Data
Data
Incentive Partners has seen data of all shapes and sizes, and our team of compensation and data experts know best practices for everything from small to enterprise-sized data. Our data services focus on preparing, cleaning, governing, integrating, and optimizing data so that your SPM processes can empower your teams for success.
Our Specialties
Data Remediation
Avoid poor upstream data quality from disrupting your planning and compensation processes by working with Incentive Partners to help you identify and correct incomplete, inaccurate, duplicated, or inconsistent data across compensation and revenue systems.
Data Preprocessing
Incentive Partners' data preprocessing practices focus on preparation, standardization, validation, and transformation of source data before it enters sales planning or compensation platforms in order to improve speed and reduce technical complexity.
Data Quality Assessment & Governance
Incentive Partners offers a review of data quality, ownership, governance standards, controls, and reporting consistency across source systems to prevent downstream issues, improve reporting accuracy, enable auditing, and build confidence in planning and compensation outcomes.
Integration Design & System Mapping
Implementation Readiness Review
Incentive Partners works with customers to provide design and documentation of data flows between CRM, ERP, HR, data warehouses, and other business systems to identify gaps, improve efficiency, and reduce operational risk.
Incentive Partners gives customers an evaluation focused on determining organizational data preparedness for a successful sales planning or compensation implementation, to help identify and resolve issues before a project begins.
Attention Enterprise Companies!
If your organization has multiple disparate data sources and millions of records then these services are tailor-made for you. Incentive Partners is fully confident we can support the identification of root causes, align people and processes, and build scalable foundations when facing compensation complexity, fragmented processes, data quality issues and technical adoption friction.