Date of Award

5-7-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Engineering Technology

First Advisor

John Talburt

Abstract

Conducting a Data Management Maturity Model (DM3) assessment is a key piece of strategic activity that can enable proper guidance and measurements of an organization’s ability to meet data demand. In this Design Science Research project, a Canonical Data Model (CDM) was constructed to analyze and translate DM3 assessments scores between three models, the European Archival Records and Knowledge Preservation (E-ARK)’s E-Ark Maturity Model for Information Governance (A2MIGO), the Enterprise Data Management Council (EDM Council)’s Data Capability Assessment Model (DCAM), and the Method for Integrated Knowledge Environment (MIKE2.0)’s Information Maturity QuickScan (IQMS). These three models were tied together using the Data Management International (DAMA’s) Data Management Body of Knowledge (DMBOK) as the CDM hub. This research was conducted in many phases including: alignment of DM3 and CDM to a defined 3-tiered structure, manual mapping of dimensions to the hub, a Natural Language Processing (NLP) mapping exercise to help align the mappings with machine learning algorithm output, testing of the canonical model through the survey of experienced DM3 practitioners, and a results analysis that compares the output of the engine to the actual scores form the sampled population. This research posits that it demonstrated proof of concept and value in creating a single logical model for rationalization that could support such a CDM, and executed successfully in the mapping alignment as well as sample runs of the engine. It is the hope of the researcher that the artifacts produced will continue to grow as more DM3s are added and the model is iteratively developed over time.

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