Presented by Nathan Fay and Erika Roach at 16NTC Conference: "Hear how employees at the Lucile Packard Foundation for Children’s Health at Stanford brought data analytics and Tableau to their organization. We will discuss approaches to creating cultural change with respect to new technology adoption: establishing a need, gaining influence and credibility, and demonstrating the value to organizational leaders. Building on this framework of cultural change, we will also discuss how to scale up your analytic culture with best practices and how to create a roadmap for success."

1. March 24, 2016
Forging Cultural Change:
Transforming Your
Organization Into a Data-
Driven Machine
Nathan Fay Erika Roach
Director, Data Analytics Data Analytics & Prospect
& Prospect Research (DAPR) Research Assistant (DAPR)
Lucile Packard Foundation for Children’s Health
#16NTCdataculture

2. 2
HOUSEKEEPING
• Materials & Collaboration Notes
http://po.st/dataculture-16NTC
• Our Hashtag: #16NTCdataculture
Friendly Reminder:

3. 1. Introduction
2. The Phases
3. The Catalyst
4. The Bottom Line
5. The Need
6. What & Why Visualization?
7. What & Why Dashboards?
8. Implementation
9. Dashboards
10. Further Momentum Scale Up
11. Data Philosophy
Image source: http://www.sganalytics.com/images/da-visual-analytics.png
PRESENTATION OVERVIEW
3

4. Nathan Fay, Director Data Analytics and Prospect Research
Erika Roach, Data Analytics and Prospect Research Assistant
• Provides business intelligence to each fundraising unit for
maximum ROI
• Dashboards
• Reports and Analysis
• 1:1 consultations
• Identifies prospects for each fundraising unit
• Wealth ratings
• Proactive and reactive research
4
INTRODUCTION

5. 5
PHASES
• Phase 1: Establish the Need
• Phase 2: Identify the Medium
• Phase 3: Build Infrastructure
• Phase 4: Further Momentum and Scale Up

6. Founding member of organization retired
• Senior Fundraiser
• Senior Executive
• Vast Knowledgebase
6
CATALYST
Changes to Organization:
• Foundation Growth/Maturation
• DAPR under new direction
• Loss of largest repository
of institutional memory

7. as.set (n.)
In financial accounting, an asset is an economic resource. Anything tangible or
intangible that can be owned or controlled to produce value and that is held to have
positive economic value is considered an asset.
7
INFONOMICS
What are some of a Non-Profit’s
largest assets?
Focus on the bottom line

8. 8
INFONOMICS
Endowment People
Mission Brand

9. 9
Data as an Asset means:
• Must be taken seriously
• Need for data
philosophy/policy
• Value increases more collected
+ accessible
• Value decreases when not
collected, not accessible
• Skilled Data Scientists are
necessary in order to
maximize realization of value
INFONOMICS

10. 10
POWER OF DATA

11. Need for more data
due to:
1. Staff Turnover
2. Gaps in institutional memory
3. Analytics for Foundation
growth and planning
4. Analytics for new campaign
planning
5. Analytics for Board Reports
THE NEED

12. 12
• Humans are visual creatures
• 65% of us are visual learners
• Visual forms more efficient than lists
• High encodability and intelligibility
• Time to Insight
• We quickly extract patterns, detect outliers and
change, visually
WHY VISUALIZATION

13. 13
Image source: http://en.wikipedia.org/wiki/Charles_Joseph_Minard#/media/File:Minard.png
Joseph Minard (1781-1870) Early Data Visualization pioneer
FAMOUS DATAVIZ

14. What is Visualization?
• Technique to communicate by encoding
information in a visual object (graph)
Image source: https://www.whitehouse.gov/sotu
14

15. 15
What is a Dashboard?
• “A dashboard is a visual display of the most important
information needed to achieve one or more objectives;
consolidated and arranged on a single screen so the information
can be monitored at a glance.”
(Stephen Few, Perceptual Edge, March 2007)
• “A performance dashboard is a multilayered application built on a
business intelligence and data integration infrastructure that
enables organizations to measure, monitor, and manage business
performance more effectively.”
(Wayne W. Eckerson, Performance Dashboards, 2006)
DASHBOARD

16. • Need for more data-driven decisions:
• Growth & Planning
• Achieving a high ROI
• Improved Resource Allocation
• Supports Discovery & Prescription
• What is happening?
• Why is it happening?
• What should we do?
16
Prescriptive
“What should happen?”
(Data-Driven Problem-Solving)
Predictive
“What’s likely to happen?”
(Forecasting)
Descriptive
“What is happening?”
(Dashboarding)
WHY DASHBOARD

17. Power of a Dashboard:
• Dynamic and
Interactive
• Performance metrics
are tracked against
goals
• At-a-glance
Understanding
• Time saving
Image source: http://www.bussedesign.com/sites/default/files/clients-
images/cs_tableau_screen3.jpg
17
WHY DASHBOARD

18. Data visualization software
considerations:
1. Fits with growth plan
2. Integrates with database
(data warehouse)
3. Easy-to-use
4. Maximizes resource use
18
VENDORS

19. • Founded by Stanford
graduates
• Data visualization
software
• Data exploration and
analysis in real-time
• “Data to the People”
19
TABLEAU

20. • Tableau Online
– Cloud
– Mobile enabled
– Does not support data cube
• Tableau Server
– Reports server via browser
– Mobile enabled
• Tableau Desktop
– Reports packaged for Tableau Reader
– Personal & Professional editions
20
• Tableau Interactor
– In-between desktop and reader
• Tableau Reader (Free)
– Report reader from Tableau Desktop
workbook
– View, filter, sort, export, print
• Tableau Public (Free)
– Cloud
– Limited datasources and records
– Social network sharing
Tableau Ecosystem
ECOSYSTEM

21. Tableau Datasources
• Multiple datasources to one dashboard
21
Local Files
Relational
Databases
Analytic
Databases
Data
Appliances
Data Cubes NoSQL Web API Other
MS Excel Firebird
Actian
Vectorwise
IBM Netezza Oracle Essbase
Cloudera
Hadoop
Google
Analytics
ODBC
MS Access IBM DB2
EMC
Greenplum
Teradata
MS Analysis
Services
Hortonworks
Hadoop Hiva
Google Big
Query
Text files (txt, csv) MS SQL Server ParAccel SAP Hanna MS Powerpivot
MapR Hadoop
Hive
ODATA
Tableau Bookmark
file (tbm)
MySQL SAP Sybase IQ
DataStax
Enterprise
Salesforce
Tableau Data
Extract file (tds)
Oracle HP Vertica MS Azure
SAP Sybase Aster Database
Amazon
Redshift
DATASOURCES

22. 2010 CENSUS
DATA
DataBlend
Households
and households
with children
under 18
DATABLENDING

23. 23
THE RESULT
Key to Success:
Full support from
Senior Leadership
achieved
• Department name changed from Prospect Research to
Data Analytics and Prospect Research (DAPR)
• Creation and implementation of dashboards made a
priority for all departments; established as board goal
• Green light for Business Intelligence software
• Additional member added to DAPR
• New partnership established between DAPR and
DevServices

24. Building out Infrastructure
Determine
key metrics
Database
overhaul
Departmental
prep
Data-
driven
Insights
24
IMPLEMENTATION
Educate your end user

25. 25
EDUCATION
https://www.youtube.com/watch?v=oKprr3tEB
ew

26. Patient
Dashboards:
Insight
Datamarts:
The bridge between database and
Tableau; reorganizes data so that
dashboards are possible
Database Systems:
Mini systems that exist within database that add
additional layers of structure; Proposals and Actions
systems are examples
Database:
Provides structure for data; codifies, organizes and arranges
Data:
Elements born from activity
LPFCH Staff & Donor/Prospect Activity
DATA PYRAMID

27. Patient
INSTITUTIONAL MEMORY

28. Patient
INSTITUTIONAL MEMORY
Database is main repository
• It’s powerful
• Combines collective
experiences of many
stakeholders into one
location
• Can plug in and get benefit
of years of experiences from
multiple sources in one
place
Keys to Success
• Access
• Participation

29. • Identify key metrics
– What will help you manage your workflow? Strategy/Planning?
– What are your goals?
– What activity do you want to see from your direct reports?
29
IMPLEMENTATION

30. • Determine location, existence and state of data
– Where does your data live? How is it arranged?
– Do you currently have reports/systems that you rely on?
– Does the current structure of your data allow for optimal reporting?
30
IMPLEMENTATION

31. 31
0
DATA MAP

32. • Create the underlying foundation
– Create/edit/finalize coding in the database
– Less is more; don’t overdo the system with too much complexity
32
IMPLEMENTATION

33. 6
8
4
8
PROPOSAL DASHBOARD

34. Patient
Fundraiser
1
Fundraiser
2
Fundraiser
3
Fundraiser
8
Fundraiser
4
Fundraiser
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Fundraiser
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Fundraiser
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GOAL DASHBOARD

35. Patient
Fundraiser 1
Fundraiser 1
Fundraiser 1
ACTIVITY DASHBOARD

36. Patient
ACTIVITY DASHBOARD

37. Patient

38. Patient

39. 39
PHASE 4
Phase 4: Further Momentum
and Scale Up

41. 41
New Data Philosophy:
Data is a highly valued asset
that serves as our institutional
memory and informs business
intelligence. The data generated
from our actions adds
tremendous value to the
foundation and strengthens our
ability to make sound business
decisions.
LAUNCH PARTY

42. 42
LAUNCH PARTY

43. 43
NEW DILEMMA
Quick Facts:
• Number of LPFCH employees: 80
• Number of dashboards: 359
• Number of charts: 897
• Number of people on Data Analytics &
Prospect Research Team…
Goal: Promote widespread adoption of Tableau throughout LPFCH

44. 44
Scaling for size and need through Tableau Power User Group
SCALING UP

45. 45
Tableau Power User Group
• Select data savvy volunteers
across 5 departments to join
Tableau User Group.
• Meet twice a week for 1 hour
training workshops; then
monthly
• Grant direct access to their
dashboards/allow self-
service dashboards light
NEW SOLUTION

46. • The Lucile Packard Foundation for Children’s Health
values its data. It understands the impact data has on its
operations. The Foundation applies the appropriate
resources necessary to ensure that its data be robust,
reliable, accessible and secure.
46
DATA PHILOSOPHY
• Every employee will successfully
complete the requisite database
training for their position
• Each department will take ownership
over their data
• Each department will vigorously record
their data in the database, and, with
the help of DAPR and DevServ, ensure
that its systems are optimal for
recording and reporting

47. Forging Cultural Change:
Transforming Your Organization
Into a Data-Driven Machine
March 24, 2016
Thank You
Evaluation Link
http://po.st/aziSRt
Nathan Fay Erika Roach
Director, Data Analytics Data Analytics & Prospect
& Prospect Research (DAPR) Research Assistant (DAPR)
Nathan.fay@lpfch.org Erika.Roach@lpfch.org
Lucile Packard Foundation for Children’s Health
#16NTCdataculture