Background (All organisations rely on information created from data stored in computer systems, to run operations and to make decisions. If this data is incomplete, invalid, inconsistent, non -standard, misinterpreted and/or not compliant with business rules, then you have a Data Quality problem. When unreliable data is used by business applications, you then have an Information Quality problem. Unless properly addressed, hidden Data and Information Quality issues will lead to untrustworth information and are major contributors to inefficient delivery, unnecessary risks and non-productive costs).
Overview (What is Data Quality and how can it be measured?; Difference between Data Quality Management and Information Quality Management; Data Quality Management Methodology and Techniques; The Need for Corrective Improvement (to improve the quality of existing data); The Need for Preventative Improvement (to remove the root cause of the problem); Implementing a Consistent, Repeatable and Reliable Process).
Module 01 Data Quality Management in Context – Introduction (Gain a common understanding of the terms relating to Data and Information Quality such as what is data, what is information, what is Data Quality, what is Information Quality?; Understanding the risks of poor Data Quality and the benefits of good Data Quality; How do we know when we have Quality Data?; Lay the foundations for the rest of this course).
Module 02 Where Do We Start? (Assessment) (Understand how to approach Data Quality initiatives by enabling you to get started using a tried and tested approach which includes i) scoping your DQ initiative; ii) analysing and interpreting the data in scope to identify DQ issues and potential issues; iii) interpreting and documenting the analysis results; and iv) presenting your results).
Module 03 What Do They Want? (Requirements) (Understand how to use what you have in terms of the assessment and determine where the organisation wants to be in terms of the data, by understanding and defining i) what action must be taken to address problems identified; ii) the risks of poor data quality; iii) the benefits of good data quality; iv) the measure of data quality required and why (in terms of targets and metrics); and v) the rules to which the data should comply).
Module 04 How Do We Measure Up? (Measurement) (How we approach determining the state of the data in terms of the defined business rules, the data quality characteristics and measuring the data).
Module 05 How Do We Fix It ? (Correction) (Understand how to approach cleansing the data, including i) What process do we follow to correct the data?; ii) Who is involved and how, in the data correction process?; iii) What are the various methods for correcting data such as parsing, standardisation or enrichment; iv) What are the risks of ‘just’ cleansing the data?).
Module 06 What’s the Real Problem ? (Prevention) (Understand why Data Quality problems re-occur and how to prevent them re-occurring by i) Understanding what Root Cause Analysis (RCA) is; ii) Understanding how to determine the causes and root causes; and iii) Identifying what to put in place to address the root causes).
Module 07 Will It Happen Again? (Prevention) (Understand that this is merely the tip of the iceberg. What else do you have to put in place? These include Establishing Data Quality Awareness; Data Governance; Data Development; Document and Content Management; and Reference and Master Data Management).