Implementing Effective Data Quality Management
Information is at the heart of all organizations, akin to blood flowing through its arteries and veins. However, all too often Information is not professionally managed with the rigour and discipline that it demands. Nonetheless the implications of poorly managed information can be catastrophic, from ICO and other regulatory sanctions ultimately to business collapse. Professor Joe Peppard summed it up when he said “the very existence of an organisation can be threatened by poor data”. This course will provide the rationale why Information Management is critical, and provide methods and practices for addressing key Information Management challenges. This 2-day Data Quality Management course address the key aspects of Data Quality Management & provides practical take away actions that will enable you to start a Data Quality initiative in your organisation. The course draws up the Data Quality discipline as defined in the DAMA body of knowledge (DMBoK). Taught by an industry recognized DAMA DMBoK(2.0) author and CDMP(Master) this course provides a solid foundation and shows the context of Data Quality within the complete Information Management spectrum. DAMA (The Data Management Association) is the World’s leading independent body for Information Management professionals, offering certification, mentoring, and guidance.
What you will learn
- Categories of Data Quality issues from real world case studies and their root causes
- Why does this matter – the drivers for Data Quality and how to link Data Quality to business priorities
- The difference between “Data Quality” and "Data Quality Management” and why it matters
- The relationship between Data Quality Management and other core Information Management disciplines particularly Master Data Management, Data Modelling and Data Governance
- The necessary steps for making this happen through a practical framework
- Who is involved in making Data Quality initiatives work
- The major concepts that are fundamental to Data Quality Management, such as a Framework for Information Quality, information life cycle, Data Quality dimensions, business impact techniques, root cause analysis techniques etc.
- Where software tools and automation can play a part in a Data Quality initiative, and the key functional capabilities expected of Data Quality toolsets
- Making the case for Data Quality • Measuring Data Quality
- Assessing the causes & impact of poor Data Quality
- A framework for improving Data Quality
- Automated support for improving Data Quality
- Fitting Data Quality into an overall Information Management Framework