meaning the model your team builds once runs automatically at each period close, with consistent logic and a complete lineage record.
Do Learn Do
Build Models Iteratively, Explore Results, and Respond
- Explore any size data quickly
- Save Table Explorer analysis as an Extract step with one click
- Build and revise iteratively with point-and-click data transforms
Batteries Included for Business
Business-Oriented Data Transformations
- Purpose-built transforms for finance and supply chain operations
- Cost allocations with market-leading assignment approach
- Direct two-way integration with ERP systems
Native Support for Multidimensional Data
Build Models Shaped Like Your Business
- Build or load dimensional hierarchies
- Tight integration between business transformations and dimensions
- Support alternate hierarchies, attributes, properties, and values
Allocations at Transaction Level Scale
Perform Allocations at Any Level Including Transactional
- Best-in-class allocation engine out-performs all other technologies in the market
- Point-and-click assignments provide a comprehensive view of the model setup
- Natively supports even the most complex allocation scenarios, including assignments at any level within a hierarchy
Power Tools
Zero Compromise. Run Your Code on Big Data
- Work with Python or SQL and operate on large, performant datasets
- Augment standard workflows with user-defined functions written in Python
- Write and test from your own IDE or Jupyter
- Manage and deploy with Git
From Raw ERP Data to Enriched Analytics. Without Writing Code
PlaidCloud's data transformation software bridges the gap between transactional ERP data and the enriched, model-ready datasets that drive profitability analysis, transfer pricing calculations, and management reporting. Each data analytics workflow is built iteratively: explore the data, apply transforms, review results, and refine. All within the same environment, without switching tools. For teams that do need to work in code, Python and SQL transforms are natively supported alongside point-and-click steps in the same workflow. Giving technical and non-technical team members a shared, integrated environment without compromise.
Browse the full transform library
More than 180 pre-built transforms across import, export, tables, allocations, machine learning, spatial, SAP, documents, and workflow control.
Explore all transforms →Frequently Asked Questions
What does Enrich provide?
A library of more than 180 low-code data transformations, from imports, joins, and allocations to machine learning, NLP, spatial analysis, and an LLM step, so finance and operations teams shape, blend, and model data without writing code, iterating with a do-learn-do approach.
Does PlaidCloud support multidimensional data?
Yes. Hierarchies and dimensions are native, so organizational structures, product rollups, and account trees behave correctly in models and reports.
Can technical users write code?
Yes. SQL and Python steps sit alongside the visual transforms, so analysts stay low-code while technical users drop to code when they want it.
How do we see the effect of a transformation?
Results are explorable immediately after each step runs, so you can inspect intermediate tables, verify logic, and respond before building the next step.
Insights
Related Insights
