From data to insights

1. From data to insights

One of the main reasons why data is so important is the added value of turning data into insights. But what does this mean exactly and why is it important? That is the topic of this video.

2. The DIKW pyramid

To better understand the value of data, we can use the Data-Information-Knowledge-Wisdom framework. Each step of this pyramid represents a new step we can take. The higher we go, the more value we can get from data, starting from raw data all the way up to perfect wisdom. In the following slides, we'll break down each step.

3. DIKW pyramid: Data

Raw data consists of loose observations or measurements. It does not have meaning yet. It is unorganized and unprocessed. For example, we can measure with a thermometer that it is 15 degrees Celsius, observe that there are dark clouds, and that some raindrops are falling. Right now, we don't know what these separate facts mean yet or how they connect with each other.

4. DIKW pyramid: Information

Information consists of raw data placed into context. This is typically done by organizing or aggregating data. In the weather example, we can organize our raw data by putting it in order: first the temperature dropped, and then it became cloudy and then the raindrops fell.

5. DIKW pyramid: Knowledge

In the knowledge step, we combine information to make connections, learn and gain meaning. This is typically done by detecting patterns, making generalizations or predictions. In the weather example, we can surmise the possible causal connection between temperature, clouds and raindrops, and use this to predict whether or not it is going to rain.

6. DIKW pyramid: Wisdom

Wisdom is applied knowledge or knowledge in action, as it allows us to act pro-actively. This is typically done by combining knowledge logically to determine the course of action. To come back to our weather example: with my accumulated knowledge I know when it is going to rain and so also know when to bring my umbrella.

7. The path to wisdom

Getting from data to wisdom involves several steps. In each step, we add something new. Going from data to information, context is added. Going from information to knowledge, meaning is added. However, the step from knowledge to wisdom is a big leap. The concept of wisdom implies a sort of perfect, complete knowledge. Which in reality, we can maybe approach but not quite reach.

8. The path to wisdom

In the weather example, if we look at temperature and clouds, is that enough to perfectly predict rain? Can we truly know all factors that come into play and measure them? And what if we want to predict rain tomorrow or next week?

9. Insights are the key

To solve this problem, we need a go-between. And this is where insights come into play. It can be considered a gold nugget, a kind of manifestation of wisdom that we can realistically achieve. And as we keep gathering insights, we can get one step closer to that perfect wisdom.

10. Insights are the key

For example, knowing about the effect of temperature and the formation of clouds might be enough to be reasonably certain a few hours beforehand about the possibility of rain and the need to bring an umbrella. Adding in the insight that humidity is a better predictor than temperature, we can improve the accuracy of our predictions. Next, using insights on atmospheric pressure areas, we can start predicting rain a few days ahead and keep that umbrella close. Eventually, we might get enough pieces of the puzzle to start seeing the full picture.

11. Let's practice!

Now that you know how to walk the path from data to insights, it is time to put your knowledge into practice.

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