MDG Data Excellence: Cleansing, Quality, Duplication, with SAP Examples
What you will learn
Basics of data cleansing and its necessity
Identifying and resolving data duplicates
Data quality metrics and assessment techniques
SAP MDG principles and overview, applicable to any system
Spotting data errors in SAP environments, applicable to any environment
Techniques to correct data in SAP and any other environment
Utilizing Excel for data cleansing tasks, starting from an SAP export or other system data export
Best practices for data quality improvement
Maintaining ongoing data cleanliness
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- Grasp the strategic imperative of pristine data, empowering superior decision-making, operational excellence, and a significant competitive edge for your organization.
- Systematically identify and mitigate the hidden costs, pervasive risks, and lost opportunities stemming from compromised data quality across business functions.
- Implement sophisticated preventative data quality strategies, designing robust ingestion processes and source controls to proactively avert data errors.
- Develop a truly platform-agnostic data quality expertise, mastering universal principles and adaptable methodologies applicable to any enterprise system or data landscape.
- Conduct deep root cause analysis of data inconsistencies, moving beyond superficial fixes to resolve underlying systemic flaws for sustainable data integrity.
- Integrate advanced data cleansing seamlessly within a robust data governance framework, fostering clear stewardship, policies, and ownership for high-quality data assets.
- Understand the critical link between impeccable data and the reliability of advanced analytics, machine learning, and AI initiatives, ensuring trustworthy insights.
- Master the art of effectively communicating data quality challenges and value to diverse stakeholders, securing organizational buy-in for data excellence.
- Navigate complex regulatory compliance (e.g., GDPR, CCPA) by understanding how meticulous data cleansing underpins legal and ethical data mandates.
- Quantify the tangible Return on Investment (ROI) of data quality improvements, building compelling business cases that demonstrate direct financial and operational benefits.
- Cultivate a proactive data stewardship culture, instilling continuous data vigilance and accountability across the enterprise to prevent future degradation.
- Utilize advanced data profiling techniques to thoroughly scrutinize datasets, uncovering subtle anomalies, hidden patterns, and intricate relationships impacting trustworthiness.
- Engineer scalable data cleansing architectures capable of managing exponential data growth and increasing complexity, ensuring future-proof data quality solutions.
- Address the inherent challenges of consolidating and harmonizing data from disparate legacy systems and external sources into a unified, pristine enterprise view.
- Optimize system performance, accelerate processing times, and enhance reliability across all critical business applications through superior data quality.
- PROS:
- Provides a holistic perspective on data cleansing, balancing technical execution with strategic business impact and ROI justification.
- Equips you with versatile, platform-agnostic skills applicable to any data environment, significantly boosting your career adaptability and market value.
- Fosters a deep understanding of root causes, moving beyond superficial fixes to sustainable data health strategies.
- Emphasizes the critical role of data quality in driving advanced analytics, regulatory compliance, and overall organizational efficiency.
- CONS:
- Requires a significant commitment to practice and apply learned techniques, as true data mastery is iterative and context-dependent.
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