Problem

The water industry delivers its products and services from a foundation of distributed assets.  Most of the asset base was adopted through privatisation and has been expanded on through further adoption, capital investment or maintenance requirements.  Many of the assets or subsequent improvements have been delivered regionally by different inhouse or contracted services.  Whilst the projects have been managed efficiently against engineering and delivery requirements, in some cases, less attention has been paid to the accuracy of data handover and subsequent capture through the life of the assets.

There is now a low level of trust in some data areas and no corrective measure has ever put in place a framework to identify with any degree of certainty what value is provided by which data.  Asset Managers speak anecdotally about what ‘good data’ is and how trust in some information would enable better decision making; in these conversations’ opinions vary across functions and seldom is monetary value placed on the data improvement programme or the benefits arising, making business case development difficult.

Impact

• Asset management decision making
• Regulatory reporting
• Work planning
• Procurement
• Exclusion from emerging technologies
• Lower business performance in all key areas (health and safety, innovation, water/waste control, environment, customer satisfaction, energy use, carbon accounting)

Project

Development of a framework that allows data to be drawn in if identified as ‘useful to business outcomes’.  Provide a trust rating on the quality of the incoming data.  Assign a value to the data as a component of the decision process. Provide a further value on the accuracy of the decision that the data supports. Recommend data infilling techniques and their benefit to any decision.

Outputs

• Summary of key datasets shaping business decision making
• A framework that:
  • Assigns a value to all data based on its influence in the decision-making process
  • Depicts all decision data and trust level scoring
  • Provides a decision success value with provided or infilled data
  • Allows for different valuations dependant on user (as the same dataset may be valued differently dependant on its use)
• Develop a methodology for assigning monetised benefit for each data point – based on its influence on the decision making or improvement in accuracy/confidence in the decision that might be obtained.
• Review of role requirements and processes for collecting, assuring, and modelling decision data