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Do you treat your data as an Asset?

When I ask the question how data driven is your organisation, I invariably get back a positive answer because of course everybody uses data in their work. However, if I ask the question do you treat data as an asset, the answers are much more subdued and vague. Observing the difference in the two answers is very interesting (if not concerning) as every organisation appreciates the importance of data and its impact on all aspects of the business. Yet, very few really appreciate data as an asset, even though this appreciation is at the core of being data driven as an organisation. Given this difficulty, I have outlined a few simple questions that will help you examine your (and your organisation’s) conceptualisation of data as an asset, which also point to a number of simple changes you can make.


  1. What financial measures do you use for organisations data? The key characteristic of any asset is that it produces financial value. Applying the same logic to data, it is essential that financial measures are utilised for data. For some, the idea of putting a value or financial measure on something as intangible as data sounds weird, but if you don’t have any financial measures in place, how can you even know if your data has value or not. Simple measures such as the cost of data should be recorded by either (a) calculating the cost of acquiring, integrating, analysing and delivering a particular data set, or (b) calculating the cost of replacing a data set if you suffered a full non-recoverable data loss. Other measures such as: market value, depreciation, appreciation, and revenue generation should also be included.
  2. What type of language do you use to have data conversations? A good indicator of whether an organisation treats its data as an asset is the language they use to describe and discuss data. If an organisation has a mature apprehension of data as an asset they will have a very concise language that is aligned to that of an asset. For instance, many of the terms described above (e.g. appreciate, depreciation, value, cost, investment) would be very closely tied to conversations around data. Other terms and concepts such as: life-cycles, renewal, governance and ownership will also be common within data conversations.
  3. Have you clearly separated IT assets from data assets? The tangibility and commercial nature of IT have made it easy for people to view IT as an asset. However, due to the fact that IT is the container and infrastructure on which data flows, organisations have a lot of difficulty in separating the two types of assets. This leads to a number of issues, including: the mistake of making technology the central focus of projects to the detriment of any data priorities. For instance, emphasis gets placed more on the type of technology to be utilised rather than the data requirements for projects.
  4. How do you maintain the condition of your data? The perquisite of answering this, is first knowing the condition of your data. The majority of organisations will admit that their data is not all of good quality but fail to actually quantity how good or bad it is. Key to understanding good quality data is noting that while data is not depletable (the amount of data does not diminish with use), more data is not necessarily better and it is also perishable if not kept up-to-date and accurate. Without practices in place to ensure your data is of good quality you will never get the most out of data as an asset.


Finally, it is worth remembering that behind every good asset are strong data savvy managers that continually ask the questions above, fully understand the value of data, and instinctively know how to facilitate a data driven organisation.


Tadgh Nagle UCC

Tadhg Nagle is joint Programme Director of the IMI Diploma in Data Business and IMI MSc in Data Business.

He is also a lecturer and researcher in Information Systems at University College Cork.

With a background in financial services his expertise is in strategic innovation and emerging and disruptive technologies. _____________________________________

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