How to Feed Hungry, Heartless Algorithms Good Data

How to Feed Good
Data
to Hungry, Heartless
Algorithms

The Mind of a Data Scientist
• BSc in Cognitive Science
• Diploma in Communication Disorders
• Hearing Instrument Specialist
• MI (Critical Information Studies)
• Co-authored paper on Queer Theory
in Self Surveillance on Bio-Hacking &
Health Informatics
• Mediation of self through technology,
through self-surveillance & data

2012-02-03 18:35:34 SampleClass6 [INFO] everything normal for id 577725851
2012-02-03 18:35:34 SampleClass4 [FATAL] system problem at id 1991281254
2012-02-03 18:35:34 SampleClass3 [DEBUG] detail for id 1304807656
2012-02-03 18:35:34 SampleClass3 [WARN] missing id 423340895
2012-02-03 18:35:34 SampleClass5 [TRACE] verbose detail for id 2082654978
2012-02-03 18:35:34 SampleClass0 [ERROR] incorrect id 1886438513
2012-02-03 18:35:34 SampleClass4 [INFO] everything normal for id
1868490543
1916666627
2050832396
2044892337
1857885262
1 microsecond sample log file for MS HD Insights

Big Data
When there is enough data that it must be
mediated to even be loosely understood.

http://crimsonhints.deviantart.com/

https://www.ljll.math.upmc.fr/perronnet/NLSE/NLSEimpl01.png

http://infolab.stanford.edu/~backrub/google.html
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... +
PR(Tn)/C(Tn))

Data Science
The actual application of the scientific method to
business questions.

Scientific Method
A way to approach research so you don’t just
make shit up.

Data Science:
What it Looks Like in Creative
Tech

1. Work you do
2. Way it is measured
3. The way you are measured doing it

Infographics
• Visuals processed
60,000x faster than
text
• Typical recall for text
averages 20%
• So far, few show any
data science results–
but this will change
soon!

Metrics
• All digital content,
including website
performance, social
platforms etc
• Deciding what to
measure is tricky
• So far, we only
occasionally use
really cool data
science stuff- but
it’s growing fast

Tracking
• IoT & Quantified Self
in the Workplace
• Partly hype, partly
already here, partly
inevitable…
• What does a
performance review
look like in five years?

Data Science:
Why You Should Care

• Vocabulary
• Visibility
• Values

http://sculptwerks.deviantart.c
om/

http://www.kids-pages.com/folders/colpages/Fairy-Tales/magic-
wand.gif

Input Algorithm Output
Input
Algorithm
Output

Credit: Jonty Wareing
https://www.flickr.com/photos/electrichamster/

http://news.discovery.com/earth/global-warming/11-billion-people-climate-change-131122.htm

Data Will Always Be:
Incomplete
Messy
Hard To Understand

(Think “instant
magic” when you
hear “data
science”)

(Make sure you have
no idea what you
have)

(make sure you never
wonder about what
you actually need)

1. Believe all hype
2. Refuse to consider that operating facts might be
wrong
3. Bias your data all to hell and don’t even realize it
4. Forget to ask why you’re even doing this
5. Don’t understand what you have
6. Don’t know what you need
7. Don’t take the time to figure it out

1. Ask questions
2. Take a serious look at how you talk about it now
3. Take a serious look at the way you measure it
4. Look at the data you have now
5. Figure out what it means (not in this alone)
6. Decide if you’re missing stuff you need
7. Get that stuff
8. Do science!! (hypothesis, method, test, discuss, repeat)
9. SHARE
10.Present & evangelize

Complexity is a Design
Challenge
• Metaphors used to discuss data science (needle
in a haystack, mining for gold) prevent this
from being a collaborative exploration by a
whack of cool people, in a room to discuss a
problem together.
• Instead, we think of this as a very solitary,
linear, (painful, boring, difficult, awful) process.
• Which it isn’t. It’s really cool. But we need
everyone to come to the party to make it an
epic party.

Why You Should Care (Reprise)
It’s the work you do
Way it is measured
The way you are measured doing it

How to Feed Hungry, Heartless Algorithms Good Data

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How to Feed Hungry, Heartless Algorithms Good Data