Why We Need Data Unions to Support the Data Economy
Shiv Malik, CEO of Pool Foundation dey talk on Data Unions and the value wey them go bring to the Data Economy
For the twelfth episode of Voices of the Data Economy, we get conversation with Shiv Malik, CEO for Pool Foundation and former Head of Growth for Streamr where he take evangelize about a new decentralized data economy. During this discussion, he tell us how Data Unions dey work, hin real-life use cases, and legislative regulations wey support the Data Union model. Here na edited excerpts from the podcast.
Note: The episode dey recorded on April 9, 2021 — when Shiv dey working with Streamr.
Wetin be Data Unions, and how dem dey work?
As individuals, we all dey produce information in the form of data, and only a few people dey harvest and profit from am. No way dey for ordinary people wey dey produce the value to retain any of that value themselves. Hence, Data Union na way of pooling data between various parties and monetizing am as collective.
However, you go still need an organization in the middle because this model go involve to reach out to data buyers. You go need to pick up a phone and speak to people — and machines no fit do that yet. Lots of people don flesh this out academically but no one don do am practically. When you try practice am, some really big stumbling blocks dey.
A Data Union framework dey provide a way to bundle a user’s real-time data together with others’ and distribute a share of the revenue when person pay to access am. On its own, person data no hold much value, but when e dey combined in a data union, it fit aggregate into an attractive product for buyers to extract insights. This na crowdselling, and get the potential to generate unique data sets by to incentivize individuals to trade previously unavailable data.
Data Unions as a revenue source to IoTs
Our smartphones na powerful devices wey dey collect information through internal sensors. All you go do na to create app wey dey collect specific data set — and then you fit imagine sey monetary incentive for people wey go do this dey.
Why we get only one map application (Google) or maybe two — and them dey owned by the same company? The reason for this na because them get monopoly on our location and we fit create that monopoly by to share our data freely — or rather we no get choice. “I go like other options — but viable ones. If you take the raw data sets away from them (devices), anyone fit build applications on top of the raw datasets. That na the world we want to live in,” Shiv talk so.
The problems with data sets and structures
To expand on the inherent issues with the data wey dey collected from mobile devices via sensors, Shiv comment sey “From the data buyers’ perspective, this na terrible data too. Why you wan collect data under the table to spy on your users wey you don bury the consent on page 70 of a contract wey no one don read? You get data just as a byproduct now of our digital lives when actually you really wan be the product. That na the problem Data Unions dey solve — if you wan create good data products, get people to create the data wey actually wan dey part of this and dey happy to do am. E dey create better data products — more rich, stable, interesting, and sustainable.”
Another problem na sey data brokers go bust all the time. Cambridge Analytica na the most famous. Recently, Jumpshot dey, wey be subsidiary of the antivirus giant Avast, wey go under last year, Vice reports. The company get $30M USD in revenue, wey dey projected to reach $70M USD with 400 employees. Vice mention sey, “An antivirus program dey used by hundreds of millions of people around the world dey sell highly sensitive web browsing data to many of the world’s biggest companies, a joint investigation by Motherboard and PCMag do found. Our report rely on leaked user data, contracts, and other company documents wey show the sale of this data dey both highly sensitive and in many cases suppose remain confidential between the company wey dey sell the data and the clients wey dey purchase am.”
Which parts of the world dey more receptive to Data Unions?
In theory, every government na pro Data Unions because them provide two benefits. One, dey clearly give monetary value to ordinary people and return am to ordinary people — as opposed to companies wey usually outside of their own country. If you dey democratic or populist government — that sound nice. Second, e go also open up innovation, and e dey actually good for business.
You fit also see rights to data portability wey already don start to dey take off in Europe. According to a report by Deloitte:
76% of respondents dey aware of the right to data portability.
9% wey reveal to don already submit portability requests.
23% neva hear of the right.
24% state sey dem no get intention to use am.
Interestingly, 81% of respondents from EU countries dey aware of the right to data portability.
The Netherlands and France dey lead the way in terms of awareness, both at 83%.
Only 68% of respondents non‑EU countries dey show awareness of this particular right.
Australia and Canada record the lowest awareness; 60% and 62% respectively. This fit dey attributed to am wey be the only new data right wey consumers gegt, and dey less practiced before now.
“Where Europe dey lead, I think other countries go follow, especially with respect to GDPR. We dey get good noise from the US, and I believe sey the UK go try catch up because of political reasons. India, too, get natural resonance to this kind model because of grassroots cooperativism. dey built into the country’s economic history in the last 100 years and na also a democratic country. I no know where China dey go with them.”
Data Unions fit dey acquired? We fit choose who we go sell our data to?
At the moment, we no get technological way of signaling the member preferences of a Data Union. But e no dey that difficult to build am in, according to Shiv. For ideal situation, people fit say at the beginning: these na the kinds of organizations I dey interested to sell to. You fit also trust a data cooperative wey you believe in to make these decisions for you. A recent EU scheme dey based on this fiduciary model. E say if you be data cooperative, you must to get a legal duty of care to your members. For November last year, the European Commission release the draft of the Data Governance Act wey set out licensing and regulatory framework for data unions — or data intermediaries as them call them — wey go give this nascent sector huge boost in terms of funding, trust, stability, and assurance and advertising a direction of travel to the world. Them also don announce sey €2bn in funding go dey made available for those wey dey seek out to build to enable software and Data Union projects.
We nva dey there yet, but at some point, you go talk sey “I only wan sell data to charities and university researchers.” This go put you for a different bucket of data, and only those buckets go dey able to dey purchased by certain types of buyers.
Shiv talk sey if one day Google decide to buy Data Unions, that na problem. “Obviously, e go haunt lot of people but certainly don haunted me. I go love to see sey you no fit force Data Union operators wey come along as entrepreneurs to be cooperatives. But I go like a federation of cooperatives. If that no work, then with the fiduciary aspect of that legal duty of care — e go probably make am really difficult for Google to purchase data sets.”
Shiv add sey, “The token model also dey help because the whole point of token really be sey you fit disassociate equity and ownership of an underlying capital asset from utility and value. And if you no fit do that, you fit turn revenues to people and then no need to sell out your equity to anyone wey also helps with the cooperative stuff. But e dey difficult. All of this stuff na stuff wey we need to think about. And I know why that dey keep me up at night.”
Here na list of the selected time stamps on the different topics wey dey discussed during the podcast:
2:10–5:25: Shiv’s journey from investigative data journalism to Data Privacy and Data Unions evangelist
5:25–10:59: Wetin be Data Unions and how dem dey work? Examples of Data Unions
10:59–14:20: How Data Unions fit dey monetized with different use cases?
14:20–22:15: Data Unions as a revenue source to IoTs
22:15–25:20: Wey parts of the world dey receptive to Data Unions and how legislative regulations don help?
25:20–29:30: Data Unions fit dey acquired? We fit choose who we sell out data to?
29:30 — End: Why Blockchain and data need to go hand-in-hand: The role of Data DAOs in Data Unions