Key Features:
Cloud-Based
Secure web application
Scalable Data Ingress
highly scalable data intake from diverse data sources
Data Validation + Quality Assessment
continual evaluation of incoming data to assess quality prior to intake and data processing
Visibility
of data flow, validation, data quality, and anomaly detection in a single application
Anomaly Detection
pattern monitoring across data sources to identify and address irregularities
Data Egress
highly scalable service to make data available to connected applications
Systems Monitoring
alerts and notifications
Integrity’s quality data pipelines enable complex data analytics, data science, and machine learning at scale across all available data domains.
Integrity Enables:
Data Lakes
creates the foundation for an enterprise Data Lakes architecture for analytics.
Data Cleaning
detects and corrects or removes inaccurate, incomplete, or irrelevant data, making analytics more accurate and reliable.
Cataloguing
facilitates data discovery and access by providing data analysts with a comprehensive view of the data assets available to them.
Hydration
adds complementary data to existing datasets to provide greater context for data analytics.
Linking
identifies and links data from multiple sources to create a comprehensive view of a particular entity.
Machine Learning Model Deployment
dynamically trains models to infer, predict, and forecast.
Visualization
offers built-in visualizations for essential analyses and tools for ad hoc analytics.
Integrity enables and systematically implements data governance best practices.
Integrity Enables:
Data Quality Management:
ensures that data is accurate, complete, consistent, and relevant to the organization's needs.
Data Security Management:
protects data from unauthorized access, use, disclosure, or destruction, through security controls, policies, and procedures.
Data Compliance Management:
ensures data management practices comply with relevant laws, regulations, and industry standards, such as GDPR, HIPAA, or ISO.
Data Lifecycle Management:
manages data throughout its lifecycle, from creation to archiving or deletion, and ensures that data is properly stored, backed up, and disposed of.
Data Access and Authorization Management
controls who has access to data, how they access it, and what they can do with it, based on their roles, responsibilities, and permissions.
Data Stewardship
assigns responsibilities for managing and maintaining data quality, security, compliance, and lifecycle to designated individuals or teams, who act as data stewards.
Data Analytics Governance
ensures data used for analytics or decision-making purposes is accurate, reliable, and trustworthy, and that analytics models and algorithms are transparent and explainable.