Modeling the Personal Data Ecosystem: Business and Policy Scenarios ID3. (C) John H.. Clippinger

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Archibald Morgan
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1 Modeling the Personal Data Ecosystem: Business and Policy Scenarios ID3 (C) John H.. Clippinger

2 A WEF View of Personal Data

Personal Data Ecosystem Marketers Banks DATA USERS Media Affiliates Information Brokers DATA BROKERS Employers List Brokers Internet

Public Medical examples: examples: C-1 Individual MEDIA Catalog Co-ops GOVERNMENT AGENCIES HOSPITALS UTILITY COMPANIES Retail

BANKS Media Archives STOCK COMPANIES Telecommunications &examples: Mobile MOBILE PROVIDERS Product & Service Delivery CABLE COMPANIES

3 Personal Data Ecosystem Marketers Banks DATA USERS Media Affiliates Information Brokers DATA BROKERS Employers List Brokers Internet examples: DATA COLLECTORS (sources) Websites SPECIAL OFFER BUY ONE, GET ONE RETAIL & CONTENT WEBSITES SOCIAL NETWORKING SERVICES Public Medical examples: examples: C-1 Individual MEDIA Catalog Co-ops GOVERNMENT AGENCIES HOSPITALS UTILITY COMPANIES Retail examples: AIRLINES CREDIT CARD COMPANIES DOCTORS & NURSES PHARMACIES Government Financial & Insurace examples: INSURANCE RETAIL STORES BANKS Media Archives STOCK COMPANIES Telecommunications &examples: Mobile MOBILE PROVIDERS Product & Service Delivery CABLE COMPANIES CARRIERS Ad Networks & Analytics Companies Credit Bureaus Healthcare Analytics Lawyers/ Private Investigators from FTC Report Law Enforcement Individuals

4 ID3 Technology Perspective on Personal Data Ecology Overview P2P Personal Data Ecosystem Architecture Core-Utility" Free" Services Authentication Payments Audits Reputation P2P Markets Discovery Data mining Private Social Networks Location Services Services Registered Pay Apps" Affinity Markets Specialized Data Vertical Data Offers Mining for Driven Services Prediction & Health, Education Identity & Authentication" Risk Management Finance, Cities OAuth 2 Open Id Connect Security" Root ID Privacy" 256 Encryption Persona ID &" HTTPS Permission" Behavioral Certs. Trust Framework FFIP Anonymizers 4 Levels Temp IDs Individual Bill " Of Rights for Trust Zero K Proofs Personal Data & Identity" Wrapper P2P Computation Root Identity Audits Persona Behavior Monitor Enforcement 2 factor Biometric Dispute Res. PKI Self Organic Auth Personal Data Lockers" Correction P2P Distributed Anonymous Cloud Encrypted Query & Comp Non sharing Sole Individual Control APIs for Data Services & Other Trust Networks" - Location Services Linked In, Facebook, Google, Bank, Amazon InterOp" " Trust Frameworks DURSA EU HIPAA My Data COPPA Copyright John Henry Clippinger" 2012 "

5 what is a Personal Data Ecosystem? Where the survival and growth of different agents/enterprises depends upon the creation, capture, processing and exchange of different kinds of personal data. Where there are complementary niches of different types of data (financial, mobile, social-search, retail ) that are processed and exchanged for mutual value by users of data Where there is cooperation and competition within and across different data niches Where value is accumulated by different agents by converting data into offers that result in sales/revenue Where the principal source of value are individuals personal data - financial, retail, health, civic, social behaviors and conditions

6 What are the dynamics in Personal Data Ecosystem? Where agents compete within their niches (within species competition) and across niches - (species to species competition) to acquire data, convert low grade data into high grade data and to convert data into completed offers The evolutionary success of agents within and across niches depends upon the efficiency with which they are able to compete and cooperate There are external parameters such as niche protection-permeability, the presences of bad actors - parasites - that steal data - and symbiots that combat parasites

7 How is value created? Value is created by the accumulation of completed offers within and across niches by different agents. In the case of data the greater the flow of data and the better the quality of the data the greater value is created. Data are not a depleted with repeated use but rather increases value through use and circulation Innovation in data is achieved through improved quality and analysis to drive other processes in the ecosystem There is value for the individual agents and there is overall ecological value representing the aggregate value created for the entire ecosystem Given that data need not be finite resources - especially through value added processing - aggregate ecological value is not fixed

8 what the model can help us understand Appreciate the dynamics over time between different agents and the consequences of significant technological changes and policy interventions Assess different kinds of scenarios and what are the likely and unlikely - and in some cases highly improbable - outcomes Appreciate the power of unintentional consequences, interaction and combinatorial effects of different decisions and policies Appreciate the interaction of local choices - such as investments in technology - and global choices - strategic and regulatory What happens when the individual is at the center - has control over their own data?

9 What are some the ecological business issues? For every enterprise how efficient are they within and across niches in acquiring data, increasing the value of the data and in making completed offers? What are their cost structures and rate of innovation/productivity in competing within and across niches? What is the relative value of aggregating data across niches (financial, mobile, social, search) What are the relative competitive advantages of different enterprises in terms of innovation/learning functions? Are they linear - no linear - step functions? What is an enterprises Learning-Adaptation coefficient? What effects the conversion of data to offers? Data quality, security, aggregation-hybridization What is an enterprises extinction coefficient?

10 What the model can help businesses leaders understand Identify their relative competitive strengths and weaknesses - costs/ - inventory of data assets, productivity of data acquisition - value creation - analysis and offer conversion ratios Legacy costs and vulnerability to step function innovations in data discovery, analysis, and aggregation Level of investment and tradeoffs in security and privacy protections Vulnerability - benefit of niche regulations and data security and privacy requirements Strategic alternatives and trigger point for pivotal strategies and investments and collaborations Evolutionary Stable Strategies re different scenarios and competitors

11 What modeling can help regulators understand Tradeoffs between heavy and light regulation in generating overall ecosystem value Distribution of value between agents/enterprises and individuals Importance and effects of niche (sector) protections for data types Power of local incentives towards security and privacy Tradeoffs between public/macro and private/micro incentives in generating innovation and overall economic value for ecosystem Level of public investment in security for overall maximum benefit Potential unintended consequences of some regulations or lack of regulations Parasite - free rider invasion - limitation strategies Economic benefit of user control over personal data Effect of strategic technologies: security - aggregation - machine learning

12 introducing the multi-agent simulation model: NetLogo

13 NetLogo Model

14 Modeling Parameters Through Sliders

15 Visualizing Outcomes and Scenarios

16 Need for Empirical Data and Experimentation to Calibrate Model What are the actual costs and productivity by sector/enterprise ( financial, mobile, retail, social media - search) for: Acquiring personal data Securing personal data Protecting privacy Validating personal data Improving quality Analytics Making and closing offers to buyers/users of personal data

17 New Category of Enterprise : User Centricity & Low Cost Collective Action Is there a threshold point of reduced coordination and discovery costs where aggregates of consumers can also become aggregators and vendors of personal data? Is there a tipping point point where there is a trigger transition from push to pull and autonomous exchanges replace traditional brokers and ad networks? What are the technology, business, and policy drivers for this tipping point? Are there empirical data to support modeling such scenarios?

18 Technology Productivity Modeling Questions How to model the productivity effects of new technology - in security - authentication - permissioning - discovery - aggregation - machine learning - storage - computation - mobile bandwidth - device costs Are these effects - linear - non linear - step functions - t? How do productivity- innovation differences in the five sectors affect competitiveness? How might high productivity gains in security, discovery, aggregation, authentication, aggregation and machine learning transform an ecosystem into a new network topology and business model?

19 Extinction Effects How does one calibrate the vulnerability of different enterprises to niche (market share) diminishment? Is is a percentage of the total? Is there a threshold trigger and the total collapse? Are there historical data? Are different types of enterprises (banks, credit bureaus, data brokers) more vulnerable to niche diminishment (high costs, low innovation rates, thin margins, slow to adapt)? Are there cascade effects arising from combinations of changes - technology efficiency - regulatory - that accelerate extinction for some enterprises and not others? Are there new enterprises (mutants-invasive species) that have sufficiently different cost, productivity, learning-adaptivity metrics that they can take over a niche or niches?

20 Ecological Value and Evolutionary Stable Solutions (ESS) Is there a way to have a total ecosystem metric value generated to assess the ranking of different scenarios? Can the value of distribution value effects be seen in terms of sustainable flows rather than simple distributional equity? Can one calculate ESS for different combinations of players to prescribe an optimum sustainable outcome for Personal Data Ecosystem?

21 More questions than answers..so work with us because the answers could be very interesting

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