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Data Valuation Tools

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It is the means to give personal data the right value. Online systems make money with users’ personal data. It is thus fundamental to know what the economic value of each piece of information is, to let the user take informed decision on what to share, at what price.

It is the means to give personal data the right value. Online systems make money with users’ personal data. It is thus fundamental to know what the economic value of each piece of information is, to let the user take informed decision on what to share, at what price. The D-VT consists in a set of methodologies that will make the value of the data transparent. It will offer standard mechanisms to publish prices, complemented with machine learning approaches to extend the knowledge to other data and systems. This information will be stored in open repositories, so that PIMS can easily give the right value to data.

Market Perspective
The Data Valuation Tools from the market perspective (DVTMP) module developed in PIMCity will leverage some of the most popular existing online advertising platforms to estimate the value of hundreds to thousands of audiences. The DVTMP module aims to provide the monetary value of audiences traded on the main online advertising platforms. This will serve any PIM deciding to implement the DVTMP module to have a realistic estimation of audiences’ value to be traded. The design of the DVTMP pursues the following objectives:

1. Crawling data value of audiences from Facebook, Instagram, and LinkedIn
2. Process, clean, and curate the collected data
3. Store processed data
4. Provide access to the data through an API

Benefits
Nowadays, the auction mechanism is the most prevailing type between sellers and buyers in the data economy. Therefore, a good approximation of the actual value of the data can assist both parties (users and companies) through the transaction. Users usually have less
experience than the companies in marketing activities. Therefore they may underestimate or overestimate the value of their data. This module tells the users the actual value that marketing platforms create using their data. This module benefits the users by:

- Providing real-time value estimation of the data,
- Help the users to sell data at their actual price and avoid losing money by selling it underpriced,
- Providing the estimations simply sending an HTTP POST request to the server.
It also benefits companies by:
- Helping them to target relevant audiences and estimate the cost of their campaigns,
- Buy users’ data at a fair price and do not lose money by purchasing overpriced data

User Perspective
The objective of the Data Valuation Tools from an End-User Perspective (DVTUP) module is to provide estimated valuations of end-users’ data they are selling through the marketplace according to the value this data provides in performing the specific AI/ML task that the buyer wants this data for. This value is not necessarily related to volume nor is equitable for the users, but requires more complex calculations that must be adapted to each specific use case.
DVTUP implements a framework that allows data marketplaces to provide value-based valuations of data products they trade. In particular, DVTUP will provide tools for the TE to:

1. Provide buyers with a hint of how valuable a piece of data is for a certain type of model or even for a specific task.
2. Calculate a fair breakdown of data transaction charges by seller, looking forward to rewarding each user proportionally to the value that each piece of data from different sellers brings to the buyer for a specific task.
In the first case, the output will be the expected accuracy the buyer will get from a dataset if purchased from the marketplace. In the second case, the output will estimate the percentage of a transaction value that corresponds to each seller, and a log of data and results obtained to justify rewards paid to different sellers.

Benefits
DVTUP overcomes some key challenges that are undermining data markets nowadays. In particular:

1. It allows data buyers to try data before they buy (TBYB) and know their value for their specific task beforehand. This feature dramatically enhances their experience and improves the value provided by the data marketplace [2].
2. It allows data marketplaces to reward users in accordance to the value they bring to the specific transactions. Since the value of data is inherently combinatorial, data marketplaces and PIMS usually sell combinations of data from different users or sources to feed a certain AI/ML model. DVTUP ensures the payback to user is fair. This incentivize the provision of high-quality data.