Provides an external data management platform that centralizes, cleanses, and enriches third-party data for enterprises, ensuring it is accurate and up-to-date. By integrating with systems like Databricks and automating data stewardship, it reduces manual processes by over 80% and mitigates the financial impact of poor data quality, which can cost companies 15% to 25% of revenue.
Funding
$8.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises struggle with managing the influx of third-party data, leading to inaccuracies and inconsistencies across systems. This results in flawed business insights and decision-making, ultimately impacting revenue and operational efficiency. The manual processes required to cleanse, enrich, and maintain this data are time-consuming and costly.
Solution
Matchbook AI offers an external data management platform that centralizes, cleanses, and enriches third-party data, ensuring data quality and consistency across the enterprise. The platform integrates with existing systems like Databricks to automate data stewardship, reducing manual processes and improving data governance. By providing a single point of integration for all external data, Matchbook AI enables organizations to transform data into actionable insights and make data-driven decisions with confidence. The platform delivers real-time notifications of data changes that may negatively impact business, allowing for proactive mitigation of risks associated with poor data quality.
Target Audience
Matchbook AI targets large enterprises across various industries that rely on third-party data for critical business functions such as customer data mastering, supplier portfolio management, and risk management.
Features
- Intelligent matching algorithms for cleansing and enriching external data
- Centralized repository for managing all external data attributes
- Real-time notifications for data changes impacting business performance
- Automated data stewardship to reduce manual processes
- Integration with Databricks and other enterprise systems
- Pre-mastering processes of external data records
- Support for compliance with real-time tracking of spend by vendor diversity