Asset Intelligence – New Nomenclature?

Let’s kick of with some standard definitions to establish common ground.

According to Wikipedia an Asset is defined as: An asset is a resource controlled by the entity as a result of past events and from which future economic benefits are expected to flow to the entity (

Intelligence is also explained as: More generally, it can be described as the ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviours within an environment or context. (

These two can then jointly form Asset Intelligence which an excellent article by Shawn Taylor expressed as: Asset intelligence is a concept in which organizations have access to a rich set of details regarding the assets that are deployed in the environment. (

After the preliminaries let’s get into the core here. Some of my colleagues agree with me that this description of Asset Intelligence is better for being enhanced by adding 4 aspects to it:

1) One rich set of data about an asset is a great start, but as for the term intelligence we think it needs at least 3 aspects of the asset to hold the title intelligence. Three aspects could for example be: A computer where the first set is about the hardware itself, make, model, tech spec and price. The second set is about the software and usability of the computer, which programs and controls it allows a user of the computer to utilise. The third set of data is around what it’s being used for, the very purpose of it, the reason for actually having a computer and software combined in such a way. The first two can be factual and measured, but the third can also be abstract and difficult to measure or capture, or even be based on observations or experiences captured and added to the set. In this case it could be observations that a certain software is only used in such a way, or specific times, or during an uncharacteristically short time span, that this tells us something dependably about it. Deducted this could mean that the software is not used for it’s intended purpose, which could indicate a new or misunderstood need by the user.

2) Making one or more connections between the rich asset data sets This is the tricky part without a formula, as it is mainly intuitive and depending on your objectives, hence also highly biased. So if you look for something that should be removed to realise a saving you do it in one way, but if you want to maximise use of an asset another. This is where someone that really knows the environment or highly capable analyst earns their crust! The most obvious connections will largely depend on your viewpoint but looking at a dataset many times with clever people that have dissimilar viewpoints is my personal favourite trick! Do make the really smart connections and prove them as far as you can, then store the output and how you used it as per next point.

3) Persistent storage of the asset intelligence allows reuse and hopefully continuous addition of attributes, connections to related data and even more meanings to the data. Thus making it, with a risk of hubris here, NOT more intelligent, but more useful and with better insights. It also needs to be accessible for the resources that can enhance it or make business use of it. The more you do of these, the more you will strive for making the documentation similar and easy to digest and reach.

4) Utilisation of the Asset Intelligence in question. If the asset intelligence is a rich set with 3 or more aspects that are persistently stored and accessible, then for it to reach the high accolade of being called: Asset Intelligence, it then subsequently needs to be used. Only when it is applied and that resultant data in turn is stored, along with the decisions made thereupon are we content with it being labelled: Asset Intelligence. If you manage to create a saving for example, that should of course also be stowed, preferably with the data set, enriching it. These actions vastly increase its usability, and therefore the actual value to the business.

Why is it important at all to document, learn from and spread knowledge is of course that it can then in turn be applied to other aspects, make more connections. ( the explanatory picture).

The use of the insights for a purpose (business or otherwise) is sometimes controversial, but considering if you gather rich data about an asset, combine that with other data sets, making clever connections and generating insights…..but then not using it seems like a waste, therefore not intelligent? Granted that sometimes the only option is to wait until a good time to use it, but document and wait until prudent, then use it is of course fine too!

I have seen, generated and presented insights that really should have prompted immediate action, for instance risks in assumed secure technical environments. However, for reasons unknown (but usually valid) the actions did not take place. The whole thing is then dropped, and everyone moves on. Sometime later, a similar analysis is done by others, possibly even the exact same. It yields the same insights and prompts similar actions. This time though, this could have been wholly avoided or made faster by documenting, storing it and making it accessible. This regretful waste is quite common, and by the rapid changes in staff, technology, partners and focus it remains hidden. Think of all that excel analysis you have made over the last years, when no action was taken, did you still store it? Could others use your analysis to build further or even stay well clear of it?

To do this in a coherent and systematic way takes experience and some sort of asset management tool at the very least! This fact makes me a strong promoter for not only good discovery tools, but as I want more business value and intelligence out of it, I need: Industrial strength asset management tools, well implemented and stalwartly managed to be able to climb towards the higher echelons of cleverness: Asset Intelligence.

About Christian Björkly-Nordström