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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 2012OR54, September 2012
Outline of the talk
Introducing the Business & Team
Project Background
The Data Analysis Spiral
Other things included in the Guide
Key messages
Introducing the Business & Team
Project Background
The Data Analysis Spiral
Other things included in the Guide
Key messages
Our Business
• 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
Scottish Water aims to:
• provide high quality affordable water• protect and enhance the environment• support Scotland’s communities and economy
What the business does:
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
Project Background
The Water Statistics User Group
3 knowledge elicitation workshopsFinal draft and update to WSUG
Presentation at the IAM conference
Publish Guide
More demand for data driven-decision making in asset management,
shares statistical approaches & promote good practice data analysis across the water industry.
Jul 2010 - May 2011
Nov 2011
May 2012
Development approach
Motivation ?
thus a growing need for an in-depth data analysis.
Part I:
Data analysis spiral
Part II:
Basic analysis health checks & case studies
Data Analysis Spiral
Capture Stakeholder Requirements
Gather BusinessData
Conduct ExploratoryData Analysis
Develop AnalysisPublish Results & Identify Opportunities
for Improvement
Acceptance Test
Increasing Maturity
Increasing acceptance
Validate Analysis
1
7
2
3
4
5
6
Data Analysis Spiral
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.
1
Data Analysis Spiral
Gather BusinessData
Conduct ExploratoryData Analysis
• The analyst challenges the data quality and develops a good understanding of the data composition.
• 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.
2
3
Data Analysis Spiral
Develop Analysis
Validate Analysis
• Pragmatism of the outputs is challenged against expert knowledge.
• An robust methodology is designed, documented and applied to the data.
• Underlying assumptions are examined and accuracy of the outputs is assessed.
4
5
Data Analysis Spiral
Publish Results & Identify Opportunities
for Improvement
• Recommendations for improvement are identified and the maturity of the analysis is assessed.
• 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.
6
Data Analysis Spiral
Capture Stakeholder Requirements
Acceptance Test
• A further iteration of the Data Analysis Spiral is initiated if the stakeholder is not satisfied.
• Stakeholders provide detailed feedback to the analyst.
7
Other things included in the guide
• examples of best-practice for each step of the Spiral. • describe potential consequences when best-practice is not applied
the analyst provides the stakeholder with analysis proposal the data can be audited documentation is version controlled
Case Studies
Analysis Health Checksa simple to-do list
real-world
What are the key messages?
Using the good practice guide, analysts can demonstrate transparency, consistency and quality in their analysis.
The growing need for robust data analysis and data management is reflected across all asset management sectors.
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.
If you would like a copy of the guide please contact us:[email protected]@scottishwater.co.uk
Thank you
