My Dam Broke - Revisiting Content Consumption
Dam Flood by OldOnliner is licensed under CC BY-NC-ND
I’ve previously talked about managing your content consumption using metaphors such as a dam, the flow of water, a reservoir, and a spillway. I highly recommend reading through if you are looking for some techniques to tame information overload. In case you don’t want to read the above article, the main point is to focus on high-quality sources and don’t consume everything.
Spoiler alert! Even though I wrote the above article, I have to revisit the topic as my metaphorical dam is broken. I’m finding myself facing the flood with 300+ articles in the reservoir. In this article, I will revisit content consumption management.
I took a look at my content consumption from a system’s perspective. There are four factors at play that determine the backlog of content to consume.
Improving the pace at which I can consume content is tricky as it depends a lot on the amount of free time I can devote to it. I decided to focus on the other three factors instead as a way to distil the content down to the most valuable.
The flow of content from sources making it to my dam
The flow of water was the analogy I used in my dam metaphor for the rate of content coming in. This is tunable and can be controlled. I had advocated highly for the use of newsletters as they are narrow in focus and are filtered for good calibre pieces.
I want to keep tabs on where the good pieces of content are coming from. The importance and value of consumed content will change over time based on projects and interests. This means that I can remove specific content sources if they are underdelivering in value.
This is something I have done organically (i.e., “I’ll unsubscribe from this newsletter as I don’t care about YYYY much anymore”), but I want to put more of an objective system in place. To do this, I want to track the volume and perceived value gain from my content sources. I’ll then routinely be able to revisit and see what is feeding into my content consumption pipeline and what is not.
The effectiveness of my dam (i.e., the filter)
My goal of consuming content is to extract information and turn that into knowledge, ultimately being able to use that to produce results. With that in mind, the following are three guiding principles I’ve decided to use before committing to consuming a piece of content:
Overall, I want to maximize the effectiveness and efficiency of knowledge extraction. The first two principles I’m carrying over from the previous system of content consumption. The third principle is new and should help improve the effectiveness of my dam.
Essentially I want the content to make it through the dam filter if
(content is long-term || content is relevant) && content is unique.
Either of the first two principles must be true for the content to be of any value. It is worth noting that having both being true is the best scenario – the information is relevant and long-term knowledge. The relevancy principle is important to have as I want to make sure that I’m not losing short-term gains on the projects and problems I’m working on.
An example of something that would be filtered out is an article on upgrading from Rails 5.x to 6.x. I wouldn’t consider this long-term knowledge as eventually, Rails will progress past these versions. There is no relevance for this article based on my current projects, and thus it should be filtered out. If afterwards I now have a project that would have benefited from this information, not all is lost as I would just seek it, embracing Just In Time learning.
Another example would be pieces of content around empathy in code reviews. This is great long-term knowledge, as well as relevant to my work. The problem is, I’ve already consumed something like this and have extracted and internalized the knowledge. This fails the third principle of being uniquely valuable, and thus I would filter these out. If at a later date, I’m doing a presentation or article on empathy in code reviews, I would employ Just In Time learning to find all relevant information.
The effective removal of content that is no longer valuable.
Right before consuming a piece of content from the reservoir, I’ll revisit the three principles. A piece of content may have lost relevancy, or is no longer going to provide unique value. This is a quick way of removing content.
With hopefully a smaller number of content sitting in the reservoir, this mechanism will be enough. In the event that the reservoir is overflowing, you can quickly sweep through everything and reevaluate. The key is to be ruthless, as important information will likely find its way back to you.
I’ve previously used a mixture of applications to store consumable content. The main application I have used for articles is Instapaper. The new system demands more metadata and so I’ve made the switch to entirely use Notion [Referral]. I’ll continue to use separate applications for podcasts and books, but they’ll likely have entries within Notion.
Everything is in a single database within Notion, these are the following fields.
These fields address the three principles within the Criteria field, as well as metadata on how to reduce the flow of content via the Origin and Value fields.
Within desktop usage, I make use of the Save to Notion extension as I can fill in more of the metadata from the browser. Within mobile usage, saving content takes another step as I have to go into Notion and add the metadata. For the most part, I try to make use of my desktop when adding content to the Notion database.
I’ll keep making changes and iterate on my content consumption system, hopefully making it better each time. The goal is still to turn pieces of content into information that I can use as knowledge.
The next thing I’m planning to tackle is the actual consumption part of the system and having a process for knowledge retention.