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Data Analysis in the Water Industry: A Good-Practice Guide with application to SW

Data Analysis in the Water Industry: A Good-Practice Guide with application to SW Deborah Gee, Efthalia Anagnostou Water Statistics User Group - Scottish Water OR54, September 2012. Outline of the talk. Introducing the Business & Team Project Background The Data Analysis Spiral

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Data Analysis in the Water Industry: A Good-Practice Guide with application to SW

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  1. Data Analysis in the Water Industry: A Good-Practice Guide with application to SW Deborah Gee, Efthalia Anagnostou Water Statistics User Group - Scottish Water OR54, September 2012

  2. Outline of the talk • Introducing the Business & Team • Project Background • The Data Analysis Spiral • Other things included in the Guide • Key messages

  3. Our Business • provide high quality affordable water • protect and enhance the environment • support Scotland’s communities and economy Scottish Water aims to: What the business does: • supply water to 2.4m households & 152,000 businesses • we manage ~97,000km of buried pipes & ~2,100 treatment works • we have 3,700 staff and revenue of £1bn per year

  4. Our Team: an in-house analytics team • Vision: grow the value of analytics in the water industry • Skill sets: statistics, operational research, computing & asset risk management • Services: develops analytical tools to support the business and in particular asset decision making. • Partnerships: Universities and Industrial Groups RISK CONSORTIUM

  5. Project Background shares statistical approaches & promote good practice data analysis across the water industry. The Water Statistics User Group Motivation ? More demand for data driven-decision making in asset management, thus a growing need for an in-depth data analysis. Development approach 3 knowledge elicitation workshops Final draft and update to WSUG Presentation at the IAM conference Publish Guide Jul 2010 - May 2011 Nov 2011 May 2012

  6. Part I: Data analysis spiral Part II: Basic analysis health checks & case studies

  7. Data Analysis Spiral 1 2 Capture Stakeholder Requirements Gather Business Data 7 3 Conduct Exploratory Data Analysis Acceptance Test Increasing Maturity Increasing acceptance 6 4 Publish Results & Identify Opportunities for Improvement Develop Analysis 5 Validate Analysis

  8. Data Analysis Spiral 1 Capture Stakeholder Requirements Acceptance Test • Business need is formulated and confirmed with stakeholders. • The format of the outputs are agreed with the stakeholders. • The appropriate level of uncertainty is agreed with stakeholders.

  9. Data Analysis Spiral 2 Gather Business Data 3 Conduct Exploratory Data Analysis • Data is obtained from robust corporate data sources or appropriate data collection mechanisms are put in place. • A clear audit trail for the data is established. • The analyst challenges the data quality and develops a good understanding of the data composition.

  10. Data Analysis Spiral • An robust methodology is designed, documented and applied to the data. • Underlying assumptions are examined and accuracy of the outputs is assessed. • Pragmatism of the outputs is challenged against expert knowledge. 4 Develop Analysis 5 Validate Analysis

  11. Data Analysis Spiral • Outputs from the current iteration of the spiral are finalised and released to the stakeholders. • Documentation is prepared for technical and non-technical audiences, alongside training material. • Recommendations for improvement are identified and the maturity of the analysis is assessed. 6 Publish Results & Identify Opportunities for Improvement

  12. Data Analysis Spiral Capture Stakeholder Requirements 7 Acceptance Test • Stakeholders provide detailed feedback to the analyst. • A further iteration of the Data Analysis Spiral is initiated if the stakeholder is not satisfied.

  13. Other things included in the guide real-world • examples of best-practice for each step of the Spiral. • describe potential consequences when best-practice is not applied Case Studies  the analyst provides the stakeholder with analysis proposal  the data can be audited  documentation is version controlled Analysis Health Checks a simple to-do list

  14. What are the key messages? The growing need for robust data analysis and data management is reflected across all asset management sectors. Using the good practice guide, analysts can demonstrate transparency, consistency and quality in their analysis. Within SW the guide…  is a benchmark for assessing data analysis.  creates a standard process for data analysis which meets the requirements for ISO9001.  inform stakeholders of what good analysis is.

  15. If you would like a copy of the guide please contact us: Deborah.gee@scottishwater.co.uk Efthalia.anagnostou@scottishwater.co.uk Thank you

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