How Redpoint enables automated data quality

Introduction

This page illustrates how Redpoint enables automated data quality.

Broad, native connectivity to a wide array of clouds, applications, and databases, supporting both streaming and batch processing. This ensures seamless integration and efficient data flow across various platforms.
Inline hygiene & validation performed at the point of data entry, allowing for the normalization of formats. This validation includes customer name standardization and nickname tables to account for name variations as well as nicknames. This process effectively rejects or routes erroneous records early in the workflow, enhancing overall data quality.
Postal & address standardization capabilities, including compliance with USPS standards and CASS reporting. Additionally, options for geographic enrichment are available to enhance the accuracy and usability of address data.

Visual, high-performance pipelines designed for no-code or low-code environments facilitate the implementation of data quality rules, enrichment processes, and schema management on an enterprise scale. This empowers users to manage data effectively without extensive programming knowledge.
Golden Record creation is integrated into the data pipeline, moving beyond traditional ETL (Extract, Transform, Load) processes. This approach ensures that a single, accurate representation of each entity is maintained, enhancing data integrity and usability.

Deterministic and probabilistic matching techniques are employed with customizable rulesets, allowing for candidate grouping to optimize performance. This results in tighter matching rules based on various identifiers such as name, address, phone, and email, leading to fewer duplicates and greater precision in identifying individuals.
Anonymous-to-known stitching capabilities enable the integration of data from web, mobile, and offline sources, ensuring continuity and a comprehensive view of each profile. This is crucial for maintaining consistent and enriched customer interactions.

Survivorship & lineage tracking provides clarity on which data sources are prioritized for each attribute and the rationale behind these decisions. This transparency is essential for effective data governance and management.
Quality KPIs such as completeness, validity, deduplication rates, and deliverability are monitored as part of the data pipeline, with established remediation paths. This systematic approach is supported by comprehensive Data Management projects and reports.

Continuously updated profiles are maintained through event-driven calculations and merges, ensuring that data is always current and ready for next-best actions, personalized experiences, and informed decision-making.
Seamless activation processes enable integration with execution stacks such as Braze, Salesforce Marketing Cloud (SFMC), Adobe Experience Platform (AEP), Tealium, and Snowflake/BigQuery. Feedback loops are established to continuously enhance data quality over time, optimizing marketing and operational efforts.