ibi™ Data Quality

Make better decisions with better data

With AI-assisted workflows, and a knowledge hub of reusable components for profiling, validating, and fixing enterprise data elements, ibi™ Data Quality software engages both business and technical users. It consistently improves the quality of data anywhere it enters your landscape, via online apps, data streams, message queues, and batch interfaces. Use the ibi™ Data Quality solution as a standalone product, or take advantage of its web APIs to improve the effectiveness of your downstream integration, migration, BI, analytics, AI/ML, and MDM efforts.

What is Data Quality?

Data quality describes the degree to which data fits the purpose it was intended for. Data is considered high quality when it accurately and consistently represents real-world entities and scenarios.

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Benefits

Save Costs & Time

Increase the effectiveness of matching algorithms and the accuracy of your golden records by profiling all contributing data sources to recommend validation and cleansing rules that can be applied in your MDM ingestion workflows.

Empower Citizen Users

Provide a low/no-code user interface and unified experiences for non-technical users to accomplish more with less. Simple intuitive browser-based UIs enable citizen users, data analysts, and data scientists to use a wide range of data quality services without the need for IT support and without wasting time on manual and repetitive data validation tasks.

Accelerate Cloud Migration Projects

Get the most out of your cloud investment by preventing bad data from entering it. Improve accuracy, reduce maintenance, and govern efficiently by adding data quality firewalls to data migration jobs. Improve the reliability and accuracy of data to result in only high-quality data.

Improve Business Outcomes

With data quality rules and API services, deliver only trusted data to decision points. Enable the desired business outcomes: growth, customer satisfaction & retention, process & resource optimization, cost reduction, risk mitigation, and increased shareholder value.

Promote Accountability

Enable collaboration between data managers and data consumers with up-to-date and reliable data quality metrics. With business users participating in data governance programs, everyone in the organization feels responsible for implementing data quality best practices and maintaining high data quality.

Capabilities

Profile, Classify & Score

Comprehensively profile data with AI-enabled data classification that includes automated checks for uniqueness and pattern detection, identification of sensitive data, correlation analysis, and many more advanced statistical analyses.

Cleanse, Standardize & Enrich

Invoke prebuilt rules that accomplish a lot in a single workflow: evaluate data against specifications, apply structure and content fixes, replace abnormal and missing values, and augment incomplete data from internal or third-party reference sources.

Web UIs and Dev Toolkit

Balance the needs of technical and non-technical users via a platform of data quality tools and services, including a no-code, browser-based UI delivering rich experiences through customizable, reusable, and scalable services.

Cloud Enablement

Increase availability and improve scalability of data quality services through Kubernetes orchestration that provides flexible deployment options in multi-cloud and hybrid environments.

Observability

Leverage native scoring capabilities to measure, track, monitor, and rank data’s trustworthiness over time so users and experts can effectively collaborate and improve visibility of data quality initiatives.

Seamless Integration

Take advantage of robust API services to connect diverse data sources to a wide range of data quality services, giving you the flexibility to introduce DQ anywhere in your data pipeline for unified user experiences