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Representative POMs are described for Information Quality at each level of maturity.

1Initial

• Practices

  • Use application or solutions imposed quality on a case by case basis.
  • React to Data and Information Quality issues on a case by case basis (if at all).
  • Execute remediation actions as stand-alone projects.

  Outcomes

  • Information quality is mostly unknown.
  • The organisation is typically not aware of the issues it is encountering due to data and information quality issues.

  Metrics

  • # of systems with 'default' or no data validation settings deployed.
  • # of controls in place to detect poor quality data or information.
  • # of “fire fighting” projects initiated at any time.

2Basic

• Practices

  • Identify data and information management stakeholders.
  • Develop and use data and information management processes.
  • Ensure operations is sensitive to data and information quality issues and responds quickly.
  • Design data and information validation and referential checks to be as close as possible to the point of entry.
  • React to data and information quality issues on a case by case basis.
  • Begin documenting some systems and processes.

  Outcomes

  • Information quality is mostly known.
  • Ownership of data and information issues is emerging.
  • The use of processes is having a positive impact and a realisation that the ability of IT to clean data is limited and better solutions will be required.
  • Organisation begins to develop end-to-end view of critical information chains.
  • Organisation begins to link business functions to information chain outcomes.

  Metrics

  • # of formal data or information stewards actively assigned to information chains.
  • # of controls in place to detect poor quality data.
  • # Data entry errors.
  • # Number of processes mapped.
  • # Number of stakeholders identified.

3Intermediate

• Practices

  • Follow information life cycle management.
  • Identify, measure, and manage key quality attributes of data and information.
  • Document systems, processes and information chains (possibly in a centralised repository).
  • Identify key ‘critical to quality’ characteristics of information and implement measurement processes.
  • Start data and information stewardship, with appropriate training.
  • Adopt structured and repeatable approaches to diagnosis and remediation of data and information quality problems.

  Outcomes

  • The organisation is switching from a reactive mode to a pro-active managed approach to data and information management and quality.
  • Organisation has an improved end-to-end view of process and information flow dependencies.
  • Key quality considerations are identified at planning stage of new processes/systems.
  • Understanding of quality of data improves; ability to make reliable decisions/reports increases.
  • Organisation begins to improve quality in a structured way and learn from common causes of error.

  Metrics

  • Growing # of systems requirements, policy, and process documents referring to information life cycle.
  • % of critical processes & information chains mapped and documented.
  • Number of critical quality characteristics defined, documented, and measured.
  • # of data stewards identified and trained.
  • Average time to remedy defect/
  • % reoccurrence of defects with similar causes.

4Advanced

• Practices

  • Strategically manage data and information quality and the cost of quality and associated trade-offs.
  • Adopt formal role profiles for data and information stewards.
  • Adopt formal accountabilities and responsibilities for quality of information at key points in information chains.
  • Define data and information quality key process indicators (KPIs) in context of strategic level KPIs and goals.
  • Adopt formal training in data and information quality principles and practices as part of job roles.

  Outcomes

  • Data and information quality issues are minimised and associated risks lie within the risk profile for the business.
  • Organisation makes formal commitment to stewardship.
  • Organisation can assign responsibility for remedial actions and process changes more effectively.
  • Organisation has clear line of sight of contribution of quality data and information to achieving organisation goals.
  • Management and staff understand how to influence data and information quality KPIs and address quality failures which lead to KPI variances.

  Metrics

  • # of data or information quality metrics that are translated into risk statements and probabilities.
  • % staff filling formal data steward roles.
  • % role profiles with clear data quality responsibilities/accountabilities.
  • % of Strategic KPIs that have a data quality KPI equivalent.
  • % Management team with formal training in data and information quality.

5Optimized

• Practices

  • Information quality is continuously monitored.
  • Design data and information quality into systems and solutions.
  • Foster a data and information quality culture.
  • Implement information quality scorecard as part of standard management reporting.

  Outcomes

  • The culture and processes to continuously improve data and information management exist.
  • Data and information quality issues are rare and the organisation understands why its data and information are of a high quality and understands the cost benefit ratios of that quality.
  • All management and staff are aware of current quality levels and issues and steps being take to remedy problems.
  • Risk of non-quality outcomes are reduced as staff act with constant awareness to quality issues and outcomes.

  Metrics

  • # Formal information quality checkpoints in process and systems design and softare development lifecycles.
  • % of projects being run using a data and information quality framework.
  • % of critical to quality processes being reported and tracked.
  • # Frequency of management communication about information quality (formal and informal).