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Data Quality Services

We provide cost-effective methods to monitor, control, and improve information quality using sophisticated data interrogation and statistical analysis tools and techniques. As businesses automate complex integrated processing of key operations, data quality becomes a requirement, not an option. Information can be a valuable asset, if it is accurate. Data quality is critical to today's leading businesses in all industry sectors. At Deloitte, we utilize a number of  products to review our clients' information and business systems for the purpose of establishing or enhancing their security and controls measures.

Audit Command Language (ACL) is a data analysis tool that allows us to access micro, mini, or mainframe data without the need for data conversion or import. ACL allows us to perform in-depth analyses that are not feasible with manual procedures.

Cost Efficient Methods. We use statistical analysis tools and techniques to monitor, control, and improve data integrity.

Improve Data Quality. We provide audit procedures to assess and improve data quality prior to, during, and following software implementation projects, including ERPs. During an ERP implementation we offer the following services:

» Design appropriate statistical sampling techniques to quantify the impact of potential conversion-coding errors 
» Purify the data during the conversion phase of the implementation
» Develop internal assessment tools that our clients can use to monitor compliance issues 
» Select samples of data to analyse the accuracy of records and assess the financial impact of differences
» Implement a continuous audit process, based on statistical techniques, using a proprietary audit tracking and reporting tool.

Standard Audit Procedures. We use the procedures to:

» Verify data completeness
» Recreate reports
» Take sampling for confirmations
» Data timeliness

Understanding and Analyzing the Client's Data. We use statistics to determine the average, highest, and lowest values of the data population. We use classification and age analysis on key fields to analyze the data. 

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