How Does Data Science Help Define NFT Value?

A person using data science to value NFTs

The NFT trend is still thriving, with new tokens and collections popping up on a regular basis. How can big data help collectors and businesses not to get lost amidst the great variety of tokens and identify the real gems?

The NFT industry continues to develop, promising to grow from a market size of $11.3 billion in 2022 to $231 billion in 2030. Being a next-gen medium of status and wealth, NFTs attract lots of creators, collectors, and businesses. Among the enterprises taking a keen interest in NFTs are such giants as Adidas, Lamborghini, and Coca-Cola, along with some renowned art galleries — Almine Rech Gallery, Pace Gallery, and Unit London.

But how do collectors and investors navigate their way through the jungle of NFTs and understand which ones will make valuable additions to their collection and which are just colorful pictures or even scams?

In this article we will explain how data science can be leveraged by NFT investors, businesses, analytic platforms and collectors to define the value of tokens and determine potential risks.

What are NFTs?

Non-fungible tokens (NFTs) are blockchain-based digital tokens that represent unique items from real and virtual worlds such as images or documents. Each token is one of a kind and can’t be substituted for any other NFT.

NFTs grabbed a lot of attention in 2021, and since then they have been finding applications in a range of spheres, from arts to real estate. Anyone can buy, sell, showcase and collect NFTs.

Some of these tokens are very expensive, with sums reaching millions of dollars. Yet the majority are sold for less than $200, meaning that you can join the community regardless of how tight your budget is.

Who decides the value of an NFT?

NFT prices can be veeeery different even within a collection. The price is determined by a variety of factors, in particular rarity, utility, interoperability, social proof, and ownership history. Let’s check how exactly these parameters affect the NFT value:

  • Rarity — the rarer the NFT, the higher the price is. Examples of rare tokens include one-of-a-kind artworks from renowned artists, celebrities’ NFTs, and hard-to-find game items.
  • Utility — to rise in value, an NFT should have some additional application or meaning. It can be the ownership of something physical such as real estate or precious metals, or something less tangible like social status. The latter utility is best represented by The Bored Apes collection, the owners of which become the members of an elite club alongside Mark Cuban, Eminem and Justin Bieber.
  • Interoperability — it is highly attractive if a token can be used across a variety of platforms. For example, if a game item is supported in different games, or a digital outfit can be worn on multiple platforms, the price goes up.
  • Social proof — expensive NFTs are usually backed by strong communities and famous names. One example is the NFT collection by Canadian singer Grimes that generated $6 million in revenue and was sold out twenty minutes after launch.
  • Ownership history — as NFTs are items of prestige, their value goes up if they have been owned by a celebrity.

What are the most common NFT risks?

A person aiming to steal NFT

Where there is money, there are frauds, and the NFT industry is no exception. NFT scams are quite common and the losses are very tangible, with both individuals and big platforms falling victims of fraudsters.

One of the recent cases is OpenSea’s loss of $1.7 million worth of NFTs. Even though the marketplace managed to return some of the stolen tokens, the incident caused great reputational damage.

Forewarned is forearmed, so let’s take a look at the most common NFT scams:

Rug pull

A rug pull is a fraudulent scheme with NFT developers advertising their project and raising investment only to then take the money and disappear.

One example of a rug pull NFT project is Frosties. It was an ice-cream-themed collection of 8,888 NFTs. Frosties had a big Discord community and the founders promised merch and raffles, and a special fund to ensure sustainable and stable growth of the collection.

However, once the collection was sold out gaining 335 ETH — just over a million dollars — Frosties’ creators scattered the funds from the sale to various wallets and deleted the project’s website and Discord channel.

Luckily, after a two-month investigation, the scammers were arrested and charged with conspiracy to commit fraud and money laundering.

Wash trading

Wash traders make the NFT look more valuable than it really is and whip up artificial excitement and interest. It works as follows: the owner of an NFT sells it for a large sum, but the buyer is either the owner themselves or their fellow scammers. This skews the price history, making the NFT look attractive and lucrative to other collectors.

In April 2022, Bloomberg reported that wash trading made up 95% of overall trade volume on LooksRare NFT marketplace. That’s about $18 billion.

Plagiarized NFTs

Sometimes people buy an NFT from a famous collection just to find out later that it is a copycat. In January, 2022, OpenSea discovered that 80% of tokens minted on their platform were plagiarized works and fake collections.

Scammers can copy the already existing NFTs and duplicate them on other blockchains or steal the artworks and turn them into NFTs without the creator’s permission. For example, after the death of Qinni, a popular digital artist, fraudsters started minting NFTs of her artwork. The NFTs were listed on the Twinci marketplace, which subsequently deleted them and permanently banned the account following multiple complaints.

These are just a few examples of fraudsters exploiting the NFT craze. Other schemes include airdrop scams, phishing, and fake customer support.

Want to know more about NFT marketplaces’ security issues? Read our article

What is data science?

A person analyzing charts and diagrams

Data science is a way of analyzing massive amounts of information to help businesses’ improve data management and extract valuable patterns and insights for both internal and external use. According to research, 44% of enterprises confirm they use data science for innovation.

However, data science application is not limited to business needs only. It is successfully used in healthcare, education, sports, government and more. For example, it is estimated that data science will reduce US healthcare expenses by $150 billion by 2026.

What are data science components?

Data science leverages different technologies such as AI, ML, cloud computing and text mining. How do they work?

Artificial intelligence

Artificial intelligence, or AI, is a technology that uses complex computer algorithms to mimic human intelligence. It can understand human speech, images, video and text, and can be “trained” to solve specific types of problems.

The main purpose behind AI is to help people manage complex data faster and more effectively.  It can do this by suggesting smart decisions in complicated situations. It is reported that 91.5% of leading businesses are investing in AI in 2022.

In data science, AI autonomously processes raw data in search of patterns, inefficiencies, and anything that can help enhance current processes.

Machine learning

Machine learning (ML) is a type of artificial intelligence that enables computer programs to make accurate predictions without being explicitly programmed to do so. ML algorithms are used for fraud detection, malware threat detection, recommendation engines, spam filtering and many other applications.

Data scientists build ML models that help analyze and automate massive chunks of data and make real-life predictions with no humans involved.

One example of the successful implementation of ML comes from Netflix, which manages to save $1 billion annually with its machine learning system that helps customize content suggestions.

Cloud computing

Data science relies on cloud services to store unlimited amounts of data and provide quick access to the desired information. According to statistics, 76% of organizations worldwide opt for multi-cloud storage, with at least one shared and one private cloud.

Top cloud platforms include Amazon Web Services, IDrive, Dropbox, Google Cloud, and Microsoft Azure.

Get acquainted with DocFlow — a document management system running on blockchain

Text mining

Text mining is the process of transforming unstructured text into a structured format to identify valuable patterns and actionable insights. It is used by data scientists to process different types of texts, which are categorized as structured (such as lists of names or addresses), unstructured (typically, social media posts or product reviews), and semi-structured (for example, JSON or HTML files).

What is the process of applying data science?

When leveraging data science, you should follow a protocol that usually involves several steps:

#1 Definition of aspects in question (e.g. customer behavior and financial processes)

#2 Data collection

#3 Data preparation for further analysis

#4 Processing data with different analytical models and approaches

#5 Results visualization (e.g. charts and reports)

#6 Setup of an action strategy based on gained insights

How is data science different from big data and data analytics? Here is our detailed comparison

How does data science estimate NFT value and help avoid scams?

A person estimating the price of an NFT

With hundreds of new NFTs minted and 3,200 tokens sold daily, it is impossible to track all the changes and activities going on in the industry. Yet, they directly influence prices of NFTs.

Data science simplifies the lives of collectors, businesses and NFT marketplaces by making it possible to track the fluctuations in price and demand of any token while providing a comprehensive risk audit. How is this achieved?

AI algorithms can keep track of all NFTs on the blockchain and analyze their trading history, including prices and owners, along with specific properties such as utility or distinctive traits.

Meanwhile, ML and text mining models are on guard for fraudulent activities like fakes or copycats. They look for symbol manipulations and image duplicates. These technologies can also detect wash trading by analyzing suspicious activities between wallets. In addition, data science can be used in proving NFT provenance and authenticity.

Data science can also make NFT value predictions based on tokens’ recent activity and floor price.

Evaluating NFTs with data science: CheckNFT and WatchDog use cases

CheckNFT is an intelligent platform that analyzes and compares NFTs to help creators, collectors, businesses and analytics agencies make well-grounded trade decisions. The solution collects data across different blockchains to make immediate updates on tokens and collections. Currently, there are 272k collections and 87m tokens on CheckNFT and the number is constantly growing.

The platform’s data intelligence and AI algorithms perform real-time monitoring of NFT history on blockchain and evaluation of token activity, thus generating valuable data on the tokens’ price, properties, history, royalties, and more.

CheckNFT users can make a side-by-side comparison of up to five NFTs and get clear visualization of the tokens’ differences.

To help NFT collectors avoid risks and scams, CheckNFT is equipped with a risk analysis module that indicates potential hazards on each token, ranging the risk degree by critical, middle, and minor. The module leverages the AI engine to detect suspicious activities, and uses ML models to detect scams and find copycats via image analysis. The platform also sends violation notifications to NFT artists and businesses, helping them to protect their collections.

There is also WatchDog — one of the CheckNFT ecosystem’s components. It is an AI-powered tool built specifically to watch for NFT duplicates, copycats and trademark infringements in order to save collectors and businesses from reputational and financial losses.


It can be complicated to define the value of NFTs, with different aspects such as rarity, utility, interoperability, social proof and ownership history greatly influencing the price. Even an experienced NFT collector can spend a considerable amount of time analyzing tokens so that they are 100% sure that it is worth the money or defining that it is a good time to put a token up for sale.

Fortunately, data science is here to help. Equipped with innovative technologies — AL, ML, cloud computing and text mining — data science extracts the newest information about NFTs and swiftly processes it, presenting valuable insights for businesses and individuals.

What to enhance your business processes with data science? PixelPlex big data consultants will show you the best ways to optimize your business’s processes and drive up its value — all with the help of novel technologies.

Contact us and be sure your project is in good hands.


Valeria Serebryantseva


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