Daniel Antal, CFA | Automated Data Observatories

Digital Music Observatory on the MaMA Convention 2021

Thu, 14 Oct 2021 11:00:00 +0200

Currently more than half of the global music sales are made by autonomous AI systems owned by Google, Apple, or Spotify. These data monopolies are getting rich, because they reap the profit from music businesses with an average employee count of 1.8 Europe. European music businesses are easy to exploit with armies of data engineers and data scientists because they do not have a single data scientist or even an IT function.

Artists in the UK had a difficulty explaining in Westminster how they are losing out in streaming– so we have created a streaming price index, like the Dow Jones, if you like, that explains the economic factors of the devaluation of music in the last 5 years in 20 countries. (See our report.)
Music organizations in Slovakia and Hungary were frustrated that their politicians and journalists believed music to be taxpayer funded, so we showed with data that they contribute more proportionally to the national budget than car manufacturers, the darling of local politicians (See our reports in Hungary (recast several times) and in Slovakia.)
We successfully challenged with data restaurant associations, hotel chains, telecom corporations and broadcasters who wanted to bring music prices down in court and via lobbying.

The music industry has envied the television and film industry which has a single go-to-point for data when it needs them, the European Audiovisual Observatory. It started lobbying for a publicly financed music observatory. But we did not wait. The music industry has a tragic track record of failed centralized international data projects. We built Reprex out of a 12-country, decentralized music project. We learned how to utilize hidden, but already existing data and research funds well, and how to manage the data governance among the poisonous conflicts of interests between rich and poor countries, authors vs producers, producer’s vs performers.

Our Digital Music Observatory is not theoretical, it is practical, because it is built around real-life court cases, damage claims, lobbying and PR arguments.
Our Digital Music Observatory is comprehensive – it contains more than a thousand indicators from all European countries. We have enough data to test the biases of the Spotify or the YouTube algorithm – you would be surprised what the data tells us.
It has data available much sooner, in much higher quality and in a more practical format than in the Audiovisual one.

Presentation Slides

You can see the presentation slides here.

Crunchconf: Open Data, New Gold Without the Rush

Fri, 08 Oct 2021 10:10:00 +0200

Every year, the EU announces that billions and billions of data are now “open” again, but this is not gold. At least not in the form of nicely minted gold coins, but in gold dust and nuggets found in the muddy banks of chilly rivers. There is no rush for it, because panning out its value requires a lot of hours of hard work. Our goal is to automate this work to make open data usable at scale, even in trustworthy AI solutions.

Summary

In his presentation, Daniel compared the current state of open data (including governmental open data and scientific open data) to a thrift store. You can often find bargains, or historical data that would be impossible to source from data vendors, but on a strictly as-is basis, without a catalogue, service, or guarantee. Therefore, working with open data requires a careful reprocessing, validation, and in many cases, frequent re-validation. Open data is often over-estimated: it is never a finished product, often it cannot even be downloaded, therefore it requires further investment to make it valuable. However, because most open data arrives from the governmental sector, you can tap into information sources where no market alternative exists. Open data in some cases may be a cheaper substitute to market vendors, but often it is an exclusive source of information that do not have any market vendors.

Sisyphus was punished by being forced to roll an immense boulder up a hill only for it to roll down every time it neared the top, repeating this action for eternity. This is the price that project managers and analysts pay for the inadequate documentation of their data assets.

The practices related to the exploitation of open data are not only relevant in an open data context: these are good data ingestion and procurement practices for any third party data, and in large organizations, for any cross-departmental data. (See the blogpost: The Data Sisyphus.)
Case Study: Belgian Drought/Flood Risk Awareness, Financial Capacity & Hydrology a complex integration of various open data sources.

In the second part of the presentation, Daniel talked about our modern data observatory concept. We have reviewed about 80 functioning and already defunct international data collection programs. Data observatories, like Copernicus’ Observatory, are permanent infrastructure to record various domain-specific data, such as alternative fuel information, information on homelessness, or on the European music business. In our assessment, most of the EU, OECD, UNESCO recognized or endorsed observatories use obsolete technology and do not rely on the new achievements of data science. Reprex, our start-up offers an open source, open data based alternative solution to build largely automated data observatories. We believe that human judgement is needed in data curation, but processing, documentation and validation is best done by computers.

Case Study: Reprocessing geographical information with administrative boundary changes

At last, he presented a few development directions with our open-source software, mentioning our work withing the rOpenGov community. This part of the presentation was originally meant to open the way for a half-day open data workshop, but due to the current pandemic situation, the physical part of the conference and the workshops were not held.

The presentation largely included the topics of our Data & Lyrics blogpost: Open Data—The New Gold Without the Rush

Presentation Slides

See the presentation slides here.

Reprex introduction in IVIR

Tue, 02 Feb 2021 10:10:00 +0100

IViRtual 9 April 2021

Product/Market Fit Validation in Yes!Delft

Fri, 25 Sep 2020 15:31:39 +0200

We would like to validate our product market/fit in two segments, business/policy research and scientific research, with a supporting role given to data journalism. Because we want to follow a bootstrapping strategy, we must focus on those clients where we find the highest value proposition, which is of course easier said than done. We see much interest in our offering from other continents, therefore we truly welcome the opportunity that we can do this on a truly global business canvas in one of the worlds’ top five incubators, the number 2 university-backed incubator in the world, second to none in Europe, in the Yes!Delft AI+Blockchain Validation Lab.

In Europe hundreds of thousands of microenterprises, such as record labels, video producers or book publishers are facing data and AI giants like Google’s YouTube, Apple Music, Spotify, Netflix or Amazon. If the recommendation engines of these giants do not recommend their songs, films or books, then their investments are doomed to fail, because about half of the global sales are driven by AI algorithms. When they make a claim for the missing money, they will immediately find themselves in a dispute with gigabytes of data that they can only handle with a data scientist, even though they do not even have an IT professional or an HR professional to make the hire.

An awful lot of money, creativity and real values are at stake, and we want to be on the creator’s side, their technician’s side, their manager’s side when they want to get a fair share from the pie and they want to help these industry leader to make the pie grow.

The UNESCO and the EU have been promoting as an organizational solution the fragmentation problem with the so-called data observatories that are pooling the business, policy, and scientific research needs of various domains, like music. This is an idea that we really like, and we believe that our research automation solutions can help these observatories to grow faster as ecosystems, create better quality and more timely data and research products and a far lower cost.

We define ourselves as a reproducible research company inspired by the philosophy of open collaboration, based on open-source software and open data. We want to explore various revenue models around these ideas.

We are not committed to open source licensing if more permissive licensing policies provide us with better opportunities.
We would like to explore various data-as-service models, because we do not want to be locked into the position of cheap open data vendors.
We want to deploy AI applications that really help earning money in these sectors with playlisting, recommendation engines, forecasting applications, or royalty valuations, because our open collaboration approach brings up enough data sooner to than its alternatives, because it manages inherent conflicts of interests, fragmentation, and decentralization better than hierarchical solutions.

Timeline

In January CEEMID reached its peak: we introduced a 12-country reproducible research project made with only freelancers in Brussels, presented as best use case of evidence-based policy design.
In February Daniel visited the Yes!Delft Co-Lab to find out who would be the best co-founder to re-launch CEEMID as an enterprise.
In April we started to release our data as open data for validation.
One month ago we started-up.
Then we launched the music.dataobservatory.eu project.
A few other data observatories.

Bonus:

Palato in the Hague, where we took our selfie and had an absolutely amazing dinner after the pitch. Check them out!