The flipside of processes is data. In recent years, the emphasis has swung away from the former and towards the latter, with the emergence of buzzwords (buzzphrases?) such as Big Data, but the reality is that the two are symbiotic and it doesn't make a lot of sense to try and do something with one unless you also give consideration to the other.
In the process world, we now have a fairly stable set of standards, techniques, and tools. The data world, however, is not there yet – in fact, many people are not even sure what data is, let alone how it relates to the processes that create, manipulate and use it. So let's a take a look at the key aspects of data, and see how they relate to the specific focus of this Column, human processes.
Data is usually considered as the foundation of a pyramid with multiple layers, as in the common formulation Data-Information-Knowledge-Wisdom (https://en.wikipedia.org/wiki/DIKW_pyramid). However, not only is the “knowledge component of DIKW generally agreed to be an elusive concept which is difficult to define” but, to me at least, the Wisdom component is poorly named since anecdotal concepts of wisdom often do not relate to specific facts but rather to a skill in using any facts.
So here I propose a more elaborate model that replaces the Knowledge and Wisdom elements of DIKW with easily understood concepts that relate directly to facts. Not only does my version, DIADEM, make explicit the connection to human processes, but it has the further advantage of being pronounceable 🙂 ￼
The elements of DIADEM are described below, with examples given in italics beneath each element by reference to my current project Town Digital Hub (http://bit.ly/tdh-psycap). All statistics are imaginary, and the example is chosen deliberately as light hearted so please don't take it too seriously. No offence is intended to any expert hoofers out there!
- Data – discrete, objective facts or observations, which are unorganized and unprocessed and therefore have no meaning or value because of lack of context and interpretation.
37 people have reviewed a local exercise class, with the average rating being 3 stars out of 5
- Information – organized or structured data, which has been processed in such a way that the information now has relevance for a specific purpose or context, and is therefore meaningful, valuable, useful and relevant.
The average rating from men of the exercise class is 2 stars, and from women 4 stars. Statistics from the provider of the class show that men attend an average of 2 sessions before dropping out and women 12. Other fitness classes have approximately equal ratings and persistence for men and women.
- Answers – Identification of, and responses to, specific questions of contextual importance.
How does this exercise class differ from others in the area?
It focuses on movement to music rather than flexibility or strength.
- Deductions – Conclusions drawn from answers.
It may be advisable for the providers of the class to (a) clarify in promotional material that the emphasis is on movement to music, so as to lower the dropout rate (b) target the class directly at women, so as to focus on the currently most successful demographic.
- Effects – Identification of actions that would make effective change.
There are cardiovascular health benefits from vigorous sustained exercise, for which movement to music is ideal, especially for people unable to take exercise outdoors or without access to specialized equipment. So there may be benefits to providing such an exercise class in a form that more men would enjoy.
- More – The feedback loop that tells service providers what additional data they should start collecting, and encourages people to share more personal data.
There are various possible sources of the poor ratings from men. For instance, some men may be embarrassed about what they feel to be their poor coordination, especially in front of women. There may also be issues related to choices of music and styles of movement. To determine whether it would be worth starting a men only movement class, men interested in doing more physical exercise could be surveyed via the website to identify their preferences, and asked if they mind this data being used to develop a new local service.
The DIADEM model not only clarifies how data differs from the uses to which it is put, but also explains exactly how to go about doing so – i.e., the human processes that are required to collect data and do something useful with it.