INTRO

Despite the sheer quantities of data available on the Web, such information is not always easy to find โ†’ a new category of professionals emerged who took on the function of information intermediaries (for a fee).

Commonly, they process, sell, and re-sell data available on the Web.

Numerous new forms of marketplaces for data have emerged.

We have conducted a comprehensive survey and comparison of multiple data marketplaces and data vendors.

46 suppliers of data, conducted from April to July 2012 aim of identifying categories and dimensions of data marketplaces as well as vendors of data in order to build a taxonomy for data marketplaces.

researching the market and its developments

  • understand market dynamics
  • new research opportunities into the application of new technologies

methodology and approach

Definitions

focused on online web services

  • trading data,
  • raw data
  • data enrichment tools

data marketplace โ†’ platform on which anybody can upload and maintain data sets. data vendor โ†’ someone who has data and offers it to others, either for a given fee or free of charge. data enrichment services โ†’ that take input from the user and enhance it in some way, e.g., by analyzing or tagging it.

Approach

initial set based on [14], expanded to 12 boolean dimensions of categorized vendors

The facts about the data vendors were gathered by means of a Web search.

INSERT TABLE OF DIMENSIONS

Limitations

The information we used was taken directly from the website of each vendor.

The market of data vendors and data market places is highly active, i.e., new actors emerge and others disappear, and the market as such is growing rapidly. Therefore, it cannot be guaranteed that this study is fully exhaustive with regard to the number of vendors in the market.

we are aware of the fact that a certain amount of data is traded directly between (large) corporations or within a certain ecosystem (such as social networks) without the use of intermediaries.

Findings

objective - subjective dimensions

1 Type

used to classify vendors based on what their core product is.

brief description of each with TABLE

2 Time frame

captures the temporal context of the data. two categories:

  • Static/Factual: Data is valid and relevant for a long period of time and does not change abruptly, i.e., population numbers, geographical coordinates, etc.
  • Up To Date: Data is important shortly after its creation and loses its relevance quickly, 1. ยข., current stock prices, weather data, or social media entries.

TABLE, 20% serves both, many specialize

3 Domain

what the actual data is about.

any โ†’ vendors are not restricted and can incorporate arbitrary domains

many any โ†’ data market places, search engines, and customizable crawlers do indeed serve any domain, depending on what customers choose to upload or search for.

TABLE

4 Data Origin

where it comes from. describe briefly 6 types mainly internet authoritative sources

TABLE

5 Pricing model

business models basically, 4 main models describe briefly some overlap having combined models

TABLE

6 data access

which means end-users receive their data from vendors. 4 categories, describe briefly API flexible, less than 30% of API services have only API

TABLE

7 Data output

format of data reasonable set composed of , list, not described necessary i suppose? maybe very briefly

TABLE

8 Language

English dominant, website and data distinction in tables

TABLE

9 Target Audience

B2B fashion is the most logical application area of data vending.

The more general vendors target their offer at all audiences.

TABLE


subjective dimensions

10 Trustworthiness

depending on the origin of the data as well as on how it is processed.

types for each category, explained with examples

TABLE overlap because of multiple sources

11 Size of vendor

division startup โ†’ global player low startups, market not easy to enter

TABLE

12 Maturity

When there are already established vendors with mature projects, the space for new companies to enter the market is relatively small.

TABLE

Related work

Expand with differences โ†’ https://www.econstor.eu/bitstream/10419/94187/1/779859502.pdf

mi sa che espando con le differenze del 2014

[3] for trends (idk, meglio le differenze i suppose)