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.

1. How Digital Asset Management is putting firms on the front foot?
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 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 organizations, the potential benefits are huge.
Why are some organizations better at Digital Asset Management?
However, while most organizations 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, organizations must set aside the idea that data is incidental to their real business. Only
by treating data as an asset — often the most 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.

2. An optimized data asset management model requires you to:
1. 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.
2. 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.
3. Define data collection strategy: every organization 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.
4. Present the data in a usable form: the outcome and benefits can only be realized 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 program. 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 immediately, 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

3. 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.
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.