Authored by Daniel Hickmore, VP of Health & Life Sciences @ Arkivum
In my previous post, I outlined the four key classic economic principles and that incorrect assumptions associated with them for long term data management could actually be costing your organisation money.
The four economic principles we are discussing:
- The demand for long term digital and data life cycle management is a derived demand.
- Digital materials are perceived to be depreciable durable assets.
- Digital assets are often non-rive in consumption and create a free-rider potential.
- Long term data management is temporarily dynamic and path dependent.
In this article, I will focus on the first economic factor of ‘demand for long-term digital and data life cycle management is a derived demand’. Here we will explore why it may be argued that it is an incorrect assumption, and why emphasis on this when presenting a business case for long term data management is so important.
Data is of direct value, not derived
My argument is that data is of direct value, it is not derived, and the value of data can be calculated.
By definition, derived demand is solely related to the demand placed on a good or service for its ability to acquire or produce another good or service. In these instances, the demand for a raw material is directly tied to the demand for products that require the raw material to be produced.
An early example of derived demand was the pick and shovel strategy, which was developed in response to correlating market forces. During the gold rush, the demand for gold prompted prospectors to search for gold. These prospectors needed picks and shovels, as well as other supplies, to mine for gold. It is arguable that, on average, those who were in the business of selling supplies to these prospectors fared better during the gold rush than the prospectors did. The demand for picks and shovels was derived, to a large degree, from the demand for gold at that time.
There are some strange contradictions for data. I would argue that insight and evidence in the form of data is the purpose of pharmaceutical R&D (Research & Development) and data underpins the credibility of the end product post licensing and acceptance. It is the cornerstone of the pharmaceutical industry. For many institutions who form the feeder for the pharmaceutical industry and perform much discovery, data is the end product.
The principles behind derived demand work in both directions. If the demand for some good decreases, the demand for the goods required to produce the item will also decrease. This is not the case for many stages of the pharmaceutical industry. For example, data demand may remain constant even if the demand for the end product declines.
Producing data is expensive. Long term management of data can also be expensive and require significant resource, but whilst the pharmaceutical industry appreciates the amount of resource required to produce the data, it is still surprising how much effort it takes to manage it. There is an extensive list of resources required to deliver long-term data management and integrity, including (but not limited to) data selection and acquisition, meta data creation, validation, storage, encryption, record management, access control and so on, not forgetting legal, compliance and information governance.
When calculating the investment parameters for long term data management, all these tools, resources and time costs need to be calculated.
When we lose our phone, the loss of the data is noticed: immediately. In the pharmaceutical industry, data demand is direct to all the business functions. Data itself is a manufactured product. Our direct experience from the loss of data or access to data is nothing compared to the impact on a complex pharmaceutical, research or biotechnology organisation.
Why good quality, credible data is important
The quality of access to credible data can have a direct impact on:
- Organisational survival
- It may result in a death of a patient
Good quality, credible data can also:
- Be part of the company competitive strategy
- Be part of the IP portfolio and direct value of the company
- Reduce project, study and R&D portfolio risk
- Improve innovation and sharing
In summary, data underpins both negative and positive challenges for the pharmaceutical industry. Data can be seen to be an objective in its own right and therefore is a cornerstone to the industry’s success. All of the above factors can be explored as investment parameters when considering a long-term data management project.