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Data Management Capability Assessment Model (DCAM)

The industry standard framework for Data Management


“The better we get at getting better, the faster we will get better.”

  • DCAM is the synthesis of research and analysis of data management practitioners across the industry.

  • DCAM was developed following the Socratic method of question and debate, to ensure the model is practical and in line with core principles of data management.

  • DCAM is designed to be applicable and easily understood by non-specialists, while structured for continued improvement across an entire organization.


1. Foundation Layer

Core components of your data management program – establishing the data management strategy; aligning the key business case; building out the elements of the program; and ensuring sustainable funding

3. Collaboration Layer

A successful data
management program will
enable what is referred to as a “data control environment”.  A data control  environment
demonstrates governance over the entire data management lifecycle –
from acquisition of data thru to consumption. And ensures collaboration with all the existing control functions across the organization

2. Execution Layer

These represent the delivery capabilities that anchor your data management program…Data architecture;
technology; data quality and data governance

4. Analytics Management

Analytics Management
structures, prioritizes, enables and governs the analytics activities of the organization in alignment with the Data Management capabilities

Future Glitch

Our Focus

Data Delvery

Describes what we need to do in order to be effective, responsible and legally compliant with our data

  • Rules, responsibilities, policies, processes and procedures

  • By itself it creates the guiderails, but this does not directly translate into business value or improved data integrity

Doing things to get data into people’s hands

  • Sourcing datasets, integrating systems, aggregating data, developing reports, dashboards and models

  • By itself it can be ad-hoc – this leads to inefficiency, inaccuracy, duplication of effort and multiple versions of the truth.

Delivering data and actionable information in a robust and sustainable manner

  • To be effective Data Governance & Data Delivery need to be aligned

  • Governance brings control and sustainability, but delivery brings the value

Data Governance

Data Management

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