Understanding Relevance

TTI / Vanguard  Understanding Understanding Conference Pittsburgh  3 October 2012 Understanding Relevance Hugh Dubberly Dubberly Design Office Iden...
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TTI / Vanguard  Understanding Understanding Conference Pittsburgh  3 October 2012

Understanding Relevance Hugh Dubberly Dubberly Design Office

Identity for a person

Relevance The right resources in the right amounts at the right time in the right place

or team

=

Context

+

to accomplish the tasks at hand without disruption or loss of flow

Dubberly Design Office · Understanding Relevance · 3 October 2012

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Understanding relevance is already a big business.

Dubberly Design Office · Understanding Relevance · 3 October 2012

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A successful bid for the search term “is auto insurance required” through Google Adwords can cost $143 per click – that’s 1 click.

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Our models of relevance are still pretty crude even at the leaders – Amazon, Facebook, and Google.

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Understanding relevance promises a new era of CRM. (customer relationship management)

Relevance transforms advertising from interruption to something more like assistance.

Fading

Today’s standard

Emerging

Mass

Personalized

Conversation

Sift through broadcast streams

Search the web for things you want

Interesting things find you

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Understanding relevance promises a new era of UX. (user experience)

Relevance expands UX from simple interaction to something more like conversation.

Fading

Today’s standard

Emerging

Command line

Direct manipulation

Delegate to agents

Remember + type

See + point

Ask + tell

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Better understanding of relevance is coming  from several simultaneous revolutions.

Sensors

Networks

Combinatorial Innovation

Cloud-based Computing Dubberly Design Office · Understanding Relevance · 3 October 2012

Mobile Computers 8

Sensors will be ubiquitous – at checkpoints – logging everything you do online – all around you – on you Identity – in you

Context

Relevance Community

Goals

Conversati

on

You

Data Tracking + Analysis

Measures

Actions

Effects

Sensors

Dubberly Design Office · Understanding Relevance · 3 October 2012

Information + Tools + Coaching

Disturbances

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Sensors are being printed – like micro-processor chips; quantities are increasing; prices are dropping.

Understanding Relevance

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Sensors are connecting – forming mesh networks. Each vine has a sensor; each sensor talks to the next; hubs connect to the internet, providing a heat and humidity map.

Dubberly Design Office · Understanding Relevance · 3 October 2012

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The same story is playing out in healthcare; with many companies offering home network hubs, connecting medical diagnostic devices to the internet.

Alere

Philips

Philips

Intel

DLM212

Telestation

Motiva (displayed through TV)

Health Guide PHS6000

Care Innovations

Care Innovations

Care Innovations

GrandCare Systems

Guide Tablet

Connect

QuietCare sensors and data communicator

Grandcare Interactive

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Healthcare hubs are one of many types of home network hubs; for now the market is expanding, but standards are emerging, and network effects mean consolidation is inevitable. Security

Media

Appliance/Energy

Routers

ADT

Logitech

GE

Apple

Pulse Home Security

Harmony 1100 Advanced Universal Remote

Nucleus Energy Manager

Airport Extreme

GE

Control4

Nest

Cisco

FrontPoint Security Touchscreen

7” In-Wall Touch Screen with Camera

The Learning Thermostat

Linksys EA4500

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Expanding networks deliver rapidly growing data streams for processing by massive cloud-based computer systems which deliver the results almost anywhere.

Sensors Dubberly Design Office · Understanding Relevance · 3 October 2012

Cloud-based Computing

Mobile Devices 14

What does this mean? Massive data collecting – organized into a taxonomy of personal identity Income + Expenses Liquid Assets Possessions Resource Use Insurance

Schools + Courses Employees + Jobs Skills + Expertise

Work Finance

Interests

Civic Voting + Registration Juries + Offices Licenses, Visas, Passports Taxes, Fees, Finances

Wellbeing

Grooming + Prevention Body Systems Emotions + Affect Exercises + Diet

Social

Family + Friends Roommates + Neighbors Colleagues + Acquaintances Dubberly Design Office · Understanding Relevance · 3 October 2012

Hobbies Create/Participate Media Curate/Coach × Performances Comment Sports Consume

Meetings Conversations × Agreements Transactions 15

Drilling into a sub-categories shows the potential for detail;

today lab tests can measure over 150 analytes; more are in development.

Pituitary gland

(hydrocortisone)

Normal, PM: 3–17 µg/dL

17 Hydroxyprogesterone * See also: ovaries

Man, normal: .06–3 mg/L Woman (follicular phase), normal: .2–1 mg/L

Angiotensinconverting enzyme (ACE)

Normal: 23–57 U/L

Growth hormone

At peak: 5–45 ng/mL Between peaks: < 5 ng/mL

Follicle-stimulating hormone (FSH)

Prepubertal: < 1 – 3 IU/L Adult male: 1–8 IU/L Adult female (follicular & luteal phase): 1–11 IU/L Adult female (ovulation): 6–26 IU/L Post-menopausal female: 30–118 IU/L

Adrenocorticotropic hormone (ACTH)

Normal: 20–80 pg/mL

Prolactin

Female, normal: < 20 ng/mL Male, normal: < 15 ng/mL

Blood Glucose

Hypoglycemia: < 3 mmol/l Normal: 3.6–5.8 mmol/l Normal, post-meal: 7 mmol/l (chronicly)

Luteinizing hormone (LH)

Female (peak): 20–75 IU/L emale (post-menopausal): 15–60 IU/L

Insulin absorption

-

Plasma osmolality

Normal: 275–295 mOsm/kg

Work

ce

Interests Grooming + Prevention

c

Wellbeing Social

Body Systems

Cardiovascular

Blood

Emotions + Affect Exercises + Diet

Dubberly Design Office · Understanding Relevance · 3 October 2012

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Identity changes over time.

Sc

Se

rvi

ce

s/A

cc

ou

Dubberly Design Office · Understanding Relevance · 3 October 2012

ore

nts

/Cr

Act

s

ed

en

tia

ivit

ies

ls

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Activities take place within contexts. Physical Contexts – Who (actors) – What (resources) – Where (location) – When (time) Why How

Goals Tasks

Cultural Contexts (task domains) – Languages (vocabulary) – Concepts (knowledge) – Methods (skills) – Rules (norms)

Recent Dubberly Design Office · Understanding Relevance · 3 October 2012

Current

Anticipated 18

The space of possible services is sparsely populated.

Goals/Tasks/Queues – Opportunities – Risks

Civic

Finance

Kickstarter

Motif

Status / Alerts – Strengths – Weaknesses Records – Successes – Failures

Dubberly Design Office · Understanding Relevance · 3 October 2012

Work

Interests

Wellbeing

Netflix Queue Amazon Wish List Air BNB

Online Wallets Square

Mint

Social

Facebook

Linkedin Resume

Evernote Amazon Purchases

EMR/PHR

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Doc Searls predicts a new order in which you control your data; CRM gives way to VRM and you control your vendor relationships. “In the not-too-distant future, you will be able, for example, to change your contact information with many vendors at once, rather than many times, over and over, at many different websites. You will declare your own policies, preferences and terms of engagement—and do it in ways that can be automated both for you and the companies you engage.”

—”The Customer As A God,” WSJ, July 21, 2012 Dubberly Design Office · Understanding Relevance · 3 October 2012

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That’s the emerging future, but where are we today?

Dubberly Design Office · Understanding Relevance · 3 October 2012

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Networked services can recognize their users and respond uniquely. recognized by

user

networked service

receives unique response from

Dubberly Design Office · Understanding Relevance · 3 October 2012

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Networked services collect information as a natural part of operating. collect

recognized by

user

networked service

data

receives unique response from

Dubberly Design Office · Understanding Relevance · 3 October 2012

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Networked services change continuously.

collect

recognized by

user

networked service

informs

data

designer

receives unique response from continuously revised by

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“... designing networked services requires a new way of thinking about a product and its development.” —Tim Misner networked service hardware

designer A

designer A

hardware

designer A

Dubberly Design Office · Understanding Relevance · 3 October 2012

networked data networkeddata service service

data

hardware

vs

vs

vs

designer B

designer B

designer B

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“...internal discussion changes from

‘what features or quality level do we think our products need?’

to

‘what data can we collect about our features and quality?’”

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With traditional hardware products, designers have limited knowledge of customer use patterns.

makes first version of

is used by

may provide feedback through

may suggest insights to

makes next version of designer/ manufacturer

hardware

Dubberly Design Office · Understanding Relevance · 3 October 2012

customer

sampling techniques - customer support call - survey - usability study - focus group - ethnographic study

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With web-based services, designers can have almost complete knowledge of customers behavior.

may provide feedback through

is used by

makes first version of

actions recorded in

sampling techniques

may suggest insights to

provide hard data for decisions

makes next version of designer/ publisher

website

Dubberly Design Office · Understanding Relevance · 3 October 2012

customer

website logs + analytics tools

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As hardware products become part of networked services, they become more like web sites.

may provide feedback through

is used by

makes first version of

actions recorded in

sampling techniques

may suggest insights to

provide hard data for decisions

sends updated software to designer/ manufacturer

hardware

Dubberly Design Office · Understanding Relevance · 3 October 2012

customer

website logs + analytics tools

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Special thanks to Paul Pangaro Michael Liebhold Michael Gallagher

[email protected] Presentation posted at www.dubberly.com/presentations/Understanding_Relevance.pdf

Appendix

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input

Dubberly Design Office · Understanding Relevance · 3 October 2012

process

output

32

goal

measure

compare

act

environment

disturbance

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goal

measure

compare

act

(set) goal

measure

compare

act

environment

disturbance

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are explained by

models and stories are tools for thinking

models

stories

models and stories are tools for discussion

create

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Human communication relies on agreement.

Do we seem to agree, that we agree?

my model of the correspondence of your model of the subject to my model of the subject (Do we seem to agree?)

my model of your model of the subject

my model of the subject

your model of the subject

me

you

subject

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Conversations may lead to trust. Trust Relationship (Trans)action Agreement Understanding

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This model describes the learning process.

SECI model of knowledge creation Ikujiro Nonaka (1995) Explicit 1

Combination Connecting

Externalization Articulating

SECI Model of Knowledge Creation

Systemizing and applying explicit knowledge and information 7. Gathering and integrating explicit knowledge 8. Transferring and diffusing explicit knowledge 9. Editing explicit knowledge

Articulating tacit knowledge through dialogue and reflection 5. Articulating tacit knowledge 6. Translating tacit knowledge

Internalization Embodying

Socialization Empathizing Sharing and creating tacit knowledge through direct experience 1. Walking around inside the company 2. Walking around outside the company 3. Accumulating tacit knowledge 4. Transferring tacit knowledge

Individual Group

Explicit 2

Tacit 2

Ikujiro Nonaka (1995)

Learning and acquiring new tacit knowledge in practice 10. Embodying explicit knowledge through action and practice 11. Using simulation and experiments

Tacit 1 Tacit 3

Organization Community of organizations Knowledge conversion spiral

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Designing is analogous to learning. SECI model of knowledge creation Ikujiro Nonaka (1995)

Analysis-Synthesis Bridge Model Dubberly, Evenson & Robinson (2008) Abstract

suggest

What “could be”

Preferred – Explicit (Future)

distilled

Internalization Embodying

Socialization Empathizing Sharing and creating tacit knowledge through direct experience 1. Walking around inside the company 2. Walking around outside the company 3. Accumulating tacit knowledge 4. Transferring tacit knowledge

Individual Group

Explicit 2

Existing – Implicit (Current)

t as

What “is”

manifes

Concrete

Systemizing and applying explicit knowledge and information 7. Gathering and integrating explicit knowledge 8. Transferring and diffusing explicit knowledge 9. Editing explicit knowledge

Articulating tacit knowledge through dialogue and reflection 5. Articulating tacit knowledge 6. Translating tacit knowledge

Model of what “could be”

to

Model of what “is”

Describe

Combination Connecting

Externalization Articulating

Tacit 2

Interpret

Explicit 1

Prototyping

Researching

Learning and acquiring new tacit knowledge in practice 10. Embodying explicit knowledge through action and practice 11. Using simulation and experiments

Tacit 1 Tacit 3

Organization Community of organizations Knowledge conversion spiral

Analysis-Synthesis Bridge Model

SECI Model of Knowledge Creation

Dubberly, Evenson & Robison (2008)

Ikujiro Nonaka (1995)

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