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Data Quality Fundamentals

Understand key concepts, principles and terminology related to Data Quality

Created by Inf Sid, Last Updated 22-Oct-2019, Language:English

Description

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 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 in data management. 

Requirements

  • Basic understanding of Enterprise Data Management
  • Basic understanding of Data Warehouse Concepts

What Will I Get ?

  • Determine data quality requirements by studying business functions, gathering information, evaluating output requirements and formats.
  • Profile select data sets to ensure quality and develop the data visualizations necessary to both manage and communicate data quality.
  • Coordinate business efforts to deliver data that is fit for use for use in critical processes, analysis and reports.
  • Collaborate with business application team to document information architecture requirements as needed
  • Serve as a subject matter expert and perform data quality related functions for urgent, high visibility, high profile, and strategic projects while meeting challenging deadlines.

Course Content

Inf Sid

Data Management Consultant

Business Intelligence Consultant and Trainer with 16+ 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 Powercenter, SSIS, ODI and IDQ, Data Virtualization, DVO, MDM.

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