EIEIOTECHNOVATION
← Training programmes

EIEIO / DATA SCIENCE

Data Quality

Course outline

1 day

Duration

1 day

What you'll learn

  • 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.

Introduction

  • What is Data Quality?
  • Example of Data Quality
  • Can we achieve 100 % Data Quality?
  • What can be done to achieve 100% Data Quality?
  • How can we measure Data Quality?

Data Quality Dimensions

  • What are Data Quality Dimensions?
  • Consistency Data Quality Dimension
  • Completeness Data Quality Dimension
  • Timeliness Data Quality Dimension
  • Uniqueness Data Quality Dimension
  • Validity Data Quality Dimension
  • Accuracy Data Quality Dimension
  • Example of Data Quality Dimension

Data Quality Vs Data Governance

Data Life Cycle

  • Introduction to the End to End Data Life Cycle with a case study
  • Data Maintenance
  • Data Derivation
  • Data Usage
  • Data Publication
  • Data Archival
  • Data Purging

Data Quality Life Cycle

Data Profiling

  • What is Data Profiling?
  • Commonly used data types during Data Profiling
  • Data Profiling Vs Data Mining
  • What are the different types of Data Profiling?

Business Expectations and Impacts of Low Data Quality

  • Business Expectations on Data Quality
  • Impacts and Costs of Low Data Quality
  • How to correct the existing errors in the Data Warehouse?
  • How does the Enhance, Transform and Calculate phase or the ETL phase help?
  • Data Standardization
  • Complete and Corrected Data
  • Match and Consolidate the Data

Data Quality Roles

  • Different Data Quality Roles in an Enterprise

Programme scope, delivery arrangements and suitability are confirmed during enquiry.