In today's data-driven world, the quality of your data can make or break your business. Poor data quality can lead to costly errors, misinformed decisions, and compliance issues. That's where our Data Quality Managed App “Certifi DQ” comes in.

Certifi DQ Managed App is a robust and comprehensive solution designed to help organizations of all sizes ensure the integrity, accuracy, and reliability of their data. Whether you're dealing with customer records, financial data, product information, or any other type of data, our app empowers you to maintain data that you can trust.

Key Features :

Data Profiling : Gain deep insights into your data with advanced profiling tools.
AI/ML based Data Anomaly : Identify anomalies, inconsistencies, and data quality issues.
Auto Tagger : AI/ML based tagging of entities and attributes for better rule governance.
DQ Rule Recommendation : Automated data quality rule recommendation based on profiling and tagging.
Configuration driven Data Quality check : Automate the process of quality check of your data. Identify duplicates, nulls etc, configure custom and SQL based rules on entities, multi-columns and columns.
Multiple DQ Engine : Rule execution engine can be distributed, standalone or sql based based on data quality workloads. DQ check can happen data at rest and also in motion.
Data Governance: Establish and enforce data quality rules. Ensure compliance with industry regulations and internal policies.
Scalable and Secure: Certifi DQ is built on a scalable architecture, ensuring it can grow with your organization's needs.
Integration: Seamlessly integrate with your existing systems and applications. Our app supports API integrations for a smooth workflow.
Data Quality Dashboards: Visualize your data quality metrics through intuitive dashboards. Track progress, identify trends, and make informed decisions

Certifi DQ addresses : -

Data Inaccuracy and Inconsistency: Inaccurate and inconsistent data can lead to costly errors, poor decision-making, and damaged customer relationships. Data quality tools can identify and rectify inaccuracies, ensuring that data is reliable and trustworthy.
Data Duplication: Duplicate records can cause confusion and inefficiency in various business processes. Data quality tools can detect and eliminate duplicate entries, resulting in a more streamlined and efficient database.
Data Completeness: Missing or incomplete data can hinder data analysis and reporting. Data quality tools can fill in missing information through data enrichment and validation, ensuring that datasets are complete and usable.
Data Compliance and Governance: Compliance with data protection regulations (e.g., GDPR, HIPAA) is a critical concern for many organizations. Data quality tools can help enforce data governance policies, ensuring that data is handled in accordance with legal and regulatory requirements.
Data Standardization: Inconsistent data formats, naming conventions, and units of measurement across different data sources can lead to integration challenges and errors.

Certifi DQ- Cognitive Data Quality FrameworkFractal Analytics Inc.

Certifi DQ- Cognitive Data Quality Framework

Fractal Analytics Inc.

Certifi DQ- Cognitive Data Quality Framework

Fractal Analytics Inc.

Certifi DQ- Cognitive Data Quality Framework
Fractal Analytics Inc.