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Understand key concepts, principles and terminology related to Data Quality
Updated on Sep, 2026
Business, Business Analytics and Intelligence, Data Quality Management
Duration - 2.5 hours
Training 5 or more people ?
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Data quality is not necessarily data that is devoid of errors. Incorrect data is only one part of the data quality equation. Managing data quality is a never-ending process. Even if a company gets all the pieces in place to handle today’s data quality problems, there will be new and different challenges tomorrow. That’s because business processes, customer expectations, source systems, and business rules all change continuously. To ensure high-quality data, companies need to gain a broad commitment to data quality management principles and develop processes and programs that reduce data defects over time.
Much like any other important endeavor, success in data quality depends on having the right people in the right jobs. This course helps you understand key concepts, principles, and terminology related to data quality and other areas of data management.
Check out the detailed breakdown of what’s inside the course
Business Intelligence Consultant and Trainer with 14+ years of extensive work experience on various client engagements. Delivered many large data warehousing projects and trained numerous professionals on business intelligence technologies. Extensively worked on all facets of data warehousing including requirement gathering, gap analysis, database design, data integration, data modeling, enterprise reporting, data analytics, data quality, data visualization, OLAP.
Has worked on broad range of business verticals and hold exceptional expertise on various ETL tools like Informatica Power center, SSIS, ODI and IDQ, Data Virtualization, DVO, MDM.
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