Get the most out of one of your organization’s most valuable assets – Data. Your Challenge The business complains to IT that their data is of poor quality, and insists that they take steps to rectify the issues The business is unable to base their decisions on concrete data, time and money are being wasted on account of data quality issues Clean-up efforts are costly and are unable to ensure longevity of good quality data in the system It is near impossible to implement any sort of ERP or BI initiative with so much defective data in the system Our Advice Critical Insight “You have to plug the holes before you can bail the boat!” – Data clean-up efforts will remain costly and futile if the root causes of data quality issues are not addressed. Plug your holes, then clean up your data. Impact and Result The member will have a firm understanding of how data quality issues are impacting their organization. They will perform an assessment of one domain of their master data system, determine the data quality issues and their sources, and work to solve these issues. From this experience they will create a Manage & Maintain Data Quality SOP which can be utilized on any data domain going forward. They will also create a standardized method of performing regular data system cleanups via the Clean & Correct Data Systems SOP.

1. Structure the Project When you determine that one domain of your master data is of
the utmost importance, or that one is having a proportionately higher business impact than
others, start there.
Determine Project Requirements and Perform a
Data Audit Technical profiling will not give you a complete picture of data quality issues.
User profiling is also required to understand where the issues are.
Optimize the Data Quality Processes To obtain both buy-in and commitment, determine
who the Data Governance Champions are within the organization, and get them on-board
with the project first.
Implement Process Changes Training may not address the issue in high turnover
environments. Work on building a strong data quality culture by streamlining tasks and
taking pride in your organization’s data quality.
Clean and Correct Defective Data Manual clean-ups only make sense when the data set
itself is small. Large data volumes will be too time consuming for a Data Steward to sift
through, making a manual clean-up initiative a poor investment.