David Price, Associate Director at Amec Foster Wheeler, looks at the benefits for companies in taking greater ownership of data.
Population growth and climate change; water stress; stricter environmental regulation; ageing infrastructure and networks; improving customer satisfaction; tackling affordability and maintaining a resilient service are key challenges for water companies. Addressing these challenges requires greater innovation and the acquisition and management of accurate data to support effective, informed decision-making.
Companies have responded by developing novel approaches to engagement across all aspects of the business. Innovation days, pitching of ideas, collaboration with academia, and collaborations with organisations not traditionally associated with the sector. The goal is to capture the right data at the right level of granularity in order to obtain an understanding and knowledge of asset performance and serviceability. With this, trends can be analysed, reactive responses can be more efficient and pre-emptive interventions implemented.
However, far too often, data are not captured in a format which aligns to corporate systems and thus, the value of the data to a business is diminished. Useful data for one function of a business may be as useful to another and should be available for use. A stand-alone data system can be quickly forgotten about and overlooked. This shortcoming is more significant when multiple unaligned data sources are used to support integrated holistic studies.
Much of the effort in feasibility studies and solution development is absorbed in gathering, cleansing, validating, combining and correlating datasets. These are generally off-line activities undertaken by external service providers for specific departments and the knowledge gained is often ‘put on a shelf’, not accessible or disseminated to the wider business. Water Networks for example, are dynamic, and as such the operation and condition of the associated assets and characteristics of its users are constantly changing. Even with improvements in the quality and coverage of corporate system monitoring and performance reporting, gaps will remain. For example, local operatives, based on experience, will know the correct response to a loss of supply. This critical local infrastructure insight is frequently not captured in a digital and readily accessible domain.
A key step forward for a business would be to take full ownership of the data gathering, analysis and dissemination processes. Historically, businesses have adapted their systems to incorporate 3rd party platforms. The resulting requirement to extract and cross-reference data from various, and often incompatible, data systems can be a time-consuming and complex process.
The goal must be to have centralised data storage and access structured around the businesses core data systems with these operated as the only source of data. However, this must go hand in hand with improvements in accessibility and of provision of feedback and regular updates. Capturing of good quality, well targeted and well-structured data must be backed up by analysis processes to convert it to systems knowledge. This can be achieved in part using compatible third part technology solutions but this must be supported with the knowledge and experience of those operating the assets in a circular process which feeds in to intelligent control systems.
Robust feedback mechanisms with frequent and accessible dissemination of knowledge across all functions of the business are fundamental in maintaining a dynamic, fit for purpose, calibrated corporate data system. Regular multi-function workshops are a mechanism for knowledge facilitation, validation and update. Collaboration is key.
Companies are moving towards whole-system assessment; performance and impact data from source to tap is combined with operational knowledge and a data-driven, unbiased, quantitative analysis undertaken to understand the root causes of issues and weaknesses in resilience to better target investment. This approach gains buy-in from stakeholders and maximises cost-benefit to customers.
To do this effectively, data analysis processes which can determine relationships and the strength and nature of correlation between different types of data have been developed usually by third parties. The next step is to move these processes into business as usual, fully transparent systems, maintained in-house, which are not restrictive and can evolve as required. Whether internal processes or third party provided, these must align with the core data storage structure and report directly to the core knowledge management structure. Combined with improved data capture this will allow on-going quantification of benefit which will help to optimise future investment and asset management decisions.
