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HAT: Engineering a market (Cambridge)
Transcript of HAT: Engineering a market (Cambridge)
ActivityID (Event) (multiple)
Driving to Work
inputtype (including multiple states)
useful for IoT appliances e.g.
fridge, oven etc.
Singel/Multiple activities make an EVENT
Markets that 'supply' input data
Individual Information Repository (other databases/servers)/Suppliers of input metadata
HAT Project: VALUE CREATION SCHEMA (STRAWMAN)
Retailer databases/CRM systems
Quantified self apps/devices
Derive Inventory apps
Derive Visualisation apps
Derive Analysis/Intelligence/prediction/advice apps
Private & Confidential: Please do not share
Value Creating Contextual Parameters
Intermediate between individual personal data and supplier
Derive matching apps
Markets that act on/transform/'buy' personal data
Individual (personal data holder)
Activities monitoring apps
& supply chain
Utility Retailer databases
1. DATA INPUT
2. DATA OUTPUT
MULTI SIDED MARKET
1. Input demand market
User must WANT to acquire data
Firms must WANT to give data
Use case: Contextualisation for diarying
2. Output demand market
USER must want to GIVE data
Firms must WANT to acquire data
Use case: Manufacturers - INVENTORY+REPLENISHMENT
Use case: Internet providers - BENCHMARKING
Externality: a consequence of an industrial or commercial activity which affects other parties without this being reflected in market prices, such as the pollination of surrounding crops by bees kept for honey (+), pollution caused by cars (-)
Externalities of a digital economy
(+): more digital visibility, better coordination
(-): fear, risks, privacy concerns
(+/-): generation of more personal metadata
Internalising the personal data externality for growth in the economy
Personal metadata from:
Control and actuation of IoT/Internet connected objects (meta data)
Online services (personal information)
Transactions e.g. banking, health (personal information)
Internalising (into the economy) the externality of a lot of personal metadata generated could result in jobs/innovation/new businesses
important (because personal metadata is starting to be viewed as a negative externality
But to do that, we need to engineer a market for personal data
What do you need for a market
Value proposition (an offering, a good or service)
Something that satisfies a need
Value creation (the experience, consumption or interaction with the good or service)
Supply (usually firms)
A place to trade (e.g. a physical place, a tech platform) for value capture (revenues/payments) to occur
With Accepted Regulation
With Accepted Price mechanisms
‘Raw’ Personal data is currently ‘messy’
Collected by firms for their own purposes
Deeply siloed within verticals, including their format and representation of data
Mixed contextual and acontextual data, object personal data and personal metadata
to make data a SERVICE: Four challenges (analogy with diamonds):
The ‘raw’ personal data supply. consumers do not have it, do not have access to it (the mining issue)
The separation of parameters into contextual and acontextual parameters (the sorting issue)
The contextualisation of personal data (the cutting issue)
Creating the marketplace to trade (Market issue)
Creating the value proposition (‘offering’) of personal data as a service
Challenge 1: Acquiring 'Raw' Vertical Data
Creating the value proposition out of ‘raw’ vertical object data:
Separating contextual from acontextual metadata
Value is created ‘in-use’ and ‘in-context’ of objects
Metadata of ‘in-use’ and ‘in-context’ must be separated from ‘static’ metadata on objects
T-shirt ‘in-context’ (contextual) have the following parameters (1) start/end time of use (duration) (2) location of use etc.
T-shirt (acontextual) have the following parameters (1) colour (2) size (3) supplier identifier ID
Contextual metadata is therefore data on value creation, acontextual metadata is data on value proposition
Challenge 3: Transforming the ‘Raw’ Vertical Data of objects to become contextual personal data:
Creating relationships between contextual metadata through events
Human value creation with objects are part of a value constellation with membership of objects i.e. toothpaste, toothbrush, water, light, mirror
Members of value constellation comes about through activity sets, i.e. events e.g. driving to work, having breakfast.
Human lives are about the mundane but events are what create meaning in human lives
Creating relationships between contextual metadata (value creation) creates relationships between value propositions (vertical industries)
creating relationships between metadata allows vertical to 'wet-wire', take horizontal step
Previous project (SeRTES) on sense making representation of technology enablement with 7 participants
6 Digital Person Zeroes who donate their body (of data) to research
Homes and personal life instrumented for data
Full ethnographic study, qualitative interviews
Data analysis - qualitative and quantitative
Needs visibility of practices
Needs understanding of value creating practices
Needs understanding of the mundane
Needs understanding of the "crises of the mundane" as a contextual resource need
Needs understanding of how contexts (and it's variety) could be served viably
Need to understand how human beings use information
How we got here - the research
Transformed personal data now becomes the a value proposition to both firms (to create applications) and consumers (to use for more effective lives) e.g.
Contextualised Data on consumption of FMCG
Contextualised Data on experience of objects
Contextualised Data on interactions with devices
Data on contexts
Data linking contextual with acontextual
How HAT addresses the challenge
One side of market currently engaged (developers, firms). Increasing engagement - Mad Hatter's tea party 17 July
Engaging developers - 24 Nov
Other side of the market to scale up globally - target to begin March 2015 with a limited public release ver 1.0 - pitched as a fun personal data instrument - possibly 120,000 students from HAT universities
inbound APIs complete
outbound APIs in 2 weeks
HAT provision - Compliance document under consultation
4 companion tools in place (more to follow)
Database out 17 July to engage with developers, device etc. (firms side market)
HAT demonstrator - out November 2014
HAT scale-up plan - Codenamed
Target HAT foundation to start - June 2015 - founding members invited
6 to 1 million HATs: Scale up deployment
The markets challenge before us
asynchronous distribution of data
Price setting & subsidies are insufficient get ecosystem performance - balance of value creation vs higher platform profits - regulatory power is required and any revenues must be incentivised to reward high platform performance & better ecosystem – ie not public regulation, not private motive
Platform regulator must hv access to wider menu of regulatory instruments to implement desired actions
‘hard’ instruments: Licensing, property rights assignment, legal instruments (‘laws’, ‘design rules’, ‘versioning’) i.e. Technical design, system architecture & technical relationships must be specified to ensure ecosystem performance and to be the trust broker & platform leader, to aid HAT providers and firms
‘soft’ instruments also used e.g. communication, signaling, ratings
Incentive instruments also used to perform regulatory role
Positioning for scaling up: CREATE ENDOWMENT EFFECT, PLAY WITH DATA (shower demonstrator, create need, create fun)
Who might want this?
outbound data: USD20billion spend on CRM - Suppliers to the home: Supply chain that ends at high street, supermarkets or pharmacies/hospitals - No visibility of consumption
inbound data: IoT device firms - 1. playing in 2-3 HAT markets helps 'monetise' personal metadata that is ethical, privacy preserving, market friendly, innovative
inbound data: IoT firms: stimulate demand, scale the digital offering to millions
Individuals would like to have greater efficiency & effectiveness of buying
Have a repository of health & wellbeing in times of crisis
Have efficient triggers to buy
60% GDP in the home
human decisions that are data-driven
Individual-side efficiency (Creating Continuous Supply of quality personal data)
HAT database schema beta by RCUK HAT Project Universities is licensed under a Creative Commons Attribution 4.0 International License
Engineering a Market for Personal Data
The HAT is a market maker - its principle aim is to generate new businesses, new innovation, new jobs, employment, improved digital economy
It is necessarily free for full network effects e.g. www
HAT Emulates the Email model (if you have an email, you should have a HAT)
Multiple HAT providers possible (gmail, hotmail of email; bank, can be HAT provider).
HAT is the DB schema and transformation engine for personal data – possiible for nterfaces, experiences etc. could be different for different HATs
However, all HATs are the same version at the core (full scalability) so whoever the HAT provider is, any app working on one HAT works on all HATs
HAT project evolves to HAT foundation in 2015 after project is over, supported by the community
Market Making function of the HAT
Challenge 2: Transforming the ‘Raw’ Vertical Data of objects to become contextual personal data
HAT “lower-level” APIs (application programming interface). The API will take the form of REST and provide interoperability to any organisation or developer wishing to make their device “HAT-Ready”. The lower-level APIs are designed for device/thing manufacturers to allow flexible present “raw” data to flow into the HAT database as useful and re-usable artefacts.
A market from an individual's perspective
A Global and Scalable 'Market-of-One' - yes this is possible - a fully standardised platform and yet fully personalisable.
Why take an individual-centric approach?
• Individuals are able to share their own metadata from various industries without any privacy restrictions, if they are given access to their own data;
• Individuals are best able to co-create and contextualise their own metadata based on what it means in their own lives;
• Individuals are stakeholders of the quality of the metadata if they can see the benefit in the use of their own metadata for their own decisions;
• Individuals are stakeholders of the supply of the metadata as they can actively seek out ways to acquire more personal metadata (e.g. buying IoT devices) if it is beneficial for their lives.
But there are challenges........
How HAT addresses the challnege:
How HAT addresses the challenge through applying SD logic
How HAT addresses the challenge (through value creation):
Crowdsourcing & Co-creating Contextualisation
Challenge 4: Creating a multi-sided marketplace for personal data as a service
Download the briefing paper!
Today: Data belongs to those who collect it.
e.g. If purchases are made on an online supermarket, that data is owned by the supermarket.
Data also owned by whoever owns the technology that collected it. Reason: Without the technology, this data won’t even exist.
We currently have no access => we don't benefit from integrating it into our lives.
Even if data is returned to us, we may noy know what to do with it.
Data was to help the institution that collected it, not us. its vertical data, not horizontal data
Today's world is increasingly digital and connected.
BIG DATA: Diverse types of data collected from transactions, interactions, movement of people and objects.
As things become connected through the Internet-of-Things, even more data is being generated.
Individuals become increasingly worried about privacy, confidentiality, security and trust.
Some of us get so worried we start to withdraw digital visibility
Canceling our Facebook accounts
Stop using google
Data gets 'noisy'.
i.e. unreliable information. 'quality' of data suffers
With the fears, we face the potential of 'shrinking supply' of data.
There is a threat of regulation, which could increase costs. This could lead to institutions become reluctant to invest in innovation
We don’t get more advanced technologies so this all ends badly for everyone.
Downward spiral: Less business opportunities, less innovation, less jobs.
How do we reverse this and help the digital economy spiral upwards?
Engineering a Market for Personal Data as a Service
Professor Irene Ng
Director, International Institute of Product & Service Innovation, WMG, University of Warwick
Professor of Marketing & Service Systems
Lead, Business Transformation research group
Inbound/outbound API control
Inbound/outbound API control
Legal, Technological compliance on DATA security, privacy, confidentiality control
Legal, Technological compliance on DATA security, privacy, confidentiality control
for purchase by other platform HAT users