Robert Hawker discusses practical data quality in a strategic and value-driven approach, highlighting its importance for data leaders in overcoming challenges. The conversation covers aligning data strategy with MDM initiatives, justifying investments in data quality, and leveraging AI to streamline rule codification.
Data quality is crucial for AI and digital transformation, with a focus on tangible business benefits.
Initiating small data quality initiatives can lead to quick wins and build credibility within organizations.
Deep dives
Practical Data Quality: Real-Life Examples and Business Impact
Practical data quality is crucial for organizations to truly understand their data. Robert Hawker, in his book on practical data quality, emphasizes the importance of incorporating real-life examples to make concepts tangible and applicable for data leaders. By providing templates and reports from past experiences, the book aims to make data quality concepts practical and actionable. The focus is on delivering tangible results that can be linked to business success.
Challenges and Focus Areas for Chief Data Officers
Chief Data Officers (CDOs) face challenges in articulating compelling business cases for data quality initiatives. Unlike projects with clear financial outcomes, data quality assessments require upfront investments without immediate visibility into benefits. CDOs must align data quality efforts with business strategies to ensure that resources are directed towards fixing data issues that contribute to organizational goals. It is essential to emphasize outcomes and concentrate on practical approaches to maintain long-term data quality improvements.
Sustainability and Continuous Monitoring in Data Quality Programs
Data quality programs should not be treated as one-time projects but as ongoing initiatives that require continuous monitoring and improvement. Simply fixing data issues temporarily through external consultants does not address the root causes of data quality problems. To sustain data quality improvements, organizations must embed data quality practices into their processes and establish a systematic approach for monitoring and addressing data issues over time. Sustainability and consistency are key factors in ensuring long-term data quality effectiveness.
Importance of Starting Small and Demonstrating Quick Wins
For CDOs initiating data quality initiatives, starting with a small, critical area of focus can lead to quick wins and build credibility within the organization. By delivering measurable improvements in key data quality areas, CDOs can showcase the value of their programs and gain support for broader data quality efforts. Celebrating successes and communicating tangible results are essential to sustaining momentum and garnering continued support for data quality initiatives.
In this episode of the CDO Matters Podcast, Robert Hawker (no relation – at least none that we’re aware of!) joins for a discussion on Practical Data Quality – which is all a programmatic approach to data quality that focuses on business benefits, quick wins, and the highest priority challenges.
Data quality issues are a major hindrance to the AI and digital transformation aspirations of most companies, so having a roadmap to resolve those issues is a strategic imperative for all data leaders. Please join Malcolm and Robert as they discuss a practical, value-driven approach to overcoming the perils of low-quality data.