Slide deck from the 2019 Museweb Conference in Boston, Massachusetts. Session date: April 4, 2019. Title: The Data-driven Museum: Data, Dashboards and Determination Session Description: One of the most challenging transitions a 21st century organization will face is becoming data-driven – maximizing innovation and advancement while controlling risk by using real data to inform and take decisive action. We can implement an infrastructure to support the capture and visualization of data, but the heavy-lifting is in determining the best metrics, how to interpret their meaning, and most-crucially changing workplace culture to act on what the data and insights tell us. This session will explore how to become a data-informed/data-driven institution, with real world examples drawn from museum practice. A data-driven organization needs trusted and useful tools, good data quality, and the ability to operationalize analytics to drive action – ideally with an elegant and easy-to-use dashboard that pulls most of that together in one place. That’s actually the easy part – becoming a data-informed or data-driven organization is considerably harder. We see resistance to establishing a data-driven culture in our organizations, including: data doesn’t tell the whole story, quantitative metrics can never be used to measure qualitative outcomes, using data to inform decisions means eliminating the human element that makes our organizations great. However, a data-informed organization is one that augments – rather than replaces – existing decision-making processes. By committing to a data-informed culture, an organization enhances its understanding of, and service to, its audiences. During this session, panelists will discuss how their institutions are collecting and using data effectively, including: methods of data analytics, the questions being answered, data dashboards, and the difference between being data-driven and data-informed? Attendees will learn how various institutions are becoming data-informed/data-driven, with stories of successes and common obstacles they have faced, and plenty of room for questions from attendees.

1. Heather Hart, VP of Information Technology, Huntington Library, Art Collections, and Botanical Garden
Jane Alexander, Chief Information/Digital Officer, Cleveland Museum of Art
Douglas Hegley, Chief Digital Officer, Minneapolis Institute of Art
Nik Honeysett, CEO, Balboa Park Online Collaborative
The Data-Driven Museum:
Data, Dashboards and Determination
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Introductions
Winslow Homer (1858)
Thanksgiving Day - Arrival at the
Old Home (detail). Gift of Dr. and
Mrs. Robert D. Semsh. P.82.40.117
Minneapolis Institute of Art

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Heather Hart
hhart@huntington.org

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Jane Alexander
Chief Digital Information Officer
The Cleveland Museum of Art

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@dhegley
bona fides >
Image source: https://freshspectrum.com/swimming-in-data/

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Nik Honeysett
@nhoneysett

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Today’s Session
Given: We collect data - plenty of it
Given: We aim to use data effectively
Thus what really matters is:
● Asking the right questions
● Making decisions informed by data insights
● Changing workplace culture
● Changing workplace culture

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Hard Enough
● Systems and infrastructure
● Analysis and statistics
● Visualization
● Data privacy and security
Image source: https://www.netio-products.com/en/content/smart-it-infrastructure-watchdog

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Hard Enough
● Systems and infrastructure
● Analysis and statistics
● Visualization
● Data privacy and security
Harder Still
● Choosing what data to collect
● Choosing the best metrics
● Interpreting & communicating results
Image source: https://stackoverflow.com/questions/39171289/rendering-huge-dataset-from-api-to-table

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Hard Enough
● Systems and infrastructure
● Analysis and statistics
● Visualization
● Data privacy and security
Harder Still
● Choosing what data to collect
● Choosing the best metrics
● Interpreting & communicating results
What’s REALLY hard
● Changing workplace culture
● Actions driven by data insights
(instead of intuition or tradition)
Chart source: http://www.analyticshero.com/2012/12/04/data-driven-design-dare-to-wield-the-sword-of-data-part-i/

11. Part 1: Changing Workplace Culture by
Using Data to Minimize Risk
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17. ...and the CMA collects a lot of data
17

18. But what is this DATA telling us?
18

19. What Do We Want to Know?
● Where Are People Going?
● How Long Are They Staying There?
● Where Do They Go Next?
● What Are They Taking Away?
19

20. Analyzing Visitor Experience with Meraki
Wireless Access Points
20

21. We Have “Big Data”
● 105 wireless access points
● 1-2 million rows of data per day
● 0.4 GB per day just for search indexes
● A system of 8 separate cloud services
(plus Tableau) to process it all
21

22. Yayoi Kusama: Infinity Mirrors Exhibition
22

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24. 24

25. 25

26. How do we use this information to go from data
informed to data driven? 26

27. How to Trust Data and Implement Change
● Verifying data with other sources allows internal staff to
trust the data in the dashboard
○ Verified Kusama data with ticket scans, Traf-Sys
numbers, and asking visitors about experience
● Tie together quantitative and qualitative to get full picture
27

28. Where Do We Go From Here?
● AB Testing for exhibitions
○ Trying different entry points to exhibition
○ Change location of signage
○ Adapt to the visitor experience
28

29. Build Trust, Minimize Risk,
Gain Momentum, Drive Change
29

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Part 2: Changing Workplace Culture by
Using Data to Plan Proactively
- Projecting attendance and attrition
- Using data insights to create mini-
experiments
- Rapid testing of small moves can build
into big results

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Part 3: Changing Workplace Culture by
Using Data to Improve KPIs
Image source: https://www.rhythmsystems.com/blog/5-reasons-
why-you-need-kpis-infographic

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NPS:
Promoters minus Detractors
76 - 7 = 69
Range -100 to 100
Above 0 is good
Above 50 is excellent
Above 70 is world class
Comparisons (2019):
Walmart -4
Williams Sonoma -2
Nordstrom 11
Adobe 25
Zappos 57
Starbucks 77
Source: https://customer.guru/net-
promoter-score/benchmarks

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Image source: https://blog.v-comply.com/overcome-resistance-change-organizations/
Part 4: Changing Workplace Culture
Dealing with Resistance to Change

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Resistance to change
For example
“Data doesn’t tell the whole story” - or “We just
need more data” (ad infinitum)
Image source: https://www.videoblocks.com/video/no-disallowing-middle-aged-man-waving-finger-white-background-b9hz70xcitv4w30n

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Resistance to change
For example
“Data doesn’t tell the whole story” - or “We just
need more data” (ad infinitum)
“Quantitative metrics can never be used to measure
qualitative outcomes”
Image source: https://www.narconon.org/blog/the-truth-about-denial.html

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Resistance to change
For example
“Data doesn’t tell the whole story” - or “We just
need more data” (ad infinitum)
“Quantitative metrics can never be used to
measure qualitative outcomes”
“Using data to inform decisions eliminates the
human element that makes our organizations so
great”
Image source: https://englishandimmigration.com/canadian-english/how-to-speak-in-canada/body-language-in-canada/

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Why do people resist change?
1. Loss of control
2. Excess uncertainty
3. Unpleasant surprise, decisions imposed suddenly
4. Everything seems different
5. Loss of face
6. Concerns about competence
7. More work
8. Ripple effects that disrupt others, so they push back
9. Past resentments
10. Sometimes the threat is real, jobs may be in the line
Source: Ten Reasons People Resist Change, Harvard Business Review

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What helps?
1. Loss of control - involve people, give them real choices (agency)
2. Excess uncertainty - plenty of clear communication, transparent process
3. Unpleasant surprise, decisions imposed suddenly - seek input, communicate
4. Everything seems different - iterate, plan for the pace and breadth of change
5. Loss of face - celebrate how the past laid the foundation for growth
6. Concerns about competence - provide info, training, support, mentoring
7. More work - reward and recognize
8. Ripple effects that disrupt others, so they push back - wide view of “stakeholders”
9. Past resentments - don’t forget to heal the past
10. Sometimes the threat is real - be honest, transparent, fast, and fair
Source: Ten Reasons People Resist Change, Harvard Business Review

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A data-focused organization
● Augments, rather than replaces
● Existing decision-making
● By committing to a data-driven culture
Therefore
● Enhancing understanding of - and service to - our
audiences
● And driving toward success
Part 5: Changing Workplace Culture
Where We Stand Now

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Thank you! Questions or Comments?
One last caveat for today: Beware spurious correlations!