Personal Data Ecosystem

From IIW

Issue/Topic: Personal Data EcoSystem

Session: Tuesday 5F

Conference: IIW-11 November 2-4, Mountain View, Complete Notes Page

Convener: Kaliya

Notes-taker(s): Barbara Bowen


Discussion notes:

Kaliya opened the session by reflecting on the past developments and identifying areas to improve:

Mistakes around previous standards and adoption?

Identity commons founder Owen Davis sought to retrieve personal data from service providers

Individuals have many digital devices and data beyond personal devices.

Three points of data aggregation are currently prevailing: Search, Telcos and call logs, and “Cookie land”

How can individuals have a personal, service or cloud where there is a personal copy of data?

User owned ad exchange, how do consumers create personal rfps?

System overview: Individuals have comprehensive and integrated view and data bank to self-monetize personal information.

Adoption will accelerate with Conversations around individual data service provider lock in and Incentives for service providers to cooperate.

Previous models: Publisher provides a venue for things to happen, what, where, who, when? Publisher shapes user experience and aggregates audience.

Key Points:

  • New shift... center of universe is wherever you are.
  • Marc Davis describes cloverleaf conversion to understand relationships

where suddenly the data that can be fused through intent and interaction.

  • MyDex in the UK is an asset locked corporation, they are a community

interest company to work on behalf of the end users. This is a working model that links to current information, agencies and vendors. Goal is to isolate an immediate form of data through change of address.

  • The core technology should work seemlessly.
  • VRM is a homebase to make connections with 3rd and 4th party

intermediaries. A specific RFP gives the user control and is given a voice in the marketplace. B-to-B is a huge dimension of VRM as well. Lead generation is personal, with VRM I could become my own qualified and personally verified lead generator

  • Business models, service providers, interaction between entities, are key


  • Peer to peer linking is very important to build in to the set of services

as well as network portability.

  • Groups are able to provide authentication and authorization and assert


  • Cloud services apps will be linked to a personal data store to provide

interesting services. An example is a wish list, that is implicit to location and inventory.

  • Schema interop between databases and data rights is a point of focus in

standardization. Currency conversion is an example of data interop is present in monetary systems and markets.

  • What lessons have been learned, and what are some of the turning points*
  • The federal trade commission has been holding hearings about personal data. “The user has been seen as someone to exploit when services optimize to


  • Scott David suggests privacy as a product placed in a position to gain

interest and engagement between business and individuals. Operations and functions that are reliable and can be shaped through services. Parameters for these of economics can scale.

  • The issue of greed vs. fear in society.
  • Market efficiencies through cohesive needs. Data should flow and the

information can be observed. Data flow can have a regulatory level.

Additional follow up sessions about the personal data ecosystem were suggested: Marketing message, Business Models, Knowledge, roles and entities, Identity portability..

Next Conversations.

  • Commercial incentives that benefit users when they expect things for free.
  • How to bring regular old users in
  • User Experience
  • Relating to the Telco Universe
  • Database scehmas and interop - many to many database normalization
  • What are the Business models for information assets
  • Pooling knowledge on ecosystem rolls
  • Current and proposed credit agencies
  • Low level technology stuff OAuth related to personal data ecosystem claims
  • More data, more corellation, more anonomyization breaks down. What are the terms for derezing data - APIs on resolution. What are ranges and thresholds