Data Management Fundamentals and DAMA Certification Preparation
This 3-day course addresses all the Information Management disciplines as defined in the DAMA body of knowledge (DMBoK) & introduces the “new” discipline of “Data Integration” introduced in DMBoK 2.0. Taught by an industry recognized DAMA DMBoK(2.0) author and CDMP(Fellow) this course provides a solid foundation across all of the disciplines across the complete Information Management spectrum. By attending the course, delegates will get a firm grounding of the core Information Management concepts and illustrate their practical application with real examples of how Information Architecture is applied.
Additionally this course provides a solid foundation for students wishing to consider proceeding to take the DAMA CDMP Industry professional certification. This course is designed and taught by the author of “Data Modelling for the Business” an industry recognized DAMA DMBoK(2.0) author, DAMA CDMP (Fellow), VP Professional Development for DAMA International, past President of DAMA UK, author & examiner for the professional CDMP certification and recipient of the DAMA Lifetime Achievement Award for Data Management Excellence.
DAMA:n kansainvälinen CDMP sertifiointi on data-ammattilaisen vahvin osoitus osaamisestaan. Se ei testaa vain tietyn teknologian tai järjestelmän osaamista, vaan sitä, ymmärtääkö datan parissa työskentelemisen perusperiaatteet ja käytännöt. Se varmistaa, että tietoa osataan soveltaa käytäntöön.
CDMP sertifioitu ammattilainen on varmistanut osaamisensa ja voi todisuksellaan vakuuttaa myös muut.
Sertifioinnin testaamat taidot hyödyntävät kaikkia datan parissa työskenteleviä riippumatta siitä onko se tietokantojen rakentamista, datan analysointia, master dataa tai vaikka dokumenttien hallintaa.
Sertifointi ei edellytä tämän kurssin suorittamista, mutta mikäli haluat vahvistaa teoriapohjaasi ennen kokeen suorittamista, Chris Bradleyn kurssi on hyvä keino siihen.
Taru Väre, CDMP, DAMA Finland hallituksen puheenjohtaja
Mikäli kurssin jälkeen olet kiinnostunut suorittamaan CDMP sertifioinnin vaatimat kokeet, DAMA Finland ry järjestää koetilaisuuksia tarpeen mukaan. Kokeen voi suorittaa myös verkkopalvelun kautta. Lisätietoja antaa Taru Väre, firstname.lastname@example.org.
Gain familiarity with the CDMP examination format, types of questions and the most appropriate way of answering them. Understand and revise the major syllabus points. Practice taking the examinations to pass the CDMP examinations and gain recognition for your professional experience.
There are 4 levels of accomplishment for the CDMP certification, Associate, Practitioner and Master, and Fellow.
- Recommended 2 years relevant Data Professional work experience
- 1 examination (DM Fundamentals). Pass mark is 60%
- Recommended 3-5 years relevant Data Professional work experience
- 3 examinations (DM Fundamentals + 2 electives).
- Pass mark for all 3 exams 70%
- Required 10+ years relevant Data Professional work experience
- 3 examinations (DM Fundamentals + 2 electives).
- Pass mark for all 3 exams 80%
- Written case study reviewed by CDMP Fellow
- By appointment
- Completion of all previous levels,
- Recognised exceptional works in the Data Management field
- 25+ years’ experience
The 2 elective exams must be chosen from the following set:
- Data Governance and Stewardship
- Data & Information Quality
- Data Modelling
- Data Warehousing & Business Intelligence
- Data Integration & Interoperability
CDMP Overview: What is the CDMP, what are the levels and how can you progress from one level to the next.
Introduction to the DMBoK: What is the DMBoK, its intended purpose and audience of the DMBoK. Changes in DMBoK 2.0, relationship of the DMBoK with other frameworks (TOGAF / COBIT etc.). DAMA CDMP professional certification overview & CDMP exam coverage by DMBoK section.
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. How to get started with Data Governance.
Data Quality Management: The Dimensions of Data Quality, policies, procedures, metrics, technology and resources for ensuring Data Quality is measured and ultimately continually improved. DQ reference model. Capabilities & functionality of tools to support Data Quality management.
Master & Reference Data Management: Differences between Reference & Master Data. 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. Types of BI, DW and Analytics.
Data Modelling: Types & levels of data models. How to develop data models. Use and exploitation of data models, ranging from Enterprise, through Conceptual to Logical, Physical and Dimensional. Maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the Software Development Life Cycle (SDLC).
Metadata Management: Types and uses of metadata. Sources of metadata. Metadata standards & guidelines. Provision of metadata repositories and the means of providing business user access and glossaries from these.
Data Architecture Management: Approaches, plans, considerations and guidelines for provision of Data Integration and access. Consideration of P2P, ETL, CDC, Hub & Spoke, Service-orientated Architecture (SOA), Data Virtualization and assessment of their suitability for the particular use cases.
Data Lifecycle Management: Proactive planning for the management of Data across its entire lifecycle from inception through, acquisition, provisioning, exploitation eventually to destruction. This IM discipline 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. Exploration of threat categories, defence mechanisms & approaches, and implications of security & privacy breaches.
Regulatory Compliance: The polices and assurance processes that the enterprise is required to meet. Adapting to the changing legal and regulatory requirements related to information and data. Assessing the approach to regulatory compliance & understanding the sanctions of non-compliance.
Data Risk Management: Identification of risks (not just security) to data and its use, together with risk mitigation, controls and reporting.
Data Integration & Interoperability: Different approaches for data integration. Modes of data interoperability. Challenges, advantages & risks of different Data Integration & Interoperability approaches..
Participants can decide which level of examination most adequately meets their need & sit the appropriate sessions.
Practitioners who seek Professional recognition and certification for Information Management including:
- Business Intelligence & Data Warehouse developers & architects
- Data Modellers
- Data Architects
- Data Analysts
- Enterprise Architects
- Solution Architects
- Application Architects
- Information Architects
- Business Analysts
- Database Administrators
- Project / Programme Managers
- IT Consultants
- Data Governance Managers
- Data Quality Managers
- Information Quality Practitioners