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1  sur  70
CHARTING
THE COURSE
Flickr Credit ~halldororn
USING DATA IN THE MUSEUM TO
EXPLORE, INNOVATE, AND REACH NEW
AUDIENCES
Robert Stein
Dallas Museum of Art
@rjstein
1. SCALE
2. MAKING FRIENDS
3. DATA IN THE ARTS
4. MISSION IMPACT
5. COLLABORATION
DALLAS
TEXAS
Flickr Credit ~cliffbaise
7%Dallas = 7.2M People - 485k 10yr Avg Attendance
SCALE
The emergent behavior of groups is
composed from the actions of individuals
SCALE
One European Starling
SCALE
Five European Starlings
SCALE
One Hundred Thousand European Starlings
SCALE
Large systems exhibit unique properties that form a
context for the individual
Tara Donovan, Untitled, 2014. acrylic and adhesive
Tara Donovan, Untitled, 2014. acrylic and adhesive
Tara Donovan, Untitled, 2014. acrylic and adhesive
Tara Donovan, Untitled, 2014. acrylic and adhesive
MUSEUMS ENGAGE
WITH INDIVIDUALS
BUT RARELY DO
WE THINK ABOUT
ENGAGEMENT
WITH THE
ENTIRE AUDIENCE
HOW CAN WE DO BETTER
THAN 7%?
MUSEUMS DELIVER
INTANGIBLE IMPACT
HOW DO YOU MEASURE
WHAT YOU CAN’T SEE?
Flickr Credit ~liquoredonlife
WHAT IF WE COULD BUILD A
SOPHISTICATED LISTENING TOOL
THAT WAS SPECIALLY TUNED TO
RECORD MUSEUM IMPACT?
CAN DATA HELP CREATE
A SMART MUSEUM THAT IS
AGILE AND RESPONSIVE?
METRICS
WE CARE
ABOUT
1.Repeat visits
2.Diverse participation
3.Increased affinity
4.Ability to motivate action
MAKING
FRIENDS
KIOSK EXPERIENCE
ACTIVITIES AND BADGES
REWARDS
ACTIVITY STREAM
BACKEND - METRICS
BACKEND - SEARCH
EMAIL INTEGRATION
EMAIL INTEGRATION
RECOMMENDATION
ENGINE
API
GET THE CODE
http://badgeos.org
http://github.com/DallasMuseumArt/DMA-Friends
A VERY LARGE SAMPLE
Bobby
http://dma.org/friends/by-the-numbers
http://dma.org/friends/by-the-numbers
BIG DATA
1.7M Rows of activity data
> 100k Users
> 20 Fields Each
… Big for Museums Anyways
FORGET THE
MATHS
SHOW ME
SOME
RESULTS
103,000 FRIENDS
97% of Friends are
New to the DMA
$5M+ support for
Friends / Free
+48%
485
540
680
720
0 400 800
FY12
FY13
FY14
FY15
Thousands
ATTENDANCE
10%Dallas = 7.2M People - 720k Projected FY15 Attendance
Up from < 7% in FY12
ATTENDANCE
Have You Been to the Museum Before?
39%
16%
16%
8%
21%
First Visit
Once Before
2-5 Visits
6-10 Visits
More than 10 Visits
0% 10% 20% 30% 40% 50%
2015
4%
11%
25%
20%
11%
29%
Never
1 time
2-3 times
4-6 times
7-10 times
11 or more
0% 5% 10% 15% 20% 25% 30% 35%
2008
Age at the DMA and in DFW
14.0%
14.3%
14.7%
14.6%
16.8%
27.5%
12.59%
7.16%
9.36%
21.48%
46.42%
2.72%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
60<
50-59
40-49
30-39
18-29
17>
Age DMA
Age DFW
Age at the DMA in 2008 and
Today
12.59%
7.16%
9.36%
21.48%
46.42%
2.72%
0% 10% 20% 30% 40% 50%
60<
50-59
40-49
30-39
18-29
17>
2015
18%
22%
17%
17%
16%
9%
0% 5% 10% 15% 20% 25%
65+
54-64
45-54
35-44
25-34
<25
2008
*note – the scale of ages surveyed in 2008 differ from the ages sampled in 2015
Race DMA in 2008 and Today
14%
2%
9%
13%
62%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Multiple Races
American Indian
Asian
Black/African American
White
2015
2%
1%
2%
4%
85%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Other
American Indian
Asian
Black/African American
White
2008
Race at the DMA and in DFW
2.5%
0.1%
5.2%
0.5%
14.4%
69.8%
13.77%
0.50%
9.35%
1.56%
13.25%
62.08%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Multiple Races
Haiiwan/Pacific Islander
Asian
American Indian
Black/African American
White
Race DMA
Race DFW
Ethnicity at the DMA and in
DFW
26.6%
26.88%
6.00%
0% 5% 10% 15% 20% 25% 30%
Hispanics DFW
Hispanics DMA
Hispanics DMA 2008
SCALE
Collaboration at
Tara Donovan, Untitled, 2014. styrene index cards, metal, wood, paint and glue
SCALE
Collaboration at
Tara Donovan, Untitled, 2014. styrene index cards, metal, wood, paint and glue
SCALE
Collaboration at
Tara Donovan, Untitled, 2014. styrene index cards, metal, wood, paint and glue
Flickr Credit ~Donkeyhotey
WHAT MIGHT A GLOBAL ENGAGEMENT
NETWORK LOOK LIKE?
Image courtesy of seoultown.blogspot.com
JOIN US!
@rjstein
Tara Donovan, Untitled, 2014. styrene index cards, metal, wood, paint and glue

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Charting the Course: Using Data in the Museum to Explore, Innovate, and Reach New Audiences

Notes de l'éditeur

  1. Is it possible to create a platform for engagement? Can such a platform support the diverse business models and unique expressions of engagement at a variety of museums?
  2. Summarize issues connected with data-size Talk about tools used and their relative advantages Chartio Tableau Python + pandas, numpy, scipy, etc…
  3. Front-of-house recruiting successes Motivate visits to the permanent collex Geographic distribution of participation (infer census) Call and response in the data (exhibitions) Repeat Visits