Digital Asset Management: Getting on the front foot
Atkins’ Ash Toma comments on how companies can wring the maximum value out of their data
In June 2021, the European infrastructure sector warned that costs were rising significantly – up to 20% in some cases. And the EU isn’t alone. In the US, research by the Federal Reserve found 65% of construction firms said that high input costs were dampening demand and 70% said rising material prices were eating into their profits. In China, the cost of some materials has risen by almost 40% in the last year.
With customer demands growing, regulations around health and safety and net zero increasing, and existing infrastructure aging with each passing day, companies are now looking for a more data led decision making approach and intelligent ways to maintain and operate their assets.
Data and smart tools are increasingly enabling businesses to make better decisions, whether operational or strategic. This is where Digital Asset Management (DAM) comes in, to inform decision-making based on asset insights, such as condition, costs, and future requirements. Although this represents a significant shift in mentality for many organisations, the potential benefits are huge.
However, while most organisations agree that data is critical to their future success, some are faring better than others at harnessing its power. Those with less success seem to be missing a clear link between the asset information strategy and the wider asset management strategy.
To avoid this, organisations must set aside the idea that data is incidental to their real business. Only by treating data as an asset — often the most valuable asset on their inventory — to be actively managed across its entire lifecycle for maximum returns, can they hope to achieve the kind of savings and efficiencies necessary to give them a competitive edge.
An optimised data asset management model requires you to:
- Define your objectives: only when you know what you want to achieve, can you hope to effectively use data with maximum effect. Start with the key operational and strategic risks that if effectively mitigated will provide the greatest outcome
- Define your data points: decide what data you need to meet your objectives, then use this to define data-points relevant to your physical infrastructure. Also, define your database scheme to outline how different datasets will be connected to provide a more comprehensive insight into any given physical asset
- Define data collection strategy: every organisation and every project will have its own set of constraints; financial, time, technology, level of data accuracy or regulatory constraints that will have an influence in selecting the most effective approach for data collection
- Present the data in a usable form: the outcome and benefits can only be realised if the data is consumed by the business. The adoption is directly linked to having the data transformed into meaningful information and presented to the end user. Essentially, for each relevant audience group with their defined use cases, make the data available in a form that enables them to make the best possible decision at any given time
Building the Right Model for your Company’s Goals
This four-stage process forms the backbone of any successful data asset management programme. But within this relatively simple model, there is room – indeed a necessity – for many refinements, depending on your company’s specific needs and the audiences you’re addressing.
Business and finance leaders, for instance, tend to want to create dashboards, which allow them to see different data sets at a glance, so they can factor these into their decision-making. Engineers, on the other hand, may prefer data-rich engineering plans and 3D environments.
Sometimes, companies want to jump straight to the task of creating a digital twin — a data-enriched, 3D replica of their asset infrastructure. But while this is often the most efficient way of presenting the insights gained from data asset management, it’s only the endpoint of the four-stage process described above. A digital twin can only ever be as good as the data being fed into it.
Finally, if the principle that data is an asset is adopted, it should also follow a similar asset life cycle as physical assets. In a similar way to physical assets, data must be maintained throughout its life and when it has reached its defined useful life and is no longer needed, it must be disposed in a controlled fashion so that data leakage and security risks are effectively managed.
Digital Asset Management in action
Within the aviation sector, our client, a large airport owner/operator, was faced with the challenge of delays in the readiness of new infrastructure and needing to extend the life of existing assets in order to remain operational. To find an optimum asset management strategy, it was concluded that a more in-depth asset health check was required. This was to determine the asset criticality, historic performance, current condition, age, degradation profile and replacement cost.
Following an initial study of the full infrastructure, an initial list of around 76,000 assets were determined to be crucial to its operations. By assessing the asset condition and defining a degradation profile for each asset class, they were able to develop an optimum plan to determine at what point they needed to be maintained, refurbished, or replaced. They received accurate cost estimates for type of intervention to produce a full 25-year investment plan, reducing the asset whole life cost by extending the life of the asset. This information was also used in making important decisions around asset renewal plans during the height of the pandemic, when operational and cost pressures were at their most intense.
With tightly defined, well executed asset information strategy, companies can wring the maximum value out of their data and out of new and innovative ways of working, such as the use of IoT, robotics and ultimately derive to a digital twin. Doing so gives them the edge over their competitors. It also puts them in pole position in the race to deliver the intelligent, sustainable and cost-effective infrastructure that the market of the future demands.
