You're facing conflicting data quality standards. How can you align IT and business stakeholders effectively?

Where there is seen to be "no quality" needs at a business level it's important to help people understand the need for quality. Encapsulate the reason why quality is important for the business and stakeholders allowing for further future improvement, risks it prevents, growth it enables and costs it saves. Some critical industries will prioritise safety, regulatory or customer requirements to minimise defects that could cause harm: In the food industry - Contaminated food can lead to health risks and potentially death. Create a meaningful vision around why data quality is important, communicate this and establish it as part of the business culture or working principles. Implement a cost-effective quality improvement framework on needs.

When facing conflicting data quality standards, effectively aligning IT and business stakeholders involves clear communication about the conflicts and their impact on the business. Consider presenting the issue through infographics, PowerPoint presentations, or simulation videos that highlight where the conflicts lie and the proposed solutions. Explain why resolving these conflicts is crucial for the business. Ensure all stakeholders understand the importance of unified data governance and consistent data quality standards. You can achieve alignment and drive better data management practices by fostering a shared understanding and commitment.

Consider the following as principles for quality management excellence: 1. Shared Responsibility: Quality is everyone’s duty. Regardless of role or position, every employee contributes to maintaining high standards. A culture of quality requires active participation from all. 2. Prevention beats Inspection: Prioritise prevention over inspections. Identify potential issues early and invest in robust processes to avoid defects. Prevention saves time, and resources, and ensures better outcomes. 3. Minor errors can become big issues, so vigilance is crucial. 4. Continuously improve and enhance over time with incremental improvements. 5. Holistically improve quality everywhere. 6. Cost-effective improvements - evaluate costs, risks and benefits.

A data governance framework <> data quality framework. You want a data quality framework to resolve quality standards. Many data quality frameworks and standards are available to improve data quality. The key is to adopt and customise it for your organisation. Consider: * ISO 8000 - Global standard for Data Quality and Enterprise Master Data * ISO 9001 - Quality Management Systems - Requirements * Data Quality Assessment Framework - how to assess data against dimensional data properties * Total Data Quality Management - define, measure and improve data quality. * Data Quality Scorecard - measure quality based on predefined indicators and thresholds * Data Quality Maturity Model - assess and improve data quality capabilities.

When facing conflicting data quality standards, Alex knew alignment between IT and business stakeholders was crucial. * Hosting a collaborative workshop, Alex encouraged open dialogue about each group's priorities and concerns. * By demonstrating how high-quality data impacts both operational efficiency and strategic decision-making, Alex built a shared understanding. * Using visualization tools like Tableau to highlight discrepancies and potential gains, Alex facilitated consensus on unified standards, ensuring both IT and business teams worked towards common goals.