Data Analysis in the Water Industry: A Good-Practice Guide Deborah Gee, Robert Murray 12th May 2011

WSUG Objectives Share approaches across the water industry so that applications/methodologies are fit for purpose and stand up to scrutiny; Promote greater use of quality data analysis within the industry

Timeline 21st July 2010 Workshop to identify opportunities to improve standard of data analysis across the industry. 17th Feb 2011 1st Draft of Guidance produced. Feedback from working group. Today 2nd Draft of Guidance and update to WSUG.

Increasing acceptance Increasing Maturity Capture Stakeholder Requirements Gather Business Data Acceptance Test Conduct Exploratory Data Analysis Increasing acceptance Increasing Maturity Publish Results & Identify Opportunities for Improvement Develop Analysis Validate Analysis

Business need is formulated and confirmed with stakeholders Capture Stakeholder Requirements Gather Business Data Acceptance Test Conduct Exploratory Data Analysis Increasing acceptance Increasing Maturity Business need is formulated and confirmed with stakeholders (defining the question that the stakeholder needs an answer to) The format of the outputs are agreed with the stakeholders. (defining what the analysis needs to produce) Acceptance tests are put in place to manage the sign-off process. Publish Results & Identify Opportunities for Improvement Develop Analysis Validate Analysis

A clear audit trail for the data is established Capture Stakeholder Requirements Gather Business Data Acceptance Test Conduct Exploratory Data Analysis Increasing acceptance Increasing Maturity Robust corporate data sources are identified or appropriate data collection mechanisms are put in place. A clear audit trail for the data is established The analyst develops a good working understanding of the data and challenges data quality with the data owners. Publish Results & Identify Opportunities for Improvement Develop Analysis Validate Analysis

Pragmatism of the outputs challenged against expert knowledge. An acceptable and justifiable methodology is designed and documented to generate the desired outputs from the available data. An in-depth understanding of the accuracy of the outputs is gained through statistical testing – confidence and significance levels. Pragmatism of the outputs challenged against expert knowledge. Capture Stakeholder Requirements Gather Business Data Acceptance Test Conduct Exploratory Data Analysis Increasing acceptance Increasing Maturity Publish Results & Identify Opportunities for Improvement Develop Analysis Validate Analysis

Outputs from the current iteration of the spiral are finalised and released to the stakeholders in the format agreed. Documentation is prepared for technical and non-technical audiences, alongside training material as appropriate. Recommendations for improvement are identified and the maturity of the analysis is assessed. Capture Stakeholder Requirements Gather Business Data Acceptance Test Conduct Exploratory Data Analysis Increasing acceptance Increasing Maturity Publish Results & Identify Opportunities for Improvement Develop Analysis Validate Analysis

What’s Also Included Case Studies Some dummy examples of why the guidance in each section is important. What is the outcome of bad/good analysis in the water industry. Analysis Health Check e.g. The data can be supplied on a regular basis. Imputed data can be easily separated from real-world data points. The data source is known.

Next Steps Feedback from WSUG More example case studies. Issue draft for consultation on WSUG website September 2011 Test guidance within companies. Consultation with wider stakeholders, e.g. Ofwat, RSS Publish guidance March 2012.

Data Analysis in the Water Industry: A Good-Practice Guide Thank You 12th May 2011