
Nansen’s advanced labeling techniques transform blockchain data into actionable insights for traders and investors.
Key takeaways
- Transforming raw blockchain transactions into insights involves multiple data processing layers.
- Nansen excels in the attribution layer, crucial for understanding on-chain flows.
- The attribution layer for labeling addresses includes both algorithmic and human efforts.
- Quality assurance is critical in maintaining trust in data labeling processes.
- Every label in the database is backed by compiled evidence to ensure accuracy.
- Public information on blockchains can be used to label addresses, with removal rights for individuals.
- Blockchains are inherently public and transparent, making information immutable.
- Labeling blockchain addresses involves studying behaviors and deterministic smart contract events.
- Data harmonization across different chains is essential for deriving meaningful insights.
- Address labeling has evolved to include agentic and algorithmic methods.
- The integrity of blockchain data labeling relies heavily on quality assurance.
- Blockchain technology’s transparency and immutability have significant privacy implications.
- Nansen’s labeling infrastructure provides real-time insights for investors and traders.
- Understanding blockchain transactions requires a combination of inference and flow analysis.
- The complexity of on-chain analytics highlights the need for advanced data processing techniques.
Guest intro
Alex Svanevik is the CEO and Co-founder of Nansen, a blockchain analytics platform pioneering AI-driven agentic trading. He previously served as Chief Data Scientist at CoinFi, where he built a crypto database and back-tested trading signals. Svanevik holds an MSc in Artificial Intelligence from the University of Edinburgh.
The process of transforming blockchain data
- Raw blockchain data must be extracted into a more efficient storage and compute layer.
The most basic thing you have to solve is to get the raw on chain data into a more convenient storage and compute layer
— Alex Svanevik
- Data harmonization across different chains is crucial for effective analytics.
You need to kind of harmonize all that data
— Alex Svanevik
- The attribution layer involves labeling addresses to provide context to blockchain transactions.
The third part… is the attribution layer where you label the addresses
— Alex Svanevik
- Nansen excels in the attribution layer, critical for understanding on-chain flows.
We probably are the best in the world at the attribution layer
— Alex Svanevik
- Transforming raw transactions into insights requires multiple data processing layers.
You need to get the raw entredata into something that is more convenient for running analytical queries
— Alex Svanevik
- The process involves both algorithmic and human efforts in address labeling.
We’ve kind of evolved our approach to labeling addresses over the years
— Alex Svanevik
The importance of data harmonization and attribution
- Harmonizing data across chains is essential for deriving meaningful insights.
The second layer is you have to kind of harmonize the data cross chains
— Alex Svanevik
- Address labeling has evolved to include both agentic and algorithmic methods.
We also do a lot of algorithmic work that is not agentic but is still super important
— Alex Svanevik
- The attribution layer is crucial for interpreting blockchain transactions.
The third part which we i would say probably are the best in the world at is the attribution layer
— Alex Svanevik
- Nansen’s expertise in attribution helps investors make informed decisions.
- The complexity of on-chain analytics highlights the need for advanced data processing techniques.
- Understanding blockchain transactions requires a combination of inference and flow analysis.
You can literally send some money to binance and see where the flows go
— Alex Svanevik
- Labeling wallets involves studying transaction behaviors and deterministic events.
Labeling blockchain addresses
- The process involves studying behaviors and deterministic smart contract events.
You have to study the behaviors of different entities
— Alex Svanevik
- Smart contracts provide deterministic events for labeling.
Some behaviors are deterministic because they’re smart contract driven
— Alex Svanevik
- Labeling wallets requires inference and transaction flow analysis.
You can literally send some money to binance and see where the flows go
— Alex Svanevik
- Quality assurance ensures trust in data labeling processes.
We do focus a lot on quality assurance
— Alex Svanevik
- Every label in the database is backed by compiled evidence.
You cannot add a label to the database unless you’ve compiled the evidence for it
— Alex Svanevik
- Public information is used for labeling, with removal rights for individuals.
We rely on public information right information that’s in the public domain
— Alex Svanevik
Quality assurance in data labeling
- Quality assurance is critical for maintaining trust in data labeling.
It’s very it’s it takes a long time to gain trust but it’s very easy to lose trust
— Alex Svanevik
- Every label is backed by evidence to ensure accuracy.
You cannot add a label to the database unless you’ve compiled the evidence for it
— Alex Svanevik
- The integrity of blockchain data labeling relies heavily on quality assurance.
- Ensuring high precision in data processing is essential for credibility.
- The rigorous approach to data labeling enhances reliability and reduces errors.
- Nansen’s commitment to quality assurance builds trust with users.
- Accurate labeling is crucial for interpreting blockchain transactions.
- The process of labeling involves both algorithmic and human efforts.
- Quality assurance practices are vital for maintaining data integrity.
Public information and privacy on blockchains
- Public information on blockchains can be used to label addresses.
We rely on public information right information that’s in the public domain
— Alex Svanevik
- Individuals have the right to request the removal of labels.
If you come to us and you say hey I actually want that label removed as an individual you can do that
— Alex Svanevik
- Blockchains are public and transparent by default.
Look this is actually on the blockchain like this is not it’s like immutable
— Alex Svanevik
- Information on blockchains is immutable and cannot be erased.
Even if we remove it from our database the ens name is always gonna be etched into that address
— Alex Svanevik
- The transparency of blockchain technology has significant privacy implications.
- Understanding the principles of blockchain data treatment is crucial.
- The distinction between individual and corporate identities is important in labeling practices.
- Ethical considerations are integral to operational policies regarding blockchain data.
- Public and transparent nature of blockchains affects data permanence.

Nansen’s advanced labeling techniques transform blockchain data into actionable insights for traders and investors.
Key takeaways
- Transforming raw blockchain transactions into insights involves multiple data processing layers.
- Nansen excels in the attribution layer, crucial for understanding on-chain flows.
- The attribution layer for labeling addresses includes both algorithmic and human efforts.
- Quality assurance is critical in maintaining trust in data labeling processes.
- Every label in the database is backed by compiled evidence to ensure accuracy.
- Public information on blockchains can be used to label addresses, with removal rights for individuals.
- Blockchains are inherently public and transparent, making information immutable.
- Labeling blockchain addresses involves studying behaviors and deterministic smart contract events.
- Data harmonization across different chains is essential for deriving meaningful insights.
- Address labeling has evolved to include agentic and algorithmic methods.
- The integrity of blockchain data labeling relies heavily on quality assurance.
- Blockchain technology’s transparency and immutability have significant privacy implications.
- Nansen’s labeling infrastructure provides real-time insights for investors and traders.
- Understanding blockchain transactions requires a combination of inference and flow analysis.
- The complexity of on-chain analytics highlights the need for advanced data processing techniques.
Guest intro
Alex Svanevik is the CEO and Co-founder of Nansen, a blockchain analytics platform pioneering AI-driven agentic trading. He previously served as Chief Data Scientist at CoinFi, where he built a crypto database and back-tested trading signals. Svanevik holds an MSc in Artificial Intelligence from the University of Edinburgh.
The process of transforming blockchain data
- Raw blockchain data must be extracted into a more efficient storage and compute layer.
The most basic thing you have to solve is to get the raw on chain data into a more convenient storage and compute layer
— Alex Svanevik
- Data harmonization across different chains is crucial for effective analytics.
You need to kind of harmonize all that data
— Alex Svanevik
- The attribution layer involves labeling addresses to provide context to blockchain transactions.
The third part… is the attribution layer where you label the addresses
— Alex Svanevik
- Nansen excels in the attribution layer, critical for understanding on-chain flows.
We probably are the best in the world at the attribution layer
— Alex Svanevik
- Transforming raw transactions into insights requires multiple data processing layers.
You need to get the raw entredata into something that is more convenient for running analytical queries
— Alex Svanevik
- The process involves both algorithmic and human efforts in address labeling.
We’ve kind of evolved our approach to labeling addresses over the years
— Alex Svanevik
The importance of data harmonization and attribution
- Harmonizing data across chains is essential for deriving meaningful insights.
The second layer is you have to kind of harmonize the data cross chains
— Alex Svanevik
- Address labeling has evolved to include both agentic and algorithmic methods.
We also do a lot of algorithmic work that is not agentic but is still super important
— Alex Svanevik
- The attribution layer is crucial for interpreting blockchain transactions.
The third part which we i would say probably are the best in the world at is the attribution layer
— Alex Svanevik
- Nansen’s expertise in attribution helps investors make informed decisions.
- The complexity of on-chain analytics highlights the need for advanced data processing techniques.
- Understanding blockchain transactions requires a combination of inference and flow analysis.
You can literally send some money to binance and see where the flows go
— Alex Svanevik
- Labeling wallets involves studying transaction behaviors and deterministic events.
Labeling blockchain addresses
- The process involves studying behaviors and deterministic smart contract events.
You have to study the behaviors of different entities
— Alex Svanevik
- Smart contracts provide deterministic events for labeling.
Some behaviors are deterministic because they’re smart contract driven
— Alex Svanevik
- Labeling wallets requires inference and transaction flow analysis.
You can literally send some money to binance and see where the flows go
— Alex Svanevik
- Quality assurance ensures trust in data labeling processes.
We do focus a lot on quality assurance
— Alex Svanevik
- Every label in the database is backed by compiled evidence.
You cannot add a label to the database unless you’ve compiled the evidence for it
— Alex Svanevik
- Public information is used for labeling, with removal rights for individuals.
We rely on public information right information that’s in the public domain
— Alex Svanevik
Quality assurance in data labeling
- Quality assurance is critical for maintaining trust in data labeling.
It’s very it’s it takes a long time to gain trust but it’s very easy to lose trust
— Alex Svanevik
- Every label is backed by evidence to ensure accuracy.
You cannot add a label to the database unless you’ve compiled the evidence for it
— Alex Svanevik
- The integrity of blockchain data labeling relies heavily on quality assurance.
- Ensuring high precision in data processing is essential for credibility.
- The rigorous approach to data labeling enhances reliability and reduces errors.
- Nansen’s commitment to quality assurance builds trust with users.
- Accurate labeling is crucial for interpreting blockchain transactions.
- The process of labeling involves both algorithmic and human efforts.
- Quality assurance practices are vital for maintaining data integrity.
Public information and privacy on blockchains
- Public information on blockchains can be used to label addresses.
We rely on public information right information that’s in the public domain
— Alex Svanevik
- Individuals have the right to request the removal of labels.
If you come to us and you say hey I actually want that label removed as an individual you can do that
— Alex Svanevik
- Blockchains are public and transparent by default.
Look this is actually on the blockchain like this is not it’s like immutable
— Alex Svanevik
- Information on blockchains is immutable and cannot be erased.
Even if we remove it from our database the ens name is always gonna be etched into that address
— Alex Svanevik
- The transparency of blockchain technology has significant privacy implications.
- Understanding the principles of blockchain data treatment is crucial.
- The distinction between individual and corporate identities is important in labeling practices.
- Ethical considerations are integral to operational policies regarding blockchain data.
- Public and transparent nature of blockchains affects data permanence.
Loading more articles…
You’ve reached the end
Add us on Google
`;
}
function createMobileArticle(article) {
const displayDate = getDisplayDate(article);
const editorSlug = article.editor ? article.editor.toLowerCase().replace(/\s+/g, ‘-‘) : ”;
const captionHtml = article.imageCaption ? `
${article.imageCaption}
` : ”;
const authorHtml = article.isPressRelease ? ” : `
`;
return `
${captionHtml}
${article.subheadline ? `
${article.subheadline}
` : ”}
${createSocialShare()}
${authorHtml}
${article.content}
${article.isPressRelease ? ” : article.isSponsored ? `
` : `
`}
`;
}
function createDesktopArticle(article, sidebarAdHtml) {
const editorSlug = article.editor ? article.editor.toLowerCase().replace(/\s+/g, ‘-‘) : ”;
const displayDate = getDisplayDate(article);
const captionHtml = article.imageCaption ? `
${article.imageCaption}
` : ”;
const categoriesHtml = article.categories.map((cat, i) => {
const separator = i < article.categories.length – 1 ? ‘|‘ : ”;
return `${cat}${separator}`;
}).join(”);
const desktopAuthorHtml = article.isPressRelease ? ” : `
`;
return `
${categoriesHtml}
${article.subheadline}
` : ”}
${desktopAuthorHtml}
${createSocialShare()}
${captionHtml}
${article.isPressRelease ? ” : article.isSponsored ? `
` : `
`}
`;
}
function loadMoreArticles() {
if (isLoading || !hasMore) return;
isLoading = true;
loadingText.classList.remove(‘hidden’);
// Build form data for AJAX request
const formData = new FormData();
formData.append(‘action’, ‘cb_lovable_load_more’);
formData.append(‘current_post_id’, lastLoadedPostId);
formData.append(‘primary_cat_id’, primaryCatId);
formData.append(‘before_date’, lastLoadedDate);
formData.append(‘loaded_ids’, loadedPostIds.join(‘,’));
fetch(ajaxUrl, {
method: ‘POST’,
body: formData
})
.then(response => response.json())
.then(data => {
isLoading = false;
loadingText.classList.add(‘hidden’);
if (data.success && data.has_more && data.article) {
const article = data.article;
const sidebarAdHtml = data.sidebar_ad_html || ”;
// Check for duplicates
if (loadedPostIds.includes(article.id)) {
console.log(‘Duplicate article detected, skipping:’, article.id);
// Update pagination vars and try again
lastLoadedDate = article.publishDate;
loadMoreArticles();
return;
}
// Add to mobile container
mobileContainer.insertAdjacentHTML(‘beforeend’, createMobileArticle(article));
// Add to desktop container with fresh ad HTML
desktopContainer.insertAdjacentHTML(‘beforeend’, createDesktopArticle(article, sidebarAdHtml));
// Update tracking variables
loadedPostIds.push(article.id);
lastLoadedPostId = article.id;
lastLoadedDate = article.publishDate;
// Execute any inline scripts in the new content (for ads)
const newArticle = desktopContainer.querySelector(`article[data-article-id=”${article.id}”]`);
if (newArticle) {
const scripts = newArticle.querySelectorAll(‘script’);
scripts.forEach(script => {
const newScript = document.createElement(‘script’);
if (script.src) {
newScript.src = script.src;
} else {
newScript.textContent = script.textContent;
}
document.body.appendChild(newScript);
});
}
// Trigger Ad Inserter if available
if (typeof ai_check_and_insert_block === ‘function’) {
ai_check_and_insert_block();
}
// Trigger Google Publisher Tag refresh if available
if (typeof googletag !== ‘undefined’ && googletag.pubads) {
googletag.cmd.push(function() {
googletag.pubads().refresh();
});
}
} else if (data.success && !data.has_more) {
hasMore = false;
endText.classList.remove(‘hidden’);
} else if (!data.success) {
console.error(‘AJAX error:’, data.error);
hasMore = false;
endText.textContent=”Error loading more articles”;
endText.classList.remove(‘hidden’);
}
})
.catch(error => {
console.error(‘Fetch error:’, error);
isLoading = false;
loadingText.classList.add(‘hidden’);
hasMore = false;
endText.textContent=”Error loading more articles”;
endText.classList.remove(‘hidden’);
});
}
// Set up IntersectionObserver
const observer = new IntersectionObserver(function(entries) {
if (entries[0].isIntersecting) {
loadMoreArticles();
}
}, { threshold: 0.1 });
observer.observe(loadingTrigger);
})();
