Data-Centric Asset Ownership

The reality of today is that organizations of all types need major capital assets in order to operate. From buildings to infrastructure, transportation to manufacturing equipment, large assets literally make the world go round. While the legal ownership of assets is often the most obvious and oft considered relationship with assets—since they are typically significant monetary value—the benefits of assets are mostly enjoyed over their operational life as they facilitate the organization’s needs.

The consequence of this is that many asset owners regard their asset as a bond, a special type of bond that happens to provide value to their operational needs. This apparent serendipity, unfortunately, leads us to opportunities for financial engineering to try and squeeze out every penny of the asset’s value. This penny-wise dollar-stupid thinking often leads to placing the owning and operating activity in separate stakeholders, which results in conflicting interests against the well-being and long-term value of the asset for the owner. The very nature of a lease between a landlord and a tenant is a prime example of this conflict, often referred to as split incentives.

Any middle school student would have had their mother teaching them that if they look after things, they last longer so they can enjoy their benefits longer. The same applies to major capital assets. We are fortunate that we are living through a digital transformation era where digital twins can actually do that, look after major assets digitally from their conception, construction, or manufacturing, and through their operational life. With today’s connectivity and big data prowess, analyzing and interpreting data of all aspects of an asset is almost rudimentary, if all the data is available.

In long lifecycle assets like buildings and infrastructure, there are often many different stakeholders that are involved with the asset: land-owner, investor, design and construction, trades, operator, landlord, multiple tenants, maintenance, occupants, and so on. Each of these stakeholders is motivated by their respective responsibilities and will thus collect, gather, analyze, and otherwise use data related to the asset for their own need with no inherent motivation or ability to make such data available to the owner or other stakeholders. Data silos of the asset are thus born, repeated for each entity involved. The IoT trend, while creating value, exacerbates this even further by ironically creating unconnected silos.

Meanwhile, the asset owner, the entity that has most to gain from data about their asset, ends up with several siloed datasets, if they have that data at all. In many cases, the owner does not have any rights to some silos of data, so the owner at best has partial data in silos with little hope of aggregating that data to improve the return of their investments in the asset.

Studies of the value of digital twins on assets such as jet engines show that having the full set of data about an asset can improve performance by as much as 10% and reduce operational costs by as much as 40%. Jet engine manufacturers like GE and Rolls Royce can achieve these levels because they have engineered their business model to retain the ownership and use of data throughout the life of those assets, avoiding the silos typically experienced by other asset owners.

What is to be learned from this? Asset owners owe it to themselves to insist that ownership or at least unfettered access to all their data about their assets must be seen as part of their ownership of the asset in order to truly maximize the asset’s value. Owning an asset without the ability to use and controlling the governance of the data about it is somewhat like owning a business without having access and control of its accounting data, at best this could be described as stumbling in the dark.