Gain A Data-Driven Competitive Edge With Quadrant Protocol (Use Case)

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A professional daily fantasy player, Lincoln, on DraftKings is looking to gain a competitive edge over his competition by accessing newly available data streams and products.

Lincoln is in the top 5% of all competitors in the PGA golf leaderboards and has been analyzing data from multiple free sources that he customizes in advanced metrics to help predict the outcomes of tournaments.

Many of Lincoln's competitors have begun using additional data sources and have started finding connections to statistics not normally tied to golf.

Lincoln begins looking for sources of data on the newest decentralized platforms but only finds raw data streams that offer little value to him.

More Than Just A Data Exchange

A data scientist friend from college introduces Lincoln to Quadrant Protocol, a unique blockchain based product that allows data consumers to purchase end-user ready data products created from provable authentic sources.

The true value of Quadrant Protocol is not raw data but instead provides data that has been linked and analyzed by Elons (data scientists, data analysts, etc). This extra layer of value is what separates Quadrant from the competition.

On previous sites, Lincolns searches led him to raw data about players, golf courses, and matchups. When using Quadrant a new universe of data products was found.

Lincoln found that data scientists were already creating linkages between PGA golfers from a PGA data stream and from independently owned IoT weather stations located on or near golf courses. The data product allows Lincoln to analyze how weather affects the performance of certain players.

This Mega-Constellation, as it's called in the Quadrant Protocol, can be combined with even more datasets and provide an even greater level of value. For example, Elons may find links between this data and news feeds or Vegas odds.

How To Get The Data?

Since Lincoln has found a very valuable source of data he would like to stream it into his computer. To do this he purchases the stream using QUAD tokens.

These tokens are the internal currency of the Quadrant ecosystem and are attained by exchanging with eQUAD tokens (ERC20) on the protocols gateway.

The datasets can be purchased for a single use, as a subscription, and can be downloaded or streamed live depending on the feed.

The data that Lincoln is purchasing can be delivered from either centralized or decentralized servers depending on the provider.

Who Gets Paid For The Data?

Quadrant Protocol is revolutionizing the way that data providers, aggregators, and analysts are paid for their data.

In Lincolns example, it is possible that the IoT weather devices are owned by hundreds of individuals and providing data to Quadrant. These data providers are referred to as Nurseries and the data they provide is hash stamped onto the blockchain to provide proof of authenticity and provenance.

The hundreds of data streams are aggregated by Pioneers and formed into Constellations which are analyzed by data scientists, analysts, and companies which are called Elons.

Elons take the raw data included in Constellations and find linkages with other Constellations. These linkages are then analyzed and provided to the data consumer in a ready to use fashion.

When the data consumer purchases the feed the Quadrant Protocol smart contract disperses the appropriate payment to all participants in the data stream from the hundreds of Nurseries all the way to the Elons and masternode Guardians.

Everyone Benefits From Quadrant Protocol

This is a simplified example of a use case using Quadrant Protocol but it shows how the ecosystem adds value to data products and also rewards the participants fairly.

Quadrant Protocol has the ability to become the data protocol that all other data projects are built upon. With a built-in partner DataStreamX, the team can continue to test and deploy their blockchain product with real-world data streams.

For More Information

📅Token Sale Dates: June 26th, 2018
📈Token Ticker: eQuad

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