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 own (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 organisational 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 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 any value or not. Simple measures such as the cost of data should be recorded by either: (a) calculating the cost of a data set at each stage of the Information Supply Chain (acquisition, integration, analysis, delivery and governance), (b) calculating the cost of replacing a data set if you suffered a full non-recoverable data loss, or (c) calculating the direct benefit/return you get from data. In addition, other measures such as: market value, depreciation, appreciation, and revenue generation should also be included in a portfolio of data asset measures as your data capability matures.
  2. What type of language do you use for data conversations? I’m not suggesting you should use French or Italian when talking about data, but a good indicator of whether an organisation treats its data as an asset is the language and more specifically the terminology 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 (eg appreciate, depreciation, value, cost, investment) would be very closely tied to conversations around data. Other terms and concepts such as: life-cycles, renewal, ownership, and stages in the Information Supply Chain will also be common within data conversations. The clarity in communication created by such terms also make the development of a common understanding around data easy to attain and is a very good starting point in nurturing a data driven culture.
  3. Have you clearly separated IT assets from data assets? The tangibility and commercial nature of IT have made it easier for people to view IT as an asset. In addition, given the close linked nature of IT and data, organisations have a lot of difficulty in separating the two as assets. As IT is the container/infrastructure on which data flows and is stored (akin to water and pipes), organisations tend to over simplify the conceptualisation of both assets by branding them both as IT. This leads to the issue of putting the cart before the horse and in many cases making technology the central focus of projects to the detriment of any data priorities. When this happens more emphasis is placed on the novelty of the technology to be utilised, without any real thought on its impact to the quality of data. This oversimplification has also meant that IT people are usually put in charge of business data; of which they often know very little about.
  4. How do you maintain the condition of your data? The perquisite of answering this is first knowing the actual condition of your data? The majority of organisations will admit that their data is not all of good quality but fail to actually quantify how good or bad it is. Key to understanding good quality data is 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. In fact, a very good rule of thumb (by Dr. Tom Redman) demonstrates that doing a task with bad data will cost you ten times more than if you did the same task with good data. Moreover, you can now imagine the negative impact of an organisational mindset of “the more data the better”, when quality is never maintained or measured.


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