Your Data Sucks.

The Supremacy of “On Chain” Data in Crypto

Hey Horde, here is a salty post by EZ, one of our founders. TLDR: On Chain Data isn’t as reliable as you might think. You need more than etherscan to watch for scams and rug pulls.

If you survey the CT or youtube space, you will see crypto data being used everywhere. Inflows/Outflows, BTC held on exchanges, etc. Most traders use data all the time for their analysis. This is good.

It is common to hear about the on-chain metrics regarding any and every part of crypto activity, floor prices of NFTs for example. This is good.

Increasingly, sleuths and internet detectives like @coffeezilla and @fatman use the chain in order to root out scams, and suspicious activity. This is very good.

Data Good

We love all this. In fact, because the available data for blockchains is all public, anyone with an internet connection can do this kind of detective work and make informed decisions. We love the work of twitter detectives, and do-gooders who want to make DeFi and crypto safe.

In fact, that’s kind of our whole thing. Make crypto SAFU again?

Data is helpful in that it provides transparency of certain events on the chain. This transparency is a key feature the crypto ethos. This public information paradigm also allows for community and socially driven protection measures. The world is watching, so this could offer a a slight deterrent to shady behavior (but, as we all know, it doesn’t).

Good Data Gone Bad

The devil is in the details of course. Data only tells you so much. And unfortunately, on chain data only can account for one part of a story.

We are creating stories/narratives from on chain data that may or may not be supported by the fact patterns outside.

It used to be the case that fact and fiction were clearly separated. We rely on facts to display a data pattern that leads to understanding events, but those data points don’t provide us the actual understanding we need-for that, we require context, supporting data, etc.

And all of that requires time, a precious resource.

With the advent of social media, this focus on fact-finding can unfortunately be turned into ghost-hunting, as the sheer volume of information can overwhelm our ability to discern the truth, and as a result, narratives and counter-narratives proliferate on social media-sorting out the details in this environment can be tricky.

This is why RugZombie is thankful for data intelligence, crypto-security conscious twitter, and detective work. We need these efforts in the crypto world.

But with great data, comes great responsibility.

Fact vs. Fiction: A Human Story

RugZombie is quite slow to call something a rug pull. Our community may be jonesing for a GRAVE to bury their dead tokens, but we try our best to be careful, and to be clear. We are not infallible guides by any means, but we pride ourselves on being a humble, community driven community that is on a mission to #cleanupdefi and be honest and forthright about our approach.

One important feature in assessing on chain data is to separate fact from fiction. Let’s imagine a scenario:

Susie tells Bob that she really hates Rick’s new haircut. Rick overhears the conversation and decides to confront Susie on her obvious hatred of him as a human being. He simply can’t believe Susie would say that! Fuming, Rick tells Susie how he has always known how she has hated him ever since the company barbecue where Susie brought guacamole even though she knew Rick was allergic, this is just another example of her behavior and makes him feel unsafe in the workplace…

While this very exaggerated scenario would make a great cue to an improv scene, it’s sadly not very far from what happens when humans try to put data together into a reliable fact pattern. But let’s examine the facts and the assumptions here:

Does Susie really hate Rick?

We actually don’t have evidence from this story that Susie hates Rick. The most that we can say is that Susie said she hated Rick’s haircut. We don’t even know if this is true, we only know she said it. And she made no comment as to Rick as a person, only his haircut.

What assumptions did Rick make?

Rick did what many of us do…he made assumptions not completely based on the facts, but did a bit of his own work to “color” in the lines. He added context and history, and even conflated Susie’s avocado blunder…likely based on presuppositions and biases of some sort that he had.

In a situation like this, having more information will inform the story and may in fact be necessary in this case. What if Susie actually has a crush on Rick, and was expressing how she hated his new haircut because she actually loved his old one? What is Susie was simply saying she hated Rick’s haircut because it reminded her of someone from her past that she was not eager to call to memory? What if Susie was just lying to impress Bob and in fact Bob is the one who hates Rick?

Here’s the point: we can rush to judgement with very little data, when the data may not tell the whole story and may in fact tell a very different story than we are narrating.

A Recent Crypto Example

Even recently, we have seen events that display the unreliability of on-chain data alone. Optimism, a large L2 protocol had users suspecting a hack based on some suspicious on chain data. The token price flash crashed 10% even though the team clarified later on that this was a measured and plan movement of funds by the team.

We can argue that the Optimism team needs to be more transparent, but crypto twitter could also use a deep breath every now and then to step back and ask the question, “do I have all the facts here?” or “am I writing a story that is supported by the data?”

I think if we stop and reflect for a bit of time, we will see that we need more than tracking wallets on the block explorer. Let’s keep sleuthing, and stay vigilant to keep people safe, but maybe with a dose of humility.

What to Do With On Chain Data

1. Gather the facts. Like, more than 1. Following your favorite crypto-twitter detective is fine. But gather supporting evidence.
2. Ask yourself, “Do I have the context?” A wallet moved a suspicious amount of $USDT? Are they going to dump it on the market? Are they buying $BTC? You don’t know. You can speculate, you can prepare, but this is not the same as bonafide insight.
3. Verify, Verify, Verify. On Chain data is what it is. But humans are fallible. Verify your insights with others, make sure you know or understand how to read blockchain transactions.
4. Write Out Your Bias. We all have them. Why do you want “X” to be true or “Y” to be false? What motivations might be guiding you that you are unwilling to admit? Simply writing these out will help you from rushing to judgement too quickly.