Governance Achievement Model
MetaGovernance utilizes a four-tier Information Governance Achievement Model to assess the current state of Information Governance within your organization and define the necessary required corrective actions to move your organization, at minimum, to the next level of achievement.
Below is a summary of the characteristics that can be found at the different states of achievement:
Level 1 is referred to as “random acts of data quality”. This level is evidenced by isolated data quality efforts targeting specific problems. Organizations are in a very reactive mode. Efforts are stove-piped and there are no consistent behaviors. The terms Data Governance or Information Governance are either used inconsistently or not at all, and technology is not considered an option to enable problem resolution. Level 1 organizations follow these basic characteristics:
- Organizations in Level 1 typically have pockets of staff informally working on solving data issues.
- There is little awareness that the problem is company-wide.
- Teams or departments use different approaches to react to data issues typically because reports to management, the Board, or external entities are incorrect or inconsistent.
- Data Governance or Information Governance as a discipline is a fuzzy concept.
- Efforts are placed on cleaning specific data. Minimal effort is invested to understand why the data is bad (root cause analysis).
- Efforts to clean data in one area typically conflict with other areas, usually around conflicting business rules that are not articulated or adequately understood.
- Technology is not leveraged to implement automated data internal controls that can be used to replace extensive manual reconciliation efforts.
Level 2 (Awareness)
OIn Level 2, organizational awareness begins to emerge. Organizations are increasingly aware of underlying data problems. They are still reactive, but a groundswell starts building towards a proactive approach. Working groups or committees are formed. The term Data Governance is bantered about, but there is little understanding of what it really means. Level 2 organizations follow these basic characteristics:
- Level 2 organizations begin to sense that the problem is not isolated to one specific area.
- Meetings are held that involve multiple departments to discuss the data cleansing efforts.
- The term “Data Governance” enters the conversations and small working groups are formed to explore the concept and how to apply it to the present issues.
- Actions are still very much reactive, based on the inability to produce accurate, timely reports.
- Organizations will start looking for the “silver bullet” to resolve the data issues.
- Root cause analysis is still not considered. There is little conscious awareness of the link between the data issues and the processes that are creating/corrupting the data.
- Technology is still not considered an aid to regulatory compliance around data issues.
- Organizations begin to realize that specific data ownership needs to be addressed, typically as attempts are made to standardize business rules and resolve the synonyms and homonyms.
Level 3 (Integrated)
In Level 3 a systematic view begins to emerge. Business and technology partner on data issues. There are proactive efforts to identify and resolve data issues. Data Governance groups are officially formed. Level 3 organizations follow these basic characteristics:
- Level 3 brings a holistic view to the problem.
- Root causes of the data issues are uncovered through extensive analysis of the relationships between business process, data creation and usage.
- Business and technology begin to realize they must team together to develop long-term solutions to underlying causes.
- Getting the data to a point of known integrity is not sufficient. Instead organizations begin to change the business processes impacting the data.
- Internal data controls are developed that monitor the “health”, or quality, of the data.
- Data Governance committees are officially formed and become governed by charters written to address the organization’s issues.
- Data quality efforts become proactive.
- Enterprise Risk Management and Internal Audit become aware that Data Governance is the pathway to organizational and regulatory compliance.
- Organizations agree upon, and publish, data quality metrics.
Level 4 (Optimizing)
Level 4 organizations are evolving into learning organizations. There is a significant mind shift around data and information. These organizations have a shared vision on the fundamental need for quality information to conduct business operations. Technology-enabled controls are prevalent across the organization. Level 4 companies follow these basic characteristics:
- Efforts in Level 4 focus on optimizing the Information Governance efforts.
- Organizations realize the difference between raw data assets and useful information that can be utilized for business operations or performance measurement.
- The ramifications around lack of data quality are understood and proactive efforts ensure data meets the current set of business rules.
- Information Governance efforts are used to drive business process re-engineering.
- The relationship between data and metadata are understood, including the linkages back to business processes.
- Lineage reporting and “full disclosure” around data and metadata become the norm in an effort to remove the shroud of mystery that seemingly surrounds an organization’s data assets.
- Technology-enabled internal data controls are the norm.
- The organization learns from previous mistakes in a continual refinement process.
- Interest in Six Sigma leads to a continual quality improvement program.
MetaGovernance utilizes this achievement model as the basis for “Navigating to the Future” of Information Governance within your organization. The simplicity of the model enables organizations to quickly assess the degree to which the concepts of Information Governance have been adopted within either the entire organization, or the division within question. The model moves from isolated, random efforts to a point of organizational awareness. The journey continues with true partnerships developing between departments, including a cohesive effort among technology and the business units. The ultimate desired future state can eventually be reached where stakeholders have evolved into a learning organization that is in a continual state of process and technology refinement. The simplicity of the model eliminates consummate grading and debate over the present position. Tools can be applied when navigating along the model to detect any variance between projected future state and desired future.