IVI Framework Viewer

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

2Basic

• Practice

  Log, measure, and report data issues.

  Outcome

  There is growing awareness of the cost of data and data infrastructure issues are beginning to get noticed and acted upon.

  Metric

  # of databases or systems where costs are known.

• Practice

  Root cause analyse big impact issues.

  Outcome

  An understanding of the causes of big impact issues emerges.

  Metric

  % of issues where the root cause is analysed and identified.

• Practice

  Use top-down (macroeconomic) evaluations.

  Outcome

  Total loss or cost per minute, hour, or day is known if a system is not available.

  Metric

  # of databases or systems where costs and business value are known in terms of profit or turnover.

3Intermediate

• Practice

  Measure the cost of data, the value of data, and the risks associated with data (execute data risk management processes).

  Outcome

  System loss and system impairment costs are understood.

  Metric

  # of databases or systems where costs are known.

• Practice

  Identify the sources of data quality issues.

  Outcome

  The sources of data quality issues are actively addressed.

  Metric

  % of the sources of data quality issues being addressed by value.

4Advanced

• Practice

  Conduct cost‒benefit analysis on data and information architecture quality.

  Outcome

  Expenditure decisions in data management are data driven.

  Metrics

  • # of data quality-related rework effort and costs.
  • # of estimates of business lost due to poor data quality.
  • % of the sources of data quality issues being addressed by value.

• Practice

  Design solutions based on a defined set of criteria for data risk mitigation.

  Outcome

  Risk mitigation, by use of concepts like self-auditing processes, significantly reduces data ownership costs.

  Metrics

  • # of databases or systems where costs are known.
  • # of activities where activity based accounting has valued data.
  • # of risk management issues mapped to data management.

• Practice

  Use bottom-up accounting (activity based accounting) methods.

  Outcome

  System loss and system impairment costs are understood. Usually, estimates for recovery are known.

  Metrics

  • # of databases or systems where costs are known.
  • # of activities where activity based accounting has valued data.

• Practice

  Map risk management identified 'value-at-risk' costs to the data.

  Outcome

  Business risk values are known.

  Metric

  # of risk management issues mapped to data management.

• Practice

  Measure the cost of data quality and the impact of poor data quality across the organization.

  Outcome

  Data quality improvement initiatives are funded and savings derived are recognized.

  Metric

  # of databases or systems where costs are known.

• Practice

  Factor the time value of information in solutions design and implementation.

  Outcome

  The timeliness of data and reporting is significantly better, in particular the provision of control or decision-making information.

  Metric

  # of databases or systems where timeliness as a factor is costed.

• Practice

  Exploit data and information for value internally.

  Outcome

  The strategic focus of analytics and business intelligence yields higher value from information.

  Metrics

  • % of senior management decision-making meetings in which data and information from the EIM function supports strategic focus.
  • # of business strategy driven analytics and business intelligence objectives.

5Optimized

• Practices

  • Improve processes using both continuous and disruptive methods to mitigate risk and maximize data value and reuse.
  • Reduce risk and facilitate data stewardship with incentives.

  Outcome

  A culture of data stewardship and continuous improvement reduce risk and maximize return on the cost of data.

  Metrics

  • # of databases or systems where costs are known.
  • # of activities where activity based accounting has valued data.
  • % of the sources of data quality issues being addressed by value.

• Practice

  Use multiple accounting and valuation methods to provide a full valuation of data.

  Outcomes

  • System loss and system impairment costs are understood.
  • Usually, estimates for recovery are known.

  Metric

  # of activities where activity based accounting has valued data.

• Practice

  Measure the cost of data quality and the impact of poor data quality across the business ecosystem.

  Outcomes

  • Data quality improvement initiatives are funded and savings derived are recognized.
  • The sources of data quality issues are actively addressed.

  Metrics

  • # of data quality related rework effort and costs.
  • # of estimates of business lost due to poor data quality.

• Practice

  Develop and manage information as a strategic asset to be exploited by the business either internally or externally.

  Outcome

  The focus of analytics and business intelligence is strategically aligned and increases the value of information.

  Metrics

  • # of business strategy driven analytics and business intelligence objectives.
  • % of senior management decision-making meetings in which data and information from the EIM function supports strategic focus.