When it comes to data innovation, do you think it’s ABC?

David Reed, director of research and editor-in-chief, DataIQ

Blockchain has been one of the biggest buzzwords in the data industry for the last two years. The resilience and imutability of the underpinning distributed ledger technology (DLT) holds a strong appeal for many data management practitioners. But since the second half of 2017, has there been a growing sense of “anything but blockchain?” (ABC, with a little poetic license.)

The trend for blockchain-enabled solutions shows no signs of abating as such. After all, investors put some $6.6 billion into start-ups and ventures using DLT last year. In the last week alone, I have received press releases from Aventus, a ticketing platform, Choon, a music-streaming platform, and Better Betting, a peer-to-peer betting network, all of which are built on this protocol. Clearly, there is a strong movement around the adoption of this technology as a new way to enable value creation and sharing.

But great ideas and eager backers alone do not guarantee the success of any one of those ventures, or even of blockchain itself as a disruptive technology. Indeed, scratch beneath the surface and there are many reasons to be sceptical about much of what is being developed, especially if you are a mainstream brand coming under pressure from internal data practitioners or external consultants to migrate some prcesses into blockchain.

According to a report by Business Insider, the majority of funding raised - some $5.6 billion - was through initial coin offerings (ICOs), rather than conventional seed funding and series capital. Significantly, the majority of these ICOs have been backed by retail or small investors, not by sophisticated investors - a distinction which regulators make in order to keep individuals who are taking a small punt with their own money away from the riskier or exotic types of investment.

And ICOs certainly qualify as risky, which is why many of the solutions which have been funded in this way really ought to have thought better of it. Imagine taking a business case to your chief financial officer which involves purchasing tokens at an uncertain current value, whose value will fluctuate in unforeseeable ways, in order to access a service which is often not even at the stage of being a minimum viable product. (Of course, early investment is often intended to translate an idea into a MVP.) Alternatively, you might want to wait for that MVP to emerge before trialling a new service, in which case you will need to buy tokens through an exchange at an unpredictable cost and with an uncertain value or onward tradability.

So start-ups are creating both a lock-in for their investors, since there may be zero demand for the tokens they purchase via the ICO, and a lock-out for potential customers who may not like the idea of using a cryptocurrency to pay for a service, rather than conventional invoicing and cash.

What is clear from this trend is that, despite attempts to break DLT out of its bitcoin roots, developers have instead been lured by the prospect of creating their own next big cryptocurrency. In some cases, that may be more what the business plan has in mind than actually creating a working blockchain solution. Perhaps it is no surprise, therefore, that only 48% of those blockchain ICOs got away successfully, or that regulators are taking a very close look at these investment projects.

Data practitioners have every reason to feel interested in what blockchain could offer and to investigate some of the platforms under development. But they also have every reason to be wary of what might be more a financial, than a data management play. The attitude should be, “anything but bitcoin”.

Related articles: As Bitcoin bounces, blockchain breaks out



Please note that blogs are the sole view of the author and that they are not neccesarily the view of IQ ddg Ltd and should not be interpreted as advice. Please read our full disclaimer

Knowledge and strategy director, DataIQ
David is developing the framework for soft skills and career development among data and analytics practitioners. He continues to be editor-in-chief and research director for DataIQ.