Course Description:

This Data Quality and Governance course is designed to provide students with an understanding of how data is collected, stored, distributed, used and managed. This class also covers the data quality requirements for different data types, business, IT and vendor practices that help create bad data, processes to help identify bad data, and the management practices and data governance needed to help facilitate the distribution of quality information.

This class is a component of the IT Management and Leadership Professional Certification. (ITMLE^©)

Course Objectives:

Upon completion of the Data Quality and Governance course, students will be able to:

• Be well-prepared to define a plan to protect the data of their company
• Define an overall strategy for data quality and governance
• Demonstrate a thorough knowledge of responsibilities for IT, vendors, users
• Lead IT and user executives in development of a DGG 

Audience:

• IT professionals who wish to work toward a formal certification
• IT individual contributors with career goals in supervising/management/senior leadership
• IT team leads or soon-to-be team leads
• IT supervisors or soon-to-be supervisors
• Newer IT managers or soon-to-be managers
• Experienced IT managers desiring a reputation for superior “technical people” management skills and to build their “brand” 

Prerequisites:

• none 

Duration:

  ½ day, full day and customized full-day formats available.

Course Topics:

      I.          Data Quality Requirements

a.    How different types of data require different levels of quality

    II.          Causes of Bad Data

a.    Data problems caused by vendors

b.    Data problems caused by IT and system architectures

   III.          Identifying Bad Data

a.    Ways to monitor and identify bad data

b.    Data heath check philosophy and practices

  IV.          Data Friendly Topologies

a.    Internal and external data friendly topologies

b.    Modifying existing data flows and topologies

   V.          Data Governance and Management

a.    Data governance and Data Governance Group (DGG) definitions

b.    Responsibilities, location, level, and structure

c.    Challenges of justifying a DGG

  VI.          Food For Thought

a.    Various mini-case scenarios that help illustrate cloud computing pains and payoffs