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Using the DIKW model to get actionable insights from your data

The DIKW model or DIKW pyramid is an often-used method, with roots in knowledge management, to explain the ways we move from data (the ‘D’) to information (I), knowledge (K) and wisdom (W) with a component of actions and decisions.

This is a key component of digital transformation. And with the emergence of IoT and AI/ML the decisions/actions can also be (semi-)autonomous (although everything depends on the nature and purpose of the data).

DIKW is a model to look at various ways of extracting insights and value from all sorts of data and it is often depicted as a hierarchical model as seen below:

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The DIKW process of transforming data into wisdom can be seen from two different angles: context and understanding.

In context, one moves from a phase of gathering parts (data), the connection of parts (information), formation of a whole (knowledge) and joining of wholes (wisdom). 

In understanding, the DIKW hierarchy can also be seen as a process starting with researching, absorbing, doing, interacting, and reflecting.

These steps fluidly connect the DIKW hierarchy, no step is linked to a particular DIKW step. The DIKW hierarchy can also be regarded in terms of time. The data, information and knowledge levels are based on the past while the final step – wisdom – looks toward the future.

Data

Logging, records, measurements, etc. are all data. And since data is a mass and includes many things, it needs to be contextualised to make sense: data alone does not provide meaningful results.

Information

Let’s consider that one million subscribers use the SMS service of a telecom operator in the United Kingdom. Alone, the one million tells us nothing other than a volume. Why? When? How? These are the questions that still need to be answered if that data set is to become useful.

For example, if one million SMS service users are analysed, it could be found eight hundred thousand users using this service are in the 15-25 age range with 80% using the service between seven pm to eleven pm in the evening.

And if you take it one step further and also work out that 90% of these users use SMS service when there is no internet connection on their cell phone, things start to get interesting. The analysed data becomes information.

Knowledge

The third level in the DIKW hierarchy is Knowledge. The Knowledge step aims to answer the How? question. Specific measures are identified and the information gained in the previous step is used to answer questions based on these measures, such as ‘How do teenagers use SMS services?’

Wisdom

The fourth and last step in the DIKW hierarchy is Wisdom. At the Wisdom stage, the knowledge found in the previous step is applied and implemented. Wisdom is the top-level reached in the DIKW hierarchy and answers the ‘Why?’ question.

If we consider our example scenario, an example of wisdom gained might be that 95% of the SMS service users use this service for short communication when they do not wait for a response from the other party. For instance, when a user wants to say goodnight to another party, or if someone arrives at the meeting point and seeks to notify another person, etc.

As you see, starting from the usage data of the SMS service from the beginning, we processed the data in the Information and Knowledge stages, and we found at the Wisdom step that most of the SMS users use this service for short communication with the other party. This is an example of how an IT service provider used the DIKW hierarchy as a model to convert data into actionable results based on wisdom.

 

Insight and actionable steps from data

To gather useful insights from data, you need clear reports displayed in an easy to understand format.

Once your data has been collated and sorted into a clear pattern (such as a graph), you’ll need to put this data into context and explain what’s happening. Look at a graph and see 5 good days where you get 200 form submissions and then 2 poor days where you see below 20 form submissions. The context you need is to find out which days the bad days are - are these weekends? Mid-week? What correlation is there between product usage and these days? Add context to your data visualisation before you can interpret the data and turn it into insight or wisdom.

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Storytelling & data

When presenting data, you need to keep in mind that a bunch of graphs tossed onto a dashboard do not necessarily make a good story. Data visualisation and the way you layout your data will help you weave together a story that helps tell a meaningful story.

Using data to tell a story is about sharing the reason why results occurred and weaving in context to the information you are sharing. It isn't about telling a narrative that suits your objective but rather adding context to your results in such a way that the information you’ve gathered provides actionable insights and meaningfully drives your business forward.

Poor results should be presented in context and with the hope of finding a way to either mitigate this in future or to understand what happened. Using the DIKW model, you could share the results, what has happened at the time or what factors affected this result and then share insights regarding future actions that you could act on due to these insights.

 

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