Data Governance

Data governance is widely understood to include all processes that are geared toward enhancing, protecting and managing data as a strategic enterprise asset. In the current environment of exploding data, it is both a critical need and a strategic opportunity for today’s organizations.

In many ways, data governance represents a true confluence of business and IT. If properly executed, it offers significant value to the organization, based on:

  • Information that is defined and used consistently across the enterprise.
  • Increased consumer confidence in data, thereby increasing the use of this data.
  • Reduced time-to-market for new data initiatives.
  • A framework to inform decision-making as it relates to data management initiatives.

Establishing a governance program

The three key areas of a data governance program consist of information lifecycle management, data quality and information security. However, none of these areas can be successful without the enabling value of appropriate data stewardship and supporting organizational structure and processes that are championed by the business. IT enablers include master (or reference) data management, metadata management, enterprise data architectures, and data security protocols.

Daman can assist you in developing a governance program that incrementally addresses the technology, process and organizational issues that are critical for success. Our first step is to assess your existing situation in the context of our Governance Maturity Model. We can then establish your target or desired maturity level. An incremental plan is developed to allow for pragmatic investments, often aligned with prioritized projects to attain the target state.

Some tangible aspects of this plan include:

An organizational roadmap, providing role definitions, executive sponsorship, identification of the data steward with the business decision-making authority, governance PMO, data quality competence centers and operational models are all part of this domain.

A process roadmap, defining governance policies and procedures, data quality processes with metrics for measurement, and business definitions that leverage industry standards as appropriate. The process roadmap also includes enterprise business architecture practices and blueprints, as well as processes for architectural review at the project level.

A technology roadmap, listing criteria for functionalities and the selection of data profiling tools used to assess data quality at a baseline and on-going basis. In addition, the technology roadmap covers data quality tools, master data management solutions, metadata repositories, industry models and taxonomies.