Data Management

In the quest for successful data exploitation, organisations look at how they can access information through dashboards and scorecards, or interactive analysis and reporting. This approach tends to overlook the increasing role of data management in delivering valuable, reliable, and relevant information to the business. The “plumbing” aspects of data management may not be as sexy as the predictive models and colourful dashboards they produce, but they’re vital to high performance. This service assesses your data management maturity across the nine domains identified in DMBOK using a CMMI framework

  • Data Governance
  • Data Development
  • Database Operations Management
  • Data Security Management
  • Reference & Master Data Management
  • Data Warehousing & Business Intelligence Management
  • Document & Content Management
  • Metadata Management
  • Data Quality Management
  • Data Architecture Management


Maximize the value of organisational information

  • Efficient information management relies on security, findability, usability, and interpretation and will empower business decision making, support and enhance business operations, and be flexible enough to change alongside evolving business requirements.

Ensuring regulatory and legislative compliance

  • Business information is subject to regulations and legislation governing how it should be collected, managed, used, and disposed of. The obligation of maintaining records is a requirement and non-compliance poses a significant risk to organisations that needs to be addressed to avoid legal, financial and PR ramifications.

Process Steps:

Step 1.

Gather information

  • A key input is an enterprise data model describing the key data elements of the business domain and the relationships between them. The data model may include data dictionaries, thesauri, taxonomies, and topic maps
  • An inventory of data stores from the infrastructure assessment
  • Existing data management policies and procedures for data/information quality; and analytics

Step 2.

Perform maturity assessment

  • Collaborate with data owners, stewards and users via workshops and questionnaires

Step 3.

Identify opportunities for improvement


Data Management Maturity Assessment