Data Quality

Make sure that your data is of sufficient quality to make decisions!

A reporting solution is only as good as the quality of the data that is input. Different rules apply to the quality of the data required for transactional purposes versus that for reporting purposes. For example, entering a generic business type within the transactional system may not cause a potential customer to be ignored, but this would cause any decisions made from reporting to be skewed and potentially lead to less effective recommendations for improvement.

Data quality is not a one-off exercise, but should be viewed as a continuous and ongoing process for improvement within the organisation. As well as identifying where problems occur, the data quality management process should provide information to allow operational resources to develop plans for improvements.

We don't just assess your environment and identify issues, we create a solution where you can continually monitor for data quality issues and understand the risk to your business.

ASSESSMENT PHASE

Perform initial data quality assessment to identify anomalies, assess candidate source systems, and provide a baseline for tracking data quality going forward

DQ MONITORING PROCESS

Design and implementation of a data quality monitoring process that looks for data issues that are either fixed through the transformation process or communicated to the relevant data quality owner for action

ALERTING, REPORTING & ANALYSIS

Execution of the monitoring process to produce alerts, operational reports, and data quality metrics to assess data quality improvement over time

Our data quality management solution focuses on turning business rules into data metrics. Our solution puts you in control of your information environment allowing you to:

Proactively assess the quality and integrity of data required by the end users to satisfy their business needs

Identify opportunities for data improvement

Work with the end users, business analysts, and operational staff to develop plans for data improvement

Develop auditing and balancing procedures necessary to satisfy data integrity requirements

Drive the business toward resolving the most critical and impacting data issues identified