The vendor is required to provide that software solutions that currently exist from qualified vendors to mitigate the risk of data discrepancies by means of data cleansing solutions.
- Collects, discovers, or imports metadata from partners. Builds or imports lineage to perform rapid root cause analysis of data quality issues and impact analysis of remediation. Applies passive and active metadata findings
- Provides easily understood, friendly interfaces with intuitive designs to facilitate user engagement.
- Increasing demand for generative ai programs and growing concerns around data consistency brings to light issues that could arise due to the possibility of incorrect or conflicting information being stored across various data sources.
- This disparity, coupled with increased demand for more effective tactics to monitor and predict potential problems, has caused the state to search for a resilient solution or solutions that can easily be scaled and managed to address the state’s data cleansing challenges.
- Requirement:
• Active metadata support: Collects, discovers, or imports metadata from partners. builds or imports lineage to perform rapid root cause analysis of data quality issues and impact analysis of remediation. applies passive and active metadata findings.
product usability: provides easily understood, friendly interfaces with intuitive designs to facilitate user engagement.
• Profiling and monitoring/ detection: Supports statistical analysis of diverse datasets, ranging from structured to unstructured data and from on-premises to cloud, to provide business users with insight into the quality of data and to enable them to identify data quality issues.
• Rule discovery, creation, and management: Designs, creates, and deploys business rules for specific data values. enables rules to be called within the solution or by third-party applications for data validation purposes, which can be done in batch or real-time mode.
• Data transformations: Segments, formats, modifies, and organizes diverse datasets based on government, industry or local standards, business rules, metadata, and machine learning. enables data modification to comply with domain restrictions or integrity constraints.
• Matching, linking, and merging: Matches, links, and merges related data entries within or across diverse datasets using a variety of traditional and new approaches, such as rules, algorithms, metadata, ai and machine learning. suggests potential matches and tunes results.
• Usability engages and supports: Various roles, including data engineers, stewards, data quality analysts, data architects, data integration analysts, business analysts, data preparation experts and other nontechnical business roles, in data quality initiatives.
• Workflow and issue resolution: Manages data quality issue resolution through the stewardship workflow. enables business users to easily identify, quarantine, assign, escalate and resolve data quality issues through collaboration, pervasive monitoring, and case management.
• Validate accuracy: Validate deviation in records caused by abbreviations, proper names, common suffixes or prefixes and addresses.
• Standardizing formats and data types: Standardize formats and data types such as dates in currency fields including symbols or commas, and leading/trailing spaces in text fields.
• Repair missing values: Removing duplicates, fix structural errors, filter outliers, anomalies, and invalid/illogical data points.
- Questions/Inquires Deadline: January 27, 2024