The fundamentals of Information Management covering the Information Functions and disciplines as outlined in the DAMA DMBoK . This course provides an overview of all of the Information Management disciplines and is also a useful start point for candidates preparing to take DAMA CDMP professional certification. Taught by CDMP(Master) examiner and author of components of the DMBoK 2.0 chris.bradley@dmadvisors.co.uk

1. Information Management Fundamentals DMBoK
Course Objectives: T o g i v e p a r t i c i p a n t s
a s o l i d g r o u n d i n g i n a l l o f t h e c o r e
I n f o r m a t i o n M a n a g e m e n t c o n c e p t s .
A d d i t i o n a l l y i t p r o v i d e s a f o u n d a t i o n
f o r s t u d e n t s c o n s i d e r i n g D A M A C D M P
p r o f e s s i o n a l c e r t i f i c a t i o n
C o u r s e D e s c r i p t i o n : T h i s i s a
5 d a y c o u r s e c o v e r i n g a l l o f t h e
I n f o r m a t i o n M a n a g e m e n t
d i s c i p l i n e s a s d e f i n e d i n t h e D A M A
b o d y o f k n o w l e d g e ( D M B o K ) .
T a u g h t b y C D M P ( M a s t e r ) e x a m i n e r
a n d a u t h o r o f c o m p o n e n t s o f
D M B o K 2 . 0
Course Content:
Introduction to the DMBoK: What is the DMBoK, purpose and audience of the DMBoK, changes due in DMBoK 2.0, relationship
of DMBoK with other frameworks (TOGAF / COBIT etc.).
Data Governance: Why Data Governance is at the heart of successful IM, a typical DG reference model, DG roles & responsibilities, the role of
the DGO & its relationship with the PMO.
Data Quality Management: Dimensions of data quality, policies, procedures, metrics, technology and resources for ensuring Data Quality is
measured and ultimately continually improved.
Master & Reference Data Management: The identification and management of Master Data across the enterprise. 4 generic MDM
architectures & their suitability in different cases. MDM maturity assessment to consider business procedures for MDM and the provision and
appropriateness of MDM solutions per major data subject area. How to incrementally implement MDM to align with business priorities.
Data Warehousing & BI Management: Provision of Business Intelligence (BI) to the enterprise and the manner in which data consumed by BI
solutions and the resulting reports are managed. Particularly important if the data is replicated into a Data Warehouse.
Metadata & Models: Provision of metadata repositories and the means of providing business user access and glossaries from these. The
development, use and exploitation of data models, ranging from Enterprise, through Conceptual to Logical and Physical. Maturity assessment
considers the way in which models are utilized in the enterprise and their integration in the Software Development Life Cycle (SDLC).
Data Architecture Management: Approaches, plans, considerations and guidelines for provision of data integration and access. Consideration
of Hub & Spoke, Service-orientated Architecture (SOA), Data Virtualization and assessment of their suitability for the particular use cases.
Data Lifecycle Management: Data is created, read, updated, and archived or deleted. This management function and its maturity assessment
determine how well this is planned for and accomplished.
Data Security & Privacy: Identification of threats and the adoption of defences to prevent unauthorized access, use or loss of data and
particularly abuse of personal data.
Regulatory Compliance: Polices and assurance processes that the enterprise is meeting and adapting to changing legal and regulatory
requirements related to its information and data.
Data Risk Management: Identification of risks (not just security) to data and its use, mitigation, controls and reporting.
Data Management Tools & Repository: Categories & examples of tools, how to select the appropriate toolset, example policy for use of
specific technology to ensure consistency and interoperability across the enterprise.