The world revolves more and more around effectively using data and this development is - to a certain extent - replacing traditional research protocols. It is difficult to predict the speed of this process, but as an innovation manager I can already see the first signs in the market.
When I compare the DIKW model - which represents the structural and functional relationship between data, information, knowledge and wisdom - to our traditional investigative methods, I predict that traditional surveying will be forced into the background by knowledge. And that we therefore run the risk of losing a large part of our market potential. Consequently, I feel that we need to develop this knowledge ourselves and look in a targeted manner for the specific form of wisdom with which we can shape the soil surveying of the future.
Various descriptions of the DIKW model can be found on the Internet: I like the description I found on this (Dutch) website: ithappens.nu:
Data, information, knowledge and wisdom are inextricably linked. Information cannot simply exist on its own (Rowly 2007) because it is data linked by relational connections. Data can be seen as raw facts that exist in one form or another, and that may or may not be usable. Data has no inherent significance and only takes on meaning and becomes information when a person establishes a certain relationship with it. Knowledge involves applying data and information in a way that is useful and allows action. Knowledge can also be seen as a deterministic process. Wisdom is a form of self-development and the opposite of a deterministic process. It uses outcomes from the previous three levels to understand the underlying principles. Wisdom is a unique human process and cannot be replaced by computer systems.
The following example clarifies the underlying relationships of the four levels:
In a traditional soil survey, we assess local pollution, use of the location, calamities, etc. based on historic and current data. We use the total data set to determine what is needed to achieve the intended goal in the research strategy, obviously on the basis of established protocols. In the past, this historical data used to be kept physically available for reference by municipalities and site visits also used to take place. At present, we already exchange a great deal of data digitally and we can view documents remotely.
Nowadays, the details we collect (permits, soil surveys, location photographs) consist of items of data that we link to each other (data converted into information), with which we, as expert soil consultants, can identify patterns in order to decide the strategy for the soil survey (the knowledge component). For that strategy, we draw up a survey plan based on different soil-threatening activities in accordance with current protocols, carry out various activities again (drilling and installing monitoring wells) and occasionally use existing monitoring wells. The knowledge that we use for this is knowledge about what information we need to get from a specific activity, and that is something we do repeatedly. At the moment, we do this largely in digital form and the person who possesses this knowledge is still central.
This pattern recognition activity is something that computers do better than humans. The greatest challenge is to teach the computer to recognise the right patterns. In view of the exponential increase in data that is currently being generated, we humans are finding it increasingly difficult to combine all that information in a single strategy. Our brains are no longer able to process this volume of data. So we need assistance in automatically recognising this type of data, information and knowledge. Our strength as human beings is that we are capable of working out why certain principles are important and that we can teach others to do the same. For example, we are currently teaching cars to recognise road signs as part of the development work for the self-driving car. So why aren’t we doing the same thing in the soil surveying field?
In theory, this is not particularly innovative; we teach new employees how to conduct soil surveys, what data is needed, how they can recognise it, which items of data need to be linked together to generate useful information, and how they can recognise these patterns. The step to true digital processing may seem difficult, but is theoretically small.
Nearly everybody is convinced that having data ultimately leads to knowledge. But ultimately, it is the degree of added wisdom that will determine how and how fast this change will take place. The sooner we embrace this development, the sooner we will be able to become part of this change.
The first step in this digital transformation is to handle data properly and convert that data into information. But the subsequent steps also offer huge potential.