Things Yet To Solve

 

Is there really a market for data?

 

Generally speaking, many marketplace business models fail, but there tends to be at least one significant, successful marketplace for a given commodity.

That said, we are not in a “commodity” business. Each piece of data is unique and has a unique value. It is NOT highly substitutable except at the macro level.

 

Data is Different and Does Not Lend Itself to the Marketplace paradigm.

 

The marketplace paradigm, which relies on heavy process standardization, ease of comparison, arms length interactions between buyers and suppliers and clear-cut value transfer.

  • It is adaptable to the point where there is zero-recognition or traceability back to the original form. For example, a user could take a structured table, cleanse it, join it with another data set, execute multiple calculations and end up with an unstructured PDF report or visualization that bears little or no resemblance to the original sources. If you start with unstructured data the traceability is even less.

  • It is highly substitutable. For example, there may be many options available for sourcing company data and while there will be differences in coverage, timeliness and general reliability the data itself is substitutable.

  • It is exceptionally diverse. Not only is the specific data diverse, but so are the formats, delivery mechanisms, frequency of update, terms and pricing. This makes it highly complex and difficult for people to engage with.

  • It is often ‘raw’. The data that is available is rarely ready to support all of the diverse use cases for which it may be applicable meaning the consumer spends significant resources adapting it to a specific use case.

  • It is digital and easy to copy. This makes data easy to steal and coupled with a lack of traceability means it’s risky

 

Need to Create Trust

 

But, what is perhaps most interesting is that managing these characteristics means generating very high levels of trust, which is ironic given that trust is understood to be critical for any successful marketplace.

Trust is Critical!

All marketplaces tend to drive a value proposition by optimizing for discovery and fulfilment. Amazon is a great example — find everything you need, one-click checkout and same-day delivery — but trust is an issue they also need to deal with. Trust is typically about the quality of the products and trust that it will arrive. Some marketplaces (e.g. Ebay) also trust that the consumer will make payment post-fulfillment.

In data marketplaces, trust is significantly more important and challenging to resolve. Users need to trust that:

  • The data is high quality and dependable

  • The supply will be consistent and not break processes

  • The data will deliver value once it has started to be used

  • The consumer will not steal the data (or have it stolen from them)

  • The consumer will not use the data for non-permitted use cases

NEED to create mechanisms to iteratively build trust and the ability to interact in low-trust situations

 

Fixing highly inefficient Industry norms

 

  • Sample-based Trials - A trade-off between proving value and losing ownership of an entire product. This creates significant downstream costs for data consumers to compare samples (e.g. storage and compute resources and developer time spent homogenising data models and making multiple joins with proprietary data). This also leaves suppliers unable to compete or provide hands-on support as to how to best use their product.

  • Non-standard Pricing - Data is notoriously difficult to price; the value varies dramatically between vertical, jurisdiction, customer and use case AND there is imperfect knowledge on both sides click to tweet. This ultimately leads to protracted commercial discussions, potentially with multiple parties simultaneously.

  • Bespoke Legal Documents - Extensive legal negotiations and agreements are often required to protect against the risk for both parties due to the supplier losing ownership of their product and the potential for loss, theft, misuse, etc. The risk appetite of organizations varies dramatically but also in line with the perceived value of the product they are trying to sell or buy.

  • Long Commitments - Due to the slow, expensive and risky processes outlined above, it is typical for data licenses to be for years. This run generates yet more risk for the consumer, as the cost of failure is high. Market data teams try to simplify this by championing specific data. But in the absence of understanding the specific use case or the business teams being able to properly quantify or articulate the difference in value between alternatives, this is not a solution that works.

  • The data marketplace paradigm to increase the usage and stickiness of their existing cloud platforms limits where the data can reside. This restricts the addressable market and creates complexity for suppliers, who need to manage their offerings across multiple data marketplaces. The value proposition for data suppliers is unclear given many have their own solutions to enable discovery and fulfilment – the main benefits of a data marketplace – and without being sat alongside their direct competitors.

 

What is the Point of DLA

 

A standard marketplace is simply not the right model for supporting transactions between data buyers and suppliers. Additionally, any marketplace has to manage issues inherent to the marketplace model, including disintermediation, commoditization, governance of negative behaviour and the cold-start problem, all of which are arguably far more significant when the commodity is data.

 

What is the Future State?

 

  • Discoverability

  • Fulfilment

  • Data curated, comparable and sometimes even standardized, which is extremely helpful.

  • Consumers are empowered to get the data on-demand, rather than it being entirely left with the supplier.