At Esri UK Annual Conference 2014 United Utilities (UU) is one of the UK’s largest water and wastewater companies with seven million people in the North West of England. UU had been working with an old GIS ‘landscape’ implementation, comprising five separate systems. Without an enterprise GIS platform affording ‘one version of the truth’, there were risks to the business of making decisions based on wrong or missing information. UU embarked on the journey of Linear Asset Management (LAM) programme with an aim to create a future-proof GIS platform and take UU right to the front of the utility industry in terms of leading-edge technology and state-of-the-art information management.

1. Tim Stanley – United Utilities

2. Our Strategy and vision – History of
GIS in UU
1900 – 1994
• Asset records hand drawn in a number of individual local
drawing offices.
• Wax on linen drawings, 1250 plans, manhole cards, trial
hole records.
• No uniform data capture standards in place.
• Sewer maintenance carried out partly in house, partly by
local authorities.

3. Strategy and vision – History of GIS
in UU
1994: First UU GIS – WaterGS
• Autodesk based product, integrated with
customer contact and work management
mainframe systems.
• Significant programme of digitising on and
offshore to create digital records for the
first time.
• Used to manage water assets, land and
property boundaries and incidents.
1997: Wastewater assets are migrated
into WaterGS
1997 – 2011:
• Business requirements drive development
of further GIS solutions, MapInfo,
Strumap, Map Extreme etc.

4. Map
Info
Strumap
What’s wrong with this picture?
Capital projects
Field reports Network Updates Data everywhere!
transfer
extract
manually update
transfer
extract
manually update
Map/geographic information available to the relevant business areas
Water GS
Why We Needed to Change

5. A consistent view of our network – wherever you are, whatever you do!
One
mapping
system
Step 1: Where Do We Need To Be

6. Step 2. Check The Foundations:
Data Work Package
• Data required migration from a number of sources, either current corporate
recognized master data sets or stand alone business data sets.
Our chosen migration path was:
• Data Discovery determining the data to be
migrated – UU / 1Spatial.
• Extract – United Utilities.
• Cleanse – 1Spatial.
• Transform to new data model – Wipro.
• Load to the new solution – Wipro.
• Total number of data sets migrated – 357.
• Total number of features migrated – 49,080,047 (including ALL attribution).

7. Strengthen the Foundations: Data
Improvement
• Three main themes:
• Data Cleansing: correcting spatial and non-spatial errors (e.g. where
pipes/sewers were not connected at nodes due to digitising errors.
• Data Enrichment: completing missing attribution on existing assets;
either by reference to other datasets, by association with other
connected assets, or automatic derivation by domain experience.
• Data Inference: the creation of missing assets using knowledge from
other UU and reference to external authoritative datasets (e.g. OSMM
and AL2).

8. Data Results: Cleanse and Enrichment
Data Cleansing
• Clean water connectivity – improved from 73% to 99%
• Wastewater connectivity – improved from 76% to 93%
Data Enrichment
• Clean water attribution completeness increased from 36% to 93% for key
attributes (material, size, age), using investment prioritisation reference data
in addition to lead risk and, ‘estimate from neighbours’ rules.
• Wastewater attribution completeness improved from 3% to 91% for key
attributes (material, size, age, invert levels and depth), using investment
prioritisation reference data in addition to the ‘estimate from neighbours’
rules.

9. Data Results: Data Inference
• Inferring the route of unknown assets, resulting from historic partial records capture.
• Using United Utilities wastewater subject matter knowledge alongside the 1spatial
team and Radius Studio tools, we managed to build, test, improve and implement a
set of business rules to derive the position of unmapped sewers -
Before After

10. Data Results: Data Inference
• Using our data inference techniques, we created:
– 7,000kms of sewer pipelines (Ex Section 24 sewers)
– 880,000 clean water service connections
• Recognising the benefits of the inferencing techniques, we have since
completed a further project to create a geographic model of our transferred
sewer assets:
– 3 months of iterative workshops to collect and express business domain
knowledge as a set of expert rules.
– Automated transferred sewers inference took 2 days to build the
inferred network for the entire region – creating 31,000km of network.

11. Step 3. Design & Build

12. • Industry standard off
the shelf package.
• Tools for network
design, asset
investment
prioritisation &
modelling.
• Stored views for
main map users.
• Data governance
assigned for layers
and local champions
for user support.
Desktop: ESRI ArcMap 10

13. • Improved
submission of
updates (red-
lining)
• Live valve status
changes from field
to centre
• Overnight update
from database and
data on call
replaces quarterly
manual process
Water & Ww Field: Arc GIS Mobile

14. • Easy navigation
and open to all
staff
• Advanced tools
including query,
draw and share
• Aerial photography
and street view
links
• Stored views for
main map users
Web: Latitude Geocortex ‘onemap’

15. • Site Area
account details
can be modified
by customers on
line and queued
for approval and
billing change
External User: Site Area

16. Step 4. Business Change & Delivery
 Dedicated business change
work stream from early in the
project. – Key in assuring
solution fitness.
 Business lead with joint
responsibility with IT delivery
manager and programme
manager for delivery.
 Business readiness group
(BRIG) meetings monthly to
review progress.
 Commitment cards provided to
all users to clarify scope and
set expectations

17. Business Change & Delivery
 Assignment of Data Stewards and GIS
Champions gave clarity on roles &
responsibilities, and provided processes
for effective data governance & business
improvement.
• Go live support was a particular success
with project team members located with
users during the first week of go-live.

18. Lessons learned and Next Steps
• Version upgrade
• Consolidation of address management
processes and implementation of AddressBase
• Continued development of data and
connectivity to support future requirements
• Understanding the user base; we believed we
had in the region of 500 users of applications to
be decommissioned. Later analysis revealed a
total of 925 users.
 Tailor training as closely as possible to
processes, including training of any bespoke
tools for particular departments.
 Success is driven by the passion and
involvement of employees, get them involved
early.