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

1Initial

• Practice

  Capture and manage data to drive systems and keep local transactions working.

  Outcome

  Risk exposure is not identified, understood or managed.

  Metrics

  • # Data valuation criteria identified.
  • # Total cost of storage.
  • # Business value or turnover enabled by the data/information.

2Basic

• Practices

  • Log, measure and report data issues.
  • Root cause analyse big impact issues.
  • Use top down (macro economic) evaluations e.g. transaction DB enables €250k a day turnover or €23k a day is enabled via web comparison web site business referrals (these measures are easier than bottom up activity based accounting approaches).

  Outcomes

  • Awareness of the cost of data and data infrastructure issues is beginning to get noticed and acted upon.
  • An understanding of the causes of these issues is emerging.
  • Total loss or cost per minute hour or day is known if the system is not available.

  Metric

  # Databases or Systems where costs are known and/or business value is known in terms of profit or turnover.

3Intermediate

• Practices

  • Measure the cost of data, value of data and the risks associated with data.
  • Execute data risk management processes.
  • Use top down (macro economic) evaluations.
  • Use bottom up accounting (activity based accounting) methods.
  • Map risk management identified ‘value-at-risk’ costs to the data.
  • Measure the cost of data quality and the impact of poor data quality.
  • Identify the sources of data quality issues.

  Outcomes

  • System loss and system impairment costs are understood.
  • Usually, estimates for recovery are known.
  • Business risk values are known.
  • Data quality improvement initiatives are funded and saving derived are recognized.
  • The sources of data quality issues are actively being addressed.

  Metrics

  • # Databases or Systems where costs are known.
  • # Activities where activity based accounting has valued data.
  • # Risk management issues mapped to data management.
  • # Data quality related rework effort and cost.
  • # Estimates of business lost due to poor data quality.
  • % Sources of data quality issues being addressed by value.

4Advanced

• Practices

  • Conduct cost benefit analysis on data and information architecture quality.
  • Design solutions based on a defined data risk mitigation set of criteria.
  • Use top down (macro economic) evaluations.
  • Use bottom up accounting (activity based accounting) methods.
  • Map risk management identified ‘value-at-risk’ costs to the data.
  • Measure the cost of data quality and the impact of poor data quality.
  • Identify the sources of data quality issues.
  • Factor the time value of information in solutions design and implementation.
  • Exploit data and information for value internally.

  Outcomes

  • Expenditure decisions in data management are data driven.
  • Risk mitigation by use of concepts like self-auditing processes significantly reduce data ownership costs.
  • System loss and system impairment costs are understood.
  • Usually, estimates for recovery are known.
  • Business risk values are known.
  • Data quality improvement initiatives are funded and saving derived are recognized.
  • The sources of data quality issues are actively being addressed.
  • The timeliness of data and reporting is significantly better in particular the provision of control or decision making information.
  • The strategic focus of analytics and business intelligence is yielding higher values from information.

  Metrics

  • # Databases or Systems where costs are known.
  • # Activities where activity based accounting has valued data.
  • # Risk management issues mapped to data management.
  • # Data quality related rework effort and cost.
  • # Estimates of business lost due to poor data quality.
  • % Sources of data quality issues being addressed by value.

5Optimized

• Practices

  • Improve processes using both continuous and disruptive methods to mitigate risk and maximize data value and re-use.
  • Reduce risk and facilitate data stewardship with incentives.
  • Use multiple accounting and valuation methods to provide a full valuation of data.
  • Map risk management identified ‘value-at-risk’ costs to the data.
  • Measure the cost of data quality and the impact of poor data quality across the business ecosystem.
  • Identify the sources of data quality issues.
  • Factor the time value of information in solutions design and implementation.
  • Develop and manage information as a strategic asset to be exploited by the business either internally or/and externally.

  Outcomes

  • A culture of data stewardship and continuous improvement reduces risk and maximizes return on the cost of data.
  • System loss and system impairment costs are understood.
  • Usually, estimates for recovery are known.
  • Business risk values are known.
  • Data quality improvement initiatives are funded and saving derived are recognized.
  • The sources of data quality issues are actively being addressed.
  • The timeliness of data and reporting is significantly better in particular the provision of control or decision making information.
  • The focus of analytics and business intelligence is strategically aligned and increasing the value of information.

  Metrics

  • # Databases or Systems where costs are known.
  • # Activities where activity based accounting has valued data.
  • # Risk management issues mapped to data management.
  • # Data quality related rework effort and cost.
  • # Estimates of business lost due to poor data quality.
  • % Sources of data quality issues being addressed by value.
  • # Business strategy driven analytics and business intelligence objectives.