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Printer Friendly VersionSelected Information - Completed Research

CP473  Practical guidance to improve telemetry data quality Data Management
Project Appreciation and Objectives

Water companies are becoming more reliant on telemetry and this is increasing the need to ensure adequate levels of data quality. Ofwat also requires water companies to make better use of real-time telemetry data and to demonstrate that they understand the uncertainty in the quality of data. Current trends to increase real-time visibility of assets and make greater use of advanced data-driven technologies will further highlight the need to tackle data quality. However, the sheer scale and complexity of water industry telemetry systems present a significant data quality challenge.

Portfolio project CP401 A Framework for Improving Telemetry Data Quality confirmed that poor quality data are a concern for all companies and, in many cases, senior management are unaware or unsure of the real impacts of poor telemetry data and the investment required to address this.

This project will build on CP401 to provide practical guidance to implement targeted improvements in telemetry data quality.

Benefits to Clients

  • Informed business strategy and targeted investment.
  • Leverage more value from data to improve performance and reliability of operational tools and achieve more effective decision making.
  • Provide a data quality management health check.
  • Demonstrate to regulators and auditors that sound principles and good practice have been applied to ensure that data is fit for purpose.

Work Programme

  1. Develop a generic end-to-end telemetry system model (data, technology and processes).
  2. Review the model and identify weak links in the data chain that can impact data quality, obtain consensus on the priority areas to be investigated in more detail: e.g. risk based maintenance prioritisation, configuration change control, automatic anomaly detection, dealing with missing or uncertain data and active quality management and monitoring.
  3. Assess specific data quality and businesses impacts, and identify root causes.
  4. Determine necessary improvement actions and develop a business case and implementation plan for each area.
  5. Disseminate and discuss the findings and best practice guidance for implementation.

Project Output

  • Documented generic system model and potential sources of data quality problems.
  • Documented findings and best practice guidance on the data quality improvement actions for agreed areas, business cases and implementation plans.

Related Work

  • A Framework for Improving Telemetry Data Quality, CP401, 2010/11.
  • Improving AMA through Data Quality, CP419, 2010/11.
  • Water Industry Alarm Systems Improvement Group, CP321a, 2009 to date.
  • Creating Value from Data, CP381, 2009.
  • UKWIR project WW21 on Data Flagging for Sewage Treatment Works Flows, 2008.
  • Real Time Data for Asset Management, CP295, 2006/7.
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