Data Quality is the reliability and application efficiency of data, particularly when kept in a data warehouse. Data quality is much more than just records, fields and values—it’s about establishing business. From personal decisions, customer relationship to control of inventory, operations and even regulatory compliance, quality data forms the very foundation of a successful and flourishing enterprise. Data quality assurance (DQA) is the method employed to verify the reliability and effectiveness of a data. Maintaining data quality requires going through the data periodically and scouring it. Generally this involves updating it, standardizing it, and de-duplicating records to make a single view of the data, even if it is stored in multiple variant systems. Poor data quality costs businesses $300 B/yr. 75% of businesses have experienced significant problems associated with Data Quality. Only 33% of businesses report satisfaction with Data Quality.
To Improve Data Quality for your organization Timewell Services include
- Data Quality Assessment using IBM Information Analyzer and IBM Discovery
- Identify a Data Quality Strategy and Roadmap for your organization using the IBM Data Quality Tools best practices and methodologies
- Usage of Data Quality Tools at various stages of the Data Integration/Data Warehousing Project
- Work with your business partners to identify Data Quality Rules for Source Data
- Identifying the Data Quality Dimensions that are most suited for your Organization
- Design and Develop Data Quality Dashboards and Scorecards to monitor Data Quality
- Work with your business partners to remediate the data quality issues
- Use IBM QualityStage to Investigate and Standardize Source Data and also remove duplicates
- Use IBM InfoSphere Discovery to profile source data across 20 different sources and provide a bottom up relational or star schema data model
- Use IBM InfoSphere Discovery to do source to target profiling and provide skeleton ETL code to ease your data integration work