Data Management Maturity (DMM) Model from CMMI Institute

The Data Management Maturity (DMM) Model was unveiled in May 2014 and released in August 2014.

This is a model that was developed by and is maintained by the CMMI Institute.

The CMMI Institute is well know for its CMMI family of models (CMMI for Development, Services and Acquisition) .

DMM model is intended to help business organizations improve their data management practices across the board.

Data management is an area that needs high level of attention by any organization as the organization's strategy and direction is increasingly becoming dependent on data.

The major challenge there is the sheer amount of data that is getting generated on a daily basis, the world over.

This also leads the way towards what can be called as digital junk.

Digital junk is all the useless data that is getting generated along with the useful ones.

In addition to the sheer volume, determining what data is useful and then making it available in the right processed format to the right user at the right time is very crucial for correct decision making by the executives involved with a business problem in an organization.

Data generation is not only internal within the organization but also outside as well.

Lot of outside data is also useful when making a decision.

Big data, data analytics, data warehousing and mining, business intelligence are some of the terms that indicate towards the need of a framework for data management.

The model contains specific process areas grouped by categories.

It can be implemented and assessed either across the entire organization or a selected business unit based on customer, geography or product/service.

The DMM Model has the following major categories:

• Data Management Strategy
• Data Governance
• Data Quality
• Data Operations
• Platform & Architecture
• Supporting Processes

Data quality is the bedrock of any management system to derive the right set of actions.

So deep focus on data quality is of paramount importance.

Data quality category has the following process areas:

• Data Quality Strategy
• Data Profiling
• Data Quality Assessment
• Data Cleansing 

Process areas under Data Management Strategy category:

• Data Management Strategy
• Communications
• Data Management Function
• Business Case
• Funding

Process areas under Data Governance category:

• Governance Management
• Business Glossary
• Metadata Management

Process areas under Data Operations category:

• Data Requirements Definition
• Data Life-cycle Management
• Provider Management

Process areas under Platform & Architecture category:

• Architectural Approach
• Architectural Standards
• Data Management Platform
• Data Integration
• Historical Data, Archiving and Retention

Process areas under Supporting Processes category:

• Measurement and Analysis
• Process Management
• Process Quality Assurance
• Risk Management
• Configuration Management