All water and waste water utility companies in the UK face similar challenges around revenue growth, operating margins, asset performance, resilience, and disruptive forces that are driving a new focus on the customer. At the same time management teams are calculating the impact of climate change, new regulation, cyber threats, competition from market reform, and new information requirements from MOSL and OFWAT such as data quality, information assurance, and customer evidence to support future capital investments.

Water and waste water utilities want to deliver a better service to their customers, deliver better product to their customers, avoid waste and increase productivity, leverage insight from the information they already have (transform the way everyone think about water as a resource), create new deeper insights from interconnect internal and external data (transform the way planners invest and manage infrastructure in the future), and adopt next generation digital capabilities in the hope they will bring competitive advantage.

Aiimi believe organisations need to protect their own data assets whilst at the same time make best use of external data and invest in acquiring new data assets such as the advance in IoT, remote sensing, and smart meters. Through good governance with high quality information and mature digital capabilities, organisations can maximise advances in artificial intelligence and machine learning, driving more automation, and enabling the disintermediation of the middle office.

• Market reform • Service Improvement Mechanism • Customer segmentation and journey• Price review • Asset operational maintenance • Capital delivery • Workforce engagement • Predictive analytics • Carbon footprint • Leakage • Network resilience • Water quality

Technology and Services:
• Visioning • Information management strategy • Development of strategies and supporting roadmaps • Information governance • Proof of technology • User Experience driven agile application build • Data Architecture i.e. information modelling, metadata management, master data management, and data quality profiling • Data Science – i.e. data exploration, artificial intelligence, and machine learning •