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CIMP certification is currently offered in seven tracks:

  • Information Management Foundations (IMF)
  • Data Quality (DQ)
  • Data Governance (DG)
  • Master Data Management (MDM)
  • Data Modeling & Metadata Management (META)
  • Business Intelligence (BI)
  • Data Integration (DI)
  • Data Warehousing (DW)

The table below maps our course catalog to the curricula for CIMP tracks. Each curriculum includes the fundamentals course (marked with letter 'F') and several core courses (marked with letter 'C').

To meet CIMP academic requirements in a track, you must complete five courses from its curriculum, including the fundamentals course and at least two core courses. For META track , one of the completed core courses must be a data modeling course (C1) and one must be a metadata management course (C2). For IMF track, you must complete the fundamentals course plus at least one course from each core area: data management (C1), data integration (C2), and information analytics (C3).



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To meet CIMP Ex academic requirements in a track, you must complete seven courses from its curriculum, including the fundamentals course and all core courses, and one elective course that can be chosen from the entire course list. For the IM Foundations track specifically, you must take the fundamentals course, plus at least two courses from each core area: data management, data integration, and information analytics.

See CIMP Rulebook for complete CIMP and CIMP Ex requirements beyond the coursework. To enroll in the program, choose one of our many CIMP Packages.

Courses (in alphabetical order) CIMP Track
IMF DQ DG MDM META BI DI DW
Assessing Business Intelligence Operations







Best Practices in Data Resource Management







Beyond Business Intelligence







BI Program and Project Management




C

BI Requirements Gathering and Management




C

Big Data Fundamentals C3






Building and Operating a Data Warehouse







C
Business Analytics Practices and Applications







Conceptual Data Modeling







Crafting the Business Case for Data Quality







Creating and Implementing a Data Strategy







Data Governance for Business Leaders







Data Governance Fundamentals or Data Governance for Data Stewards C1
F C



Data Integration Techniques for Designing an ODS







Data Integration Fundamentals and Best Practices C2




F C
Data Mining Concepts & Techniques







Data Mining in R







Data Parsing, Matching & De-duplication


C



Data Profiling
C

C2


Data Quality Assessment
C

C2


Data Quality Fundamentals or Data Quality for Data Stewards C1 F C



C
Data Stewardship Fundamentals or Data Stewardship Core

C




Data Virtualization


C

C
Data Warehousing Fundamentals C2




C F
DW and BI Data Modeling



C1

C
Ensuring Data Quality in Data Integration
C



C
Fundamentals of Business Intelligence C3



F

Fundamentals of Data Modeling and Metadata Management or
Metadata Management for Data Stewards
C1


F


Fundamentals of Predictive Analytics C3



C

How to Deploy and Sustain Data Governance

C




Information Management Fundamentals F
C




Introduction to NoSQL







Location Intelligence & GIS







Logical Data Modeling



C1


MDM Architecture and Implementation


C

C
MDM Fundamentals and Best Practice or MDM for Data Stewards C2

F



MDM: Selecting a Vendor







Organizing for Data Quality







Prescriptive Analytics Using Simulation Models







Root Cause Analysis
C





Statistical Process Control







The Data Model Scorecard







Understanding and Evaluating the BI Platform




C

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