Biz Case for Data Portability

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Convener: Elias Bizannes


Business case, modeling, portability, economics, adoption, maximizing value

Discussion notes:

Dataportability Business Case Information Value Chain

P (Data creation -> Information generation -> Knowledge Application)

S [storage] [processing] [distribution & socialisation]

Theory: Specialization leads to comparative advantage

If you get different people focusing on one key part of the chain each, then everyone can get better value, thanks to specialization.

Counter: However, diversification is more profitable than specialization… or at least it appears that way. Because of customer acquisition costs, many companies work to maximize how much they can monetize from each customer by offering more and more services and functionality.

Perhaps improved data quality and the resultant reduced costs is significant. "50% of a Business's cost infrastructure exists to compensate for not knowing what the Consumer already knows…"

John McKean, Author "Information Masters - Secrets of the Customer Race". .

So, is the business case 100% cost savings?

Not necessarily. What about e-commerce that can reduce the % of abandoned shopping carts?

Perhaps the measure is engagement?

Counter: The primary theory doesn't necessarily actually encourage or suggest or explain the business case for data portability. It supports specialization, but that could lead simply to kieretsu-based dependencies between members of the chain. In order to make a case for portability, you'd have to make the case for interchangeability between elements in the same layer in the value chain.

Key obstacles to adopting this kind of model?

1. does it make sense 2. cultural? NIH & thinking in specialist models

Recommendation: the Big Switch