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What is Fraoula Data Auditor?

Fraoula Data Auditor acts as a robust solution for evaluating data quality and auditing schemas, specifically designed for teams involved in data engineering, analytics, governance, and compliance. It thoroughly analyzes data structures along with sample records to identify problems such as schema drift, inconsistencies in field types, high rates of null values, outlier data, and breaches of predefined rules, ultimately generating a risk report that offers suggested corrective actions. This tool proves advantageous for scrutinizing data pipelines that feed into warehouses, analytics systems, risk management frameworks, or AI applications, especially within the finance and healthcare industries. Users have the flexibility to select from a range of workflows, including a rapid browser-based assessment or more detailed audits, with options for both cloud-based and self-hosted setups. The insights produced empower teams to identify possible data risks and formulate remediation strategies before the data is leveraged in subsequent processes, thereby improving overall data integrity and trustworthiness. By taking proactive measures to tackle these challenges, organizations can maintain the precision and reliability of their data for all essential functions and applications, ensuring that decision-making processes are founded on solid, trustworthy information. This effective approach not only enhances productivity but also fosters greater confidence in data-driven initiatives across the organization.

What is Data Quality on Demand?

Data plays a vital role in multiple sectors of a business, such as sales, marketing, and finance. To fully leverage this data, it's important to maintain its integrity, protect it, and manage it effectively throughout its entire lifecycle. At Uniserv, we believe that data quality is a core principle of our identity and the services we offer. Our customized solutions convert your customer master data into a crucial asset for your business. The Data Quality Service Hub ensures that your customer data remains of the highest quality across all locations within your organization, including those overseas. We offer services that align your address information with international standards, utilizing the best reference data available. Furthermore, we validate email addresses, phone numbers, and banking information with meticulous attention to detail. If your data has duplicate entries, we can quickly pinpoint them according to your defined business requirements. Often, the identified duplicates can be merged automatically using predefined rules or categorized for manual assessment, which promotes an efficient data management workflow that boosts operational productivity. This thorough strategy for ensuring data quality not only aids in compliance but also builds trust and credibility in your customer relationships, ultimately leading to stronger business outcomes. An unwavering commitment to data quality fosters a culture of accountability within the organization, encouraging all departments to prioritize accurate data handling.

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Integrations Supported

Amazon Redshift
Amazon S3
Apache Airflow
Apache Kafka
Databricks
Google Cloud BigQuery
PostgreSQL
Snowflake
dbt

Integrations Supported

SAP Store

API Availability

Has API

API Availability

Pricing Information

$0
A free tier is available. A 14-day pilot is paid; enterprise pricing is discussed with the sales team.
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Fraoula

Date Founded

2024

Company Location

United States

Company Website

www.fraoula.co

Company Facts

Organization Name

Uniserv

Date Founded

1969

Company Location

Germany

Company Website

www.uniserv.com/en/business-cases/customer-data-management/data-quality/

Categories and Features

Data Quality

Not specified

Categories and Features

Data Quality

Data Deduplication

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