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Data Quality
Data Quality
The quality of data and information largely defines the quality of the decisions made using it. Poor quality marketing data means inefficient campaigns and irritated customers. Poor quality financial data means inaccurate reporting and poor business intelligence. Furthermore, poor quality data doesn't just mean inaccurate data; it often means duplicated data or no data at all.
Good data quality is now widely regarded as a must-have by business and government, yet most would admit that the quality of their data falls far short of their needs. However, poor data quality is usually only the symptom of an underlying problem. That problem typically involves a complex array of people, process and system issues. Achieving good data quality can require a systematic re-engineering of the fabric of an organisation.
What we do
We help our clients improve their data quality with a series of specific services:
- Data governance strategy - We help clients define strategies and objectives for data quality based on industry benchmarks for best practice. These are then used as the basis for data quality improvement programmes.
- Data quality audits - We use data profiling and analysis tools to perform data quality audits. Such audits typically represent a key planning tool in large data integration projects such as data warehouse developments.
- Data Quality Scorecards - We implement processes and systems for measuring data quality an automated operational basis.
- Selection and implementation of data quality tools - The market is awash with tools for data quality and profiling, cleansing, matching, address validation and metadata management. We help clients select and implement the right ones for them.
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