The utility sector is undergoing huge digital transformation, aiming to drive down costs and improve customer satisfaction while also facing growing environmental challenges that need to be addressed sector-wide.
Two major components underpin this change – the improved use of data and continued capital delivery across infrastructure.
As set out in Transforming Infrastructure Performance: Roadmap to 2030 – a new study developed by the Project Praxis Group at WMG University of Warwick in tandem with The Oakland Group – data enables the links between outcomes, decisions, and interventions to be better observed and understood.
The study consulted an extended panel of senior project practitioner experts from a range of infrastructure and utilities organisations. It highlights the cost savings and efficiencies that can be achieved through greater use of project data analytics across the lifecycle and supply chain of major infrastructure projects.
It is a key area to target in a sector where the ability to alter income is often restricted through price caps and limited competition.
A record £650 billion of infrastructure investment is projected over the next decade in the UK, and there are significant benefits to be derived from the greater use of technology in the planning of delivery of projects.
What are project data analytics?
Project data analytics involve using data to more effectively support decision-making in project delivery.
Infrastructure project delivery is perceived as notoriously poor, and feasibility studies indicate that using project data analytics more effectively could produce savings of over £23 billion per annum in the infrastructure sector.
Large projects are notorious for their perceived poor delivery, and at the root of this lies substandard decision-making. Decisions around original plans or reactions to events that may disrupt those plans are not taken in a timely fashion or with sufficient insight.
The utility sector has more data than ever as well as readily availability of tools that can combine, clean, structure, process and analyse data from disparate sources. With an ageing asset estate across many parts of the utility sector requiring significant investment and improvement, project data analytics have the potential to transform infrastructure project delivery.
Furthermore, in its ‘Time to act, Together’ strategy, Ofwat called for an open data approach from water companies to help transform their performance.
Need for data literacy
Project data analytics need ‘bottom-up’ development if they’re to recover the £23 billion annual sum mentioned for the infrastructure sector.
This means that project delivery professionals need a concerted agenda of data education and digital upskilling to be rolled out for them. The answer was not to have cohorts of data analysts join the ranks of infrastructure project practitioners.
What is meant by data literacy?
The study suggests that senior people make decisions based on data that fundamentally they don’t understand.
While organisations believe not everyone has to be a data person, they need some basic awareness – i.e. have an understanding the role of data and how it may contribute to decision-making and be tech-savvy enough to understand what tech can and cannot do.
It’s clear from the study’s participants that there are lots of variable skills across organisations in analysis, understanding, interpreting of information at a basic level.
Participants identified they were often encountering fundamental failures to use common definitions throughout their organisation for example, standardisation of field names. While we may have the same data, such as a project number, it often has a different name within multiple systems or even within the same system. This makes it challenging to link things up, so you have one system of the truth.
Many data formats are not compatible. It would be a step forward to just agree on what data format to use and develop a standardised data format or manual.
Given the huge potential for project data analytics to improve project delivery, it is clear that this potential is far from being achieved. The main reasons are the sheer magnitude of the implementation task and understanding and identifying the starting point.
But overwhelmingly, participants identified the importance of reducing fear around project data analytics’ implementation and improving people’s confidence in data-driven decision making.
Fear manifested itself not only in terms of fear of the unknown but with particular reference to the role project data analytics would have on existing project management roles. The idea that AI and by association project data analytics would eliminate specific functions and substantively change remaining roles led to a stymied opinion of project data analytics.
The lack of data literacy has contributed to this fear. While in actual fact, analytics using corroborative data across different data sets provides the project manager with confidence in making a decision.
The evidence is clear. Integrating data insight and decision making into daily operations can be transformational and organisations that are often awash with data just need help to access it in a way that can drive insight and change.
The technology is advancing exponentially, and putting the groundwork in today to make data accessible and useable will unlock the power of greater insight today and artificial intelligence tomorrow.
Digital transformation and collaboration will be two of the key drivers for the utilities sector if it is to achieve its potential as an enabler of the net-zero journey. But how do we break down silos, upgrade analogue skills for a digital age and convince customers that their data is safe with utilities?
A new report by Utility Week in association with Amazon Web Services – available to download here – seeks answers to these timely questions.
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