Since its launch, Outset Media Index (OMI) was introduced as a point of reference for media analysis. Pull it up and the platform immediately displays scores, rankings, filters, and a tidy list of outlets ready for analysis.
At that point you see the finished product. What you don’t see is the work behind it: the digging, the systems built from scratch, the testing and manual checks that make it work.
To get a sense of this groundwork, this piece reflects on a recent interview with Maximilian Fondé, senior media analyst at OMI, who has been part of the team since day one.
The numbers worth getting were the hardest to get
To be what it is today, OMI was born from years of scattered notes and records that the team had built working with media, from traffic and pricing to turnaround, regional reach and who was likely to repost who. Bringing it together into one system was the easy part. The hardest part was managing data that these documents never really had.
As anyone would do, the team would have started with well-known and basically must-have tools. Similarweb has provided the clearest and most comprehensive analysis of how much traffic an outlet generates, where it comes from, and how readers behave once they arrive on the site. Moz added analytical depth and domain authority also came into play, but according to Fondé, mainly because the industry still relies on it as a standalone actionable signal reflexively.
Each tool gave part of the picture. However, what the team wanted most was missing. These tools could evaluate a media outlet but would not determine whether an article published there had the potential to find readers, and that is precisely the most important factor to determine.
That’s why one thing that came up repeatedly throughout the conversation was how often OMI found himself questioning assumptions that are typically taken for granted in media analysis. A good example is visibility itself.
Maximilien argued that publication and discovery are often treated as the same thing when they are not.
“The mere existence of an article on a media outlet does not mean that users will discover it organically“, he said.
This realization ultimately shaped some of OMI’s internal infrastructure, including a custom reissue analyzer that tracks how content actually moves after it goes live.
For example, you can publish an article on a respected media outlet and see it reach almost no one. Here, a traffic figure tells you that the platform is busy, not that your article has surfaced inside, and that many well-placed articles sink without a trace. This gap, between an existing history and a discovered history, was the one that Maximilien continually encountered.
A single article rarely ends up on the website that publishes it first. In crypto, it can appear on a dozen smaller sites in a day, and each collection brings in readers the original publisher never counted on. Consider these second-hand films as background and you may miss most of the people who saw the story. IMO reflects these collections through its reprint and aggregator metrics.
Humans still go through every line
Why not let the software take over all the load? It’s the natural question to ask about any data product in 2026, but the OMI team refuses to take the easy route.
Maximilian continues to read the spreadsheets line by line, looking for the area that breaks a rule that everyone signed on last month. When he encounters a problem, he immediately looks for the answer himself.
Automation then takes its turn, and mainly to detect what a person could have passed.
“The judgment and conclusions of our process are formulated exclusively by people“, that’s how he said it, and the whole system is built around this order: people decide, software assists them.
Scale forces the same type of call. Stack a large outlet next to a small independent, the big one wins on raw numbers every time, whether they deserve it or not. For this comparison to be fair, someone interprets under what conditions what matters before the rankings are published.
Crypto takes each of these problems up a notch. Many websites worth following keep quiet about their numbers and share too little data to judge them, so they get rejected even when they deliver their results.
When numbers exist, they remain estimates, and an estimate won’t tell you whether the audience behind an outlet is real or padded to look the part. The big analytics providers aren’t much help either, as none of them see crypto as its own beat. This all turns into a fuzzy category, and anyone comparing crypto stocks is working from a framework that no one drew for them.
This is why crypto media couldn’t just borrow analysis designed for finance, they also needed something drawn around their own shape.
What holds everything together
According to Maximilian, the more complete a final product is, the less it has been finished by a machine, at least in this niche. The most important data had to be constructed by hand because no tool sold or provided it, and the judgments that put the rankings together are still made by a person because no formula makes them good.
What reaches your screen is a clean score and a tidy list. What’s left behind is the slow part, finding stories that were published but never found, and the decision that keeps a small publication from being flattened by a giant. This buried work is the only reason the visible half has value and credibility.
