In considering data as an organisational resource it is important to realise that it is quite different from other resources. Data is obviously not a tangible resource like product components in a manufacturing process; you can’t touch it or hold it however it does have the advantage of not depleting through use. You can use data and reuse it as much as you like and it will still be there for you to use again. This makes it a very valuable asset within a modern organisation but it also means that organisations can easily slip into bad habits that take data for granted. They forget that they have to put effort into nurturing their data in order to get the best out of it. You can take, take, take but if you don’t give in return then the effect on data, and consequently your organisation, can be disastrous.
Any mature organisation that uses data will know that, data can simultaneously cost you money and save (or make) you money through-out it’s lifecycle. The enduring challenge is ensuring that you can overcome the associated costs and tip the balance in favour of the saving/making money side. Danette McGilvary states, in her book, that all stages of the data lifecycle have associated costs and by continuing the illustrative analogy of balancing a set of scales we can begin to show what factors in this lifecycle can affect the equilibrium.
On one side of the scales you have the costs associated with things like data storage, software/hardware costs, maintenance overheads and the cost of resources required for data capture. In order to counter-balance these costs we need to ensure that the other side of the scales hold enough weight and we can establish ways to actually reduce weight on the costs side.
As part of your balancing act you need to ensure that your data assets are appropriately managed throughout the data lifecycle. This will involve finding ways to reduce storage (think MDM and SCV), reducing the number of occasions that the obtaining of data is duplicated across the organisation and most importantly use and reuse the data more. A key tactic will be to unearth new stakeholders and new uses for data assets.
Conceptually it is easy to see how you can balance cost and savings but actually working out what the values on both sides are can be incredibly difficult. Tangible costs and savings are easy to quantify however it’s the unseen costs and savings that can often tip the scales one way or the other. For example, until now I haven’t even mentioned the quality of your data; we’ve assumed that the data is of sufficient quality to help tip the scale but what if you have quality problems?
As you’d expect poor data will not help you save money; it will in fact tip the scales the other way. In the same way that a defective component will impact the quality of the end product in a manufacturing process, poor data will impact outputs and cost you additional funds to correct. The more you reuse poor quality data, the more it costs you in the longer term. All those ‘shadow’ processes that have secretly crept into business areas in order to fix or compensate for bad data cost you and ultimately impact the actual value of your data assets. Your data has a potential value that can only be realised when the organisation is using and managing it in an optimal way.
This post obviously tries to very briefly cover a very small part of a very large subject that would easily fill a book (and indeed has filled many), but I thought it would be useful to start the topic and maybe revisit it with later posts. I’ll leave you with some basic concepts to think about:
- Good quality data saves or makes you money every time it is used (assuming appropriate use).
- Bad quality data costs you money every time it is used.
- Each stage in the data lifecycle costs money. Try to optimise and reduce duplication in each stage.
- Promote and increase the use and reuse of the same data across the organisation.
As always, thoughts and comments encouraged.