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DataBroker DAO






Sale Completed

Concept

It is probably best to offer an analogy to explain the concept behind the Databroker DAO model. As we know, companies are required by law to periodically report on their revenues. These reports are published individually by each company, but entire industries – as testified to by the existence of the likes of Bloomberg, Thomson Reuters, FactSet and others – have emerged from simply aggregating these individual reports into large data hubs that service all kinds of other industries in turn: research houses at investment banks, quantitative traders at hedge funds, benchmark providers etc., all of whom require this aggregated company data to run their own businesses and consequently pay huge sums for it. 

Databroker is seeking to do something similar. But, instead of the raw data being sourced from individual companies that publish reports, its raw data will come from IoT (Internet of Things) devices. But Databroker is not the equivalent of Bloomberg or Thomson Reuters in our analogy. Rather, it is seeking to be the platform that facilitates the rise of new Bloomberg-type data aggregators who, it is hoped, will emerge to exploit and monetise the new reams of data that the world’s exponentially growing number of IoT devices will serve up. 

 

 

Here’s an example: today, car insurance companies issue policies to their customers whose pricing depends – in part – on forecast estimates of vehicle usage. The more you use your vehicle, the more your insurance policy will cost. And those estimates are generally based on one simple metric – the car’s mileage from the previous year.   

However, that metric is flawed because it assumes that two people who drive a car an equal distance over the course of the year have the same risk profile. But a more nuanced analysis would show that drivers who drive fifty thousand kilometres per year on the motorway are way less likely to be involved in an accident that those who drive fifty thousand kilometres per year in dense urban neighbourhoods. 

As cars become increasingly digitised, however, they will increasingly find themselves fitted with GPS-type devices that can provide this more nuanced picture of vehicle usage. Databroker DAO’s ambition, then, is to create an infrastructure that will allow car manufacturer’s to monetise this vehicle usage data by providing a set of tools which will allow others – such as insurance companies – to pay for access to the kind of data that will allow for more granular analysis of what an insurance policy should cost. 

This is one small example. In reality, the opportunities in the upcoming IoT age for data aggregation are going to be limitless – and the team at Databroker DAO is hoping to position itself as a leader in the field. 

White-paper

At just under forty pages, the white-paper covers a lot of ground and does so without going into too much excruciating detail in relation to the technical implementation itself. The document outlines four core categories of stake-holders in the future Databroker DAO ecosystem. By understanding each of these, you go along way to understanding the general business model. These are:

Data Providers: these are simply the owners of the IoT devices and they will be rewarded for allowing their data to be shared to the network. For doing so, they will receive 80% of all monies paid (in DXT token) for that data. 

Data Processors: these are the platforms that aggregate the data from individual IoT devices into large, rich data sets which they then sell on to Data Buyers. 

Data Buyers: these are the end users of the data – like the insurance company in our analogy above – who pay for access to the data. 

Gateway Operators: in order for any IoT device to be connected to the internet, there needs to be a wireless network operator. These are the mobile phone and satellite companies who operate the GSM, SigFox and LORA-type networks which serve up the interconnected world that we live in and who will need to climb onboard the Databroker DAO project to marry up Data Providers, Data Processors and Data Buyers to each other. In order to do so, 10% of monies paid out for data by Data Buyers will be distributed back to the Gateway Operators as an incentive to participate. 

The overall aim of the project, then, is to create an open platform for seamless data sharing for all kinds of data. It should be noted that Databroker DAO are not planning to store the actual raw data on the blockchain itself – there is simply far too much data for that. Rather, Data Providers will simply store the data in the usual data silos but which will be opened to the outside world through a combination of Databroker-built dAPI’s that Data Providers will need to integrate into their existing systems.

Access to the data will then be managed by a set of blockchain-hosted smart-contracts that also manage payments between parties in real-time. 

Road Map

The road map is simple and straight-forward, and has been lifted directly from the website – leaving out references to the sale dates which can be consulted under the Details section of this same page. 

Databroker DAO Roadmap

For those who are keen to take a closer interest in the project’s general progress, the project team offers an open-access webinar on a periodical basis which allows participants or potential participants in the token sale to come forward with their own questions. Details on the webinar can be found here

Tokenomics

Nothing too complex here. Once the platform is up and running, proceeds from data sales will be distributed as follows: 80% to the raw data providers (owners of the IoT devices), 10% to the Gateway operators and 10% to the Databroker DAO platform itself. 

The number of tokens to be minted sits at 225 million, 30% of which will be locked up until 2021. This is being done presumably to give value to early-issued tokens but also to provide liquidity to the future Databroker DAO ecosystem which, if things go as well as the project team hopes, will have experienced widespread adoption.

Whilst the number of tokens in circulation may appear high, it makes perfect sense in the context of both the scope and ambition of this project: to facilitate data aggregation for every imaginable kind of data set that will be served up the IoT economy. 

Team

Databroker DAO team

Based out of both Dubai and Belgium, the headline member of the team is Patrick Byrne, founder and CEO of Overstock.com 

The remainder of the team includes a dedicated marketer, lead project manager, content manager, several engineers and two former company directors. All figures listed come with LinkedIn profiles which, for the most part, demonstrate relevant industry experience embedded in rich networks. 

Marketing

The project’s presence on social media is modest but solid. ICOs tend to employ specialised marketing agencies which use various ploys – usually Twitter and Facebook pre-packaged follower farms – to build up the appearance of large followings. With this project, however, we appear to be observing organic growth across all of the major social network platforms. 

Whatever the case may be, this does not appear to be an ICO that will struggle to get its message out there when it has already attracted the attention of the likes of Patrick Byrne, CEO of Overstock, who is quoted within the white-paper itself and who serves, in any case, as an advisor to the project.

The project’s headline team also lists a dedicated marketing and communications manager, and there are signs of a professional marketing strategy at play – as evidenced by the hosting of media press-kit and inclusion of other media resources on the website.

It is difficult to make a more generalised judgement, however, as neither the white-paper nor the website appear to make any reference to budget allocations for marketing, or for any other aspect of the project. 

Conclusion

On the face of it, this is a solid proposition. Its success seems likely to hinge on the project team’s ability to sign partnerships with the kinds of network operators who can offer significant reach into the IoT jungle. However, with a business model that offers an incentive mechanism for network operators to climb aboard, that should make the task easier – although it is likely that at least some operators will be sceptical about the value that comes from being paid in cryptocurrency. 

There are other platforms out there currently competing for dominance in the IoT economy – not least Streamr and IOTA, both of which are also hoping to make their own breakthroughs sooner rather than later. However, our own suspicion is that the eventual and inevitable IoT incentivised data-sharing economy which is coming will be able to accommodate a host of players. And given the solid proposition that the project team has laid out here, it appears to stand as much chance as any other in making its mark in this newly emerging economy.