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HAT: Engineering a market (Cambridge)

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Irene Ng

on 29 October 2014

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Transcript of HAT: Engineering a market (Cambridge)

What_Activity
ActivityID (Event) (multiple)
Owner_ActiveEntity
EntityType (multiple)
What_ActiveEntities
EntityType (multiple)
VCpractice_XXX (rule)
EntityLocation
EntityLocation
LocationID
LocationCondition
Activity_Time
ActivityStartTime
ActivityEndTime
What_NonActiveEntities
EntityType (multiple)
InputType
UDF (userdefined/input)
Server (openAPI)
Sensor
Other
ActivityID (Event)
Shower
Breakfast
Driving to Work
Sleep
WatchingTV
Inputtype
Activity_Set
ActivitySetID
What_Activity (multiple)
EntityType
EntityType_Utility
EntityType_Thing
EntityType_Person
EntityType_Animal
Entity_Type_Thing
ETTVCpracticetype
ThingIDtype
ThingPurchaseinfo
ETTVCpracticetype
VCpractice_Experience
VCpractice_Consumed
VCpractice_Interacted
ThingID_FMCG
Inputtype
Itemname
ItemNo
ItemDescription
PackDescription
ProductGroup
QtyPerPack
NumberOfInners
Size
Weight
Volume
ReorderPeriod
ReorderLevel
ReorderQty
MinimumStockLevel
MaximumOrderQty
VatCode
CommodityCode
RSP
Class
UnitHeight
UnitWidth
UnitDepth
CaseHeight
CaseWidth
CaseDepth
LayQty
PallQty
PromoFill
PromoPMP
PromoOther
ExclusiveToDCS
SeqNo
SuppUnits
BarcodeOuter
Person_ID
Person_ID_socialdata
Person_ID_details
Person_ID_HealthMon
Person_ID_emotion
EntityType_person
PersonID
Relation
UDF1
UDF2
Person_ID_HealthMon
Weight
Height
BP
HeartRate
Blood Alcohol
GSR
LocationID
GPS
Homespace1
InputType
Homespace1
LivingRoom
Inputtype
LocationCondition
Humidity
Light
Temperature
PollenCount
CO2_level
AQI
Dust
VCpractice_XXX
IF Entity_Type_thing_ETTVCpracticetype=
VCpractice_experience
VCpractice_Experience_starttime
VCpractice_Experience_endtime
Inputtype

IF Entity_Type_thing_ETTVCpracticetype=
VCpractice_Consumed
VCpractice_Consumed_starttime
VCpractice_consumed_endtime
VCpractice_Consumed_startunit
VCpractice_Consumed_endunit
inputtype

IF Entity_Type_thing_ETTVCpracticetype=
VCpractice_Interacted
VCpractice_Interacted_startime
VCpractice_Interacted_endtime
inputtype (including multiple states)
useful for IoT appliances e.g.
fridge, oven etc.
ActivityStarttime
Date
Time
InputType
ActivityEndttime
Date
Time
InputType
PollenCount
Date
Time
Number
InputType
CO2_level
Date
Time
Number
InputType
AQI
Date
Time
Number
InputType
Dust
Date
Time
Number
InputType
Height
Date
Time
Number
InputType
Weight
Date
Time
Unitmeasure
InputType
BP
Date
Time
Number_DIA
Number_SYS
InputType
HeartRate
Date
Time
Measure
InputType
BloodAlcohol
Date
Time
UnitMeasure
InputType
GSR
Date
Time
UnitMeasure
InputType
Person_ID_details
FirstName
MiddleName
LastName
DoB
Gender
Marital Status
Dependents
Income
Nationality
Date
Time
InputType
I_MSP_Thing
Humidity
Date
Time
Unitmeasure
InputType
Light
Date
Time
Unitmeasure
InputType
Temperature
Date
Time
Unitmeasure
InputType
I_MSP_environ
Singel/Multiple activities make an EVENT
Entity_Type_Utility
ETTVCpracticetype
UtilityChannel (multiple)
UtilityIDtype
UtilityIDtype
Utility IDTypePurchaseinfo
Water
Electricity
Data
TVMedia
RadioMedia
ThingPurchaseinfo
SupplierID
Thingpurchasedata
Thingpurchasedata
Datepurchased
Timepurchased
Quantity
LocationID
...
InputType
Server
ServernameID
serversupplierID
Sensor
SensornameID
SensorsupplierID
Markets that 'supply' input data
Individual Information Repository (other databases/servers)/Suppliers of input metadata
Calendar
Phonebook
Emails
Person_ID_socialdata
Contacts
Date
Time
InputType
Social media
Relation
Self
Other
Other
Mother
Father
Brother
Sister
Son
Daughter
Friend
Date
Time
Inputtype
ThingIDtype
ThingID_FMCG
ThingID_object
ThingID_Food_unit
ThingID_Food_meal
BarcodeInner
BarcodeUnit
SuppNetMass
Core Range
DoubleStacked
Sector
Category
SubCategory
Brand
MedLicenceReq
RepackedProduct
CustomerSpecific
AppearInOrdF
WEEEPerCase
DateCreated
DateLastUpdated
Manufacturer
MaxHazAllow
ManufacturerPartNumber
MinShelfLifeDCS
MinShelfLifeCustomer
CountryOfOrigin
Boxed
Shrinked
Trayed
ShelfReadyPackaging
Glass
SealedContainers
ProductCrushable
ProductStrongAroma
ProductCrossContamination
DiscontinuedFlag
ExclusiveToDCS
AppearInOrderFor
LocationBBEDate
LocationBatchCode
WastePaperCard
WasteGlass
WasteAluminium
WasteSteel
WastePlastic
WasteOther
HAT Project: VALUE CREATION SCHEMA (STRAWMAN)
GPS
locatorID
Date
Time
Unitmeasure
Inputtype
Person_ID_emotion
Happy
Sad
Date
Time
InputType
ThingID_object
Date
Time
Open/On
Close/Off

InputType
ThingID_food_unit
Inputtype
Itemname
ItemNo
ItemDescription
PackDescription
ProductGroup
QtyPerPack
Expiry date
Retailer databases/CRM systems
Smarthomes/Architectural apps/sensors/databases
Quantified self apps/devices
Diary/Calendar apps
Developers
Derive apps/algorithms
Derive Inventory apps
Derive Visualisation apps
Derive Analysis/Intelligence/prediction/advice apps
Scenarios

LivingRoom
spaceattributes
EntityType
Inputtype
Spaceattribs
Areaofspace
Heightofspace
indoororoutdoor
levelfmground
levelfmsea
Date
Time
inputtype
Private & Confidential: Please do not share
IoT sensors/objects
Household appliances
I_MSP_person
Contacts
Value Creating Contextual Parameters
Intermediaries
Intermediate between individual personal data and supplier
Derive matching apps
Retailers
Replenishment
Innovation
Rebundling/personalisation/customisation
Markets that act on/transform/'buy' personal data
Individual (personal data holder)
ThingID_foodmeal
Item 1
Item 2
Inputtype
Item 1
Mineral 1
Mineral 2
Calories
InputType
MyFitnessPal
Activities monitoring apps
Nike
Runkeeper
Thing
Databases
& supply chain
Utility
Phone
SatNav
UtilityChannel
EntityType_thing
Utility Retailer databases
UtilityIDTypePurchaseinfo
SupplierID
UtilityIDTypepurchasedata
UtilityIDTypepurchasedata
Datepurchased
Timepurchased
Quantity
...
InputType
VC=Value Creation

1. DATA INPUT
Individuals
Firms
2. DATA OUTPUT
Individuals
Firms
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


Some background
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)
Demand (customers)
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
PARAMETER SEPARATION
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
E.g.
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
3-6months
Full ethnographic study, qualitative interviews
Data analysis - qualitative and quantitative

Purpose:
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
Some background
Some background
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
Current Status
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
621MH

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
scalability
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
Firm-side efficiency
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

.......cue demonstrator
Market Making function of the HAT
621MH
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
Thank you

Download the briefing paper!
Introduction
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
Uh Oh.
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
HAT Platform
Provider
HAT Platform
Provider
HAT
Foundation
Developers
Firms
Makers
Makers
Firms
Developers
HAT users
HAT users
HAT communities
HAT communities
sell apps
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
Storefront catalogue
HAT Kernel
HAT Kernel
Updates
Updates
30%
10%
10%
for purchase by other platform HAT users
Full transcript