Working down from trends
How to work down from known topics to discover new insights and points of view.
What is the top-down way of working with foresight?
Top-down refers to a way of working where you start with a known topic or topics and work towards more detail.
This approach is often used for a trend management type of approach to foresight.
How to do top-down foresight in FIBRES
FIBRES can be used to support many kinds of foresight workflows, including the top-down model.
In this article, you'll learn one way to apply the FIBRES data model and available features to this type of foresight process.
Is the top-down model is right for you?
There is no right or wrong when it comes to creating your own foresight process. The top-down model is a linear simplification of one way of doing foresight, but not the only way.
Most likely, your foresight process will be a combination of several parallel models or ways of working. See also our article for the bottom-up model for more inspiration.
1. Create a summary or a radar
In the top-down model, you start with the big picture. First, outline the topic on a broad level. Your starting point could be something quite vast, like the future of mobility.
Your first step could be configuring a radar, where you'll start plotting the trends you're already aware of. Alternatively, you could set up a summary where you will start summarizing the topic.
2. Create top-level findings
Next, outline some subtopics. For example, if you're working with the future of mobility, you might be interested in more concrete topics like MaaS, driverless cars, and sustainable transport.
In FIBRES, it's recommended to create an individual finding for each individual subtopic. Suitable finding types for this purpose are Megatrend, Trend, and Signal cluster.
If you're working with a radar, you could populate your radar with these findings. This will help you become aware of what you already know and what you might still be missing.
If you're working with a summary, remember to link your findings with the summary. When you turn on summary layers in the Network view, you'll see the summary connected to the findings.
3. Write descriptions for your findings
Now that you have a radar or a summary full of new findings, it's time to refine their descriptions. Write in the finding descriptions what you already know. You can also apply additional metadata by using classifications.
4. Use AI picks to find more
At this point, you'll find that AI picks is already offering you suggestions on the topic. AI picks recommends completely new content based on your description and linkages to other findings.
AI picks are selected individually for each summary and finding. Remember to check the suggestions both inside your summary and the individual findings to add interesting new inputs.
5. Refine your understanding based on collected signals
Refine your top-level understanding based on the new information that you collect. Your top-level findings will become your scouting tools, surfacing new stuff when they come along and helping you notice things you might otherwise be missing.
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