Data analytics & AI.pdf

ON BEHALF OF:
Findings From the Annual Data & Analytics
Global Executive Study
Data, Analytics, & AI:
How Trust Delivers Value
C U S T O M R E S E A R C H R E P O R T

CUSTOM RESEARCH REPORT — DATA, ANALYTICS, AND AI: HOW TRUST DELIVERS VALUE
Table of Contents
Executive Summary.......................................................................................................................................................................................1
Section I: Building Trust in Data: How Analytics Leaders Get ‘the Right Stuff’...................................................................................2
• Trust Advances Analytics Maturity......................................................................................................................................................2
• Why Closing the Trust Gap Matters.................................................................................................................................................... 3
• Grade Your Data................................................................................................................................................................................... 4
• The Human Factor: Partner With Domain Experts............................................................................................................................ 5
• AI Built on a Bedrock of Data Governance ........................................................................................................................................ 5
• Commit to Treating Data as an Asset ................................................................................................................................................. 6
Section II: Success With Customer Data Depends on Keeping Customers’ Trust.........................................................................10
• The Pivotal Roles of Data Security and Privacy ................................................................................................................................10
• The Opportunity to Build Customer Trust Based on Data ................................................................................................................11
• Trust Is Fragile — Handle With Care...................................................................................................................................................11
Section III: Building Trust in Innovation by Creating a Culture of Inquiry and Experimentation......................................................15
• Driving Data Literacy Through the Workforce ..................................................................................................................................16
• Fostering Collaboration Drives Culture Change...............................................................................................................................16
• Analytics Expertise: Centralize vs. Decentralize ..............................................................................................................................17
• Communication and Education Encourage an Analytics Mindset..................................................................................................18
Leaders’ Best Practices
• Health Care – Cleveland Clinic’s Centralized Data Store Helps Build Trust in Analytics.....................................................................8
• Manufacturing – Caterpillar Tailors Analytics Strategies to Business Unit Needs.......................................................................... 9
• Government – DataSF Teaches the Art of Asking Analytical Questions ........................................................................................13
• Financial Services – Cross-functional Teamwork Improves Predictive Models at Barclays US ...................................................14
About the Research ....................................................................................................................................................................................20
Acknowledgments......................................................................................................................................................................................20
Sponsor’s Viewpoint....................................................................................................................................................................................21
MIT SMR Connections develops content in collaboration with our sponsors. It
operates independently of the MIT Sloan Management Review editorial group.
Copyright © Massachusetts Institute of Technology, 2019. All rights reserved.

1
Deriving more value from analytics and emerging technologies
èÙæÁ ĎęÙľ¸Ù èÙîęÁèèÙÓÁî¸ÁĒę ĎęĒıÙę×ęĎĝĒęɷĒÙíċèĳ·Á¸ ĝĒÁ½ ę
collected for analytics must be trusted. Much like the need for
sterility in clinical laboratories or a clear chain of evidentiary
¸ĝĒęô½ĳÙîè ıÁîÒôĎ¸ÁíÁîęɷ¸ĝĒęôíÁĎĒ î½ċ ĎęîÁĎĒę× ęĒ× ĎÁ
½ ę íĝĒęęĎĝĒęę× ęÙęʖĒĒ ÒÁÓĝ Ď½Á½ î½ĝĒÁ½ ċċĎôċĎÙ ęÁèĳÒĎôí
¸ôèèÁ¸ęÙôîę×ĎôĝÓ×ĒęôĎ ÓÁ î½ęô×ôıÙęʖĒ ċċèÙÁ½ɼôîİÁĎĒÁèĳɷ
ôî¸ÁÙîĒÙÓ×ęĒÁíÁĎÓÁÒĎôí ċċèĳÙîÓ î èĳęÙ¸Ēęôę×Á½ ę ɷÙî½Ù-
viduals throughout the organization must understand the care
given to data management so that they trust those insights —
î½ĝĒÁę×Áíʊęôí æÁ½Á¸ÙĒÙôîĒ î½ ĒæîÁıčĝÁĒęÙôîĒɼ
WĝĎÓèô· èĒĝĎİÁĳôÒíôĎÁę× îɘɷɚɖɖ·ĝĒÙîÁĒĒèÁ ½ÁĎĒ î½í î-
ÓÁĎĒ ċĎôİÙ½ÁĒ ÙîĒÙÓ×ę Ùîęô ôĎÓ îÙĺ ęÙôîĒʖ ¸ęÙİÙęÙÁĒ Ùî Á ¸× ôÒ
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ĎÁ·Á¸ôíÙîÓíôĎÁí ÙîĒęĎÁ í î½ı×ÁĎÁę×Áĳí ĳĒęÙèè·Á
ÁĲ¸ÁċęÙôî èɼ=ęÒôĝî½ę× ęĎÁĒċôî½ÁîęĒı×ô× İÁ ½İ î¸Á½ę×ÁÙĎ
î èĳęÙ¸ĒċĎ ¸ęÙ¸ÁĒęôÙî¸ôĎċôĎ ęÁ=ʌ· ĒÁ½ęÁ¸×îôèôÓÙÁĒĒĝ¸×
Ēí ¸×ÙîÁèÁ ĎîÙîÓ î½î ęĝĎ èè îÓĝ ÓÁċĎô¸ÁĒĒÙîÓıôĎæÙî
ôĎÓ îÙĺ ęÙôîĒę× ę½ôę×ÁíôĒęęôÒôĒęÁĎ½ ę čĝ èÙęĳɷĒ ÒÁÓĝ Ď½
½ ę ĒĒÁęĒɷ î½½ÁİÁèôċ¸ĝèęĝĎÁĒôÒ½ ę èÙęÁĎ ¸ĳ î½Ùîîôİ ęÙôîɼ
WİÁĎ èèɷę×ÁĒĝĎİÁĳÒôĝî½ ċÁĎĒÙĒęÁîęÓ ċɶ“×ÙèÁę×Áí äôĎÙęĳ
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ı×ôĎÁċôĎę ×ÙÓ×èÁİÁèôÒęĎĝĒęÙî½ ę ÒôĎ î èĳęÙ¸Ē ĎÁ èĒô
íôĎÁèÙæÁèĳęôĒ×ôıèÁ ½ÁĎĒ×ÙċÙîÒôĝî½ ęÙôî è ¸ęÙİÙęÙÁĒę× ę
ÁîĒĝĎÁę× ęę×Á½ ę ÙĒ×ÙÓ×čĝ èÙęĳ î½èÁ ½ĒęôĝĒÁÒĝèÙîĒÙÓ×ęĒɼ

{×ÁĒĝĎİÁĳɷ ĝÓíÁîęÁ½·ĳÙîęÁĎİÙÁıĒıÙę×èÁ ½ÙîÓċĎ ¸ęÙęÙôîÁĎĒ
î½ ÁĲċÁĎęĒ Ùî ę×Á ľÁè½ɷ Ó ĝÓÁĒ ę×Á ½ ę î èĳęÙ¸Ē ċĎ ¸ęÙ¸ÁĒ
ôÒôĎÓ îÙĺ ęÙôîĒ Ďôĝî½ę×ÁıôĎè½ î½×ôıĎÁĒċôî½ÁîęĒİÙÁı
ę×ÁÁÒÒÁ¸ęÙİÁîÁĒĒôÒę×ôĒÁċĎ ¸ęÙ¸ÁĒɼ{×ĎÁÁí äôĎ¸ôî¸èĝĒÙôîĒ
ÁíÁĎÓÁ íôîÓę×Á¸×ÙÁÒľî½ÙîÓĒɶ
1. Better data governance is needed.
íÙîôĎÙęĳôÒĎÁĒċôî½ÁîęĒ× İÁÒôĎí è ¸ęÙİÙęÙÁĒÙî½ ę čĝ èÙęĳ
ĒĒĝĎ î¸Áɷı×Ù¸×ċôÙîęĒęôę×ÁîÁÁ½ÒôĎ ÓĎÁ ęÁĎ¸ôííÙęíÁîę
ęô½ ę ÓôİÁĎî î¸ÁÙîĒĝċċôĎęôÒ ½İ î¸Á½ î èĳęÙ¸Ēɼ{×ÁċĎ ¸-
ęÙęÙôîÁĎĒÙîęÁĎİÙÁıÁ½ċĎôİÙ½ÁÁĲ íċèÁĒôÒ ċċĎô ¸×ÁĒęô½ ę
Ď¸×ÙęÁ¸ęĝĎÁɷÓôİÁĎî î¸Áɷ î½čĝ èÙęĳę× ę¸ î·ĝÙè½ęĎĝĒęÙî
data for analytics.
2. Data privacy emerges as an opportunity.
! ę ĒÁ¸ĝĎÙęĳÙĒę×ÁĒęĎôîÓÁĒęÒô¸ĝĒ íôîÓĒĝĎİÁĳĎÁĒċôî½ÁîęĒɷ
·ĝęę×ÁĎÁ ĎÁôċċôĎęĝîÙęÙÁĒęôÙî¸ĎÁ ĒÁę×Áí ęĝĎÙęĳôÒĒÁ¸ĝĎÙęĳ
ċĎ ¸ęÙ¸ÁĒ ·ĳ ċċèĳÙîÓ î èĳęÙ¸Ē î½ = Ùî ę×ÙĒ ĎÁî ɼ ! ę
ċĎÙİ ¸ĳÙîÙęÙ ęÙİÁĒ ĎÁîôęčĝÙęÁ ĒĒęĎôîÓɼ=îęÁĎİÙÁıĒ×ÙÓ×èÙÓ×ę
ôċċôĎęĝîÙęÙÁĒ ęô ċċĎô ¸× ċĎÙİ ¸ĳ ÙîÙęÙ ęÙİÁĒ î½ 7!nq Ē
ı ĳęô·ĝÙè½ęĎĝĒęıÙę×¸ĝĒęôíÁĎĒɷĎ ę×ÁĎę× îĒÙíċèĳęĎÁ ęÙîÓ
ę×Áí Ē¸ôíċèÙ î¸Áʌ½ĎÙİÁîí î½ ęÁĒɼ
3. Fostering an analytics culture improves innovation.
LÁ ½ÁĎĒ×Ùċ î½í î ÓÁíÁîęċĎ ¸ęÙ¸ÁĒę× ęĒĝċċôĎę ¸ĝèęĝĎÁ
ôÒ î èĳęÙ¸Ēʌ½ĎÙİÁî Ùîîôİ ęÙôî ĎÁ ĎÁè ęÙİÁèĳ ĒęĎôîÓɷ ·ĝę ę×Á
ĎÁĒÁ Ď¸×Ē×ôıĒí îĳôĎÓ îÙĺ ęÙôîĒ× İÁ îôċċôĎęĝîÙęĳęô½ô
íôĎÁęôĒċĎÁ ½ę×ÁîÁ¸ÁĒĒ ĎĳĒæÙèèĒ î½íÙî½ĒÁęę×ĎôĝÓ×ôĝęę×Á
ıôĎæÒôĎ¸ÁɼíÙîôĎÙęĳôÒĎÁĒċôî½ÁîęĒ ĎÁÁîÓ ÓÁ½Ùî ¸ęÙİÙęÙÁĒ
ę× ę ½ÁİÁèôċ ıôĎæÒôĎ¸Á ½ ę èÙęÁĎ ¸ĳʁ íÁ îı×ÙèÁɷ ľî½ÙîÓ
ıôĎæÒôĎ¸ÁıÙę×ę×ÁĎÙÓ×ęĒæÙèèĒı Ē¸ÙęÁ½ ĒôîÁôÒę×ÁíôĒęĒÙÓ-
îÙľ¸ îę¸× èèÁîÓÁĒęôÙîîôİ ęÙîÓɼ=îęÁĎİÙÁıĒıÙę×ċĎ ¸ęÙęÙôîÁĎĒ
Ē×ôı×ôı î èĳęÙ¸ĒèÁ ½ÁĎĒ¸ îÒôĒęÁĎ ¸ĝèęĝĎÁôÒÙîîôİ ęÙôî
·ĳÁ½ĝ¸ ęÙîÓɷ¸ôííĝîÙ¸ ęÙîÓɷ î½¸ôèè ·ôĎ ęÙîÓıÙę×ċ ĎęîÁĎĒ
in lines of business.
Organizational choices — such as centralizing the analytics
Òĝî¸ęÙôî î½ × İÙîÓ ¸×ÙÁÒ ½ ę ôÒľ¸ÁĎ ôĎ ¸×ÙÁÒ î èĳęÙ¸Ē
ôÒľ¸ÁĎĎôèÁʊí ĳ èĒô×Áèċ ½İ î¸Á î èĳęÙ¸Ēí ęĝĎÙęĳɼ{×ôĒÁ
ı×ô× İÁ !WôĎW ĎÁíôĎÁèÙæÁèĳęôĎÁċôĎęę× ęę×Áĳ
× İÁę×Á½ ę îÁÁ½Á½ÒôĎ½Á¸ÙĒÙôîʌí æÙîÓɷ Ē ĎÁę×ôĒÁı×ô
ıôĎæÙîôĎÓ îÙĺ ęÙôîĒı×ÁĎÁ î èĳęÙ¸Ē ĎÁ¸ÁîęĎ èÙĺÁ½ɼ
For leaders of organizations still striving to achieve analytics
í ęĝĎÙęĳɷ ę×Á ęÁĒęÙíôîĳ ÒĎôí ċĎ ¸ęÙęÙôîÁĎĒ ʊ ı×ô Ď îÓÁ
ÒĎôí½ ę èÁ ½ÁĎĒ ęíĝèęÙî ęÙôî è¸ôĎċôĎ ęÙôîĒęôę×Á×Á ½
ôÒ Ēí èè íĝîÙ¸Ùċ è ÓôİÁĎîíÁîę ęÁ í ʊ ÙĒ ċ ĎęÙ¸ĝè Ďèĳ
ĝĒÁÒĝèɼ {×ÁÙĎ ¸×ÙÁÒ èÁĒĒôîɶ ôííĝîÙ¸ ęÙôî î½ ¸ôèè ·ôĎ -
ęÙôî·ÁęıÁÁî î èĳęÙ¸Ē î½·ĝĒÙîÁĒĒÁĲċÁĎęĒèÁ ½ęôíĝęĝ è
ĝî½ÁĎĒę î½ÙîÓ î½ íÁ ĒĝĎ ·èÁ ·ÁîÁľęĒɼ =î ôę×ÁĎ ıôĎ½Ēɷ
trust delivers value.O
Executive Summary

2
Building Trust in Data:
How Analytics Leaders
Get ‘the Right Stuff’
W
×ÁîÁîęÁĎċĎÙĒÁÙîÒôĎí ęÙôîèÁ ½ÁĎĒ ęę×ÁèÁİÁè î½
èÙîÙ¸ĒÁęę×ÁÓô èôÒ ½İ î¸ÙîÓę×ÁôĎÓ îÙĺ ęÙôîʖĒ
½ ę î èĳęÙ¸Ēí ęĝĎÙęĳ ·ôĝęÒôĝĎĳÁ ĎĒ Óôɷę×Áĳ
× ½ èĎÁ ½ĳÁĒę ·èÙĒ×Á½ĒęĎôîÓċĎôÓĎ íĒÒôĎ½Á¸ÙĒÙôîĒĝċċôĎę
î½·ĝĒÙîÁĒĒÙîęÁèèÙÓÁî¸Áɼ{×ÁĳæîÁıę× ęÓôÙîÓ·Áĳôî½½ Ē×-
·ô Ď½Ē î½ĎÁċôĎęĒęôÓÙİÁ¸èÙîÙ¸Ù îĒ î½í î ÓÁĎĒċĎÁ½Ù¸ęÙİÁ
ʊ î½ċĎÁĒ¸ĎÙċęÙİÁʊÙîĒÙÓ×ęĒċôıÁĎÁ½·ĳ ĎęÙľ¸Ù èÙîęÁèèÙÓÁî¸Á
î½í ¸×ÙîÁèÁ ĎîÙîÓıôĝè½½Áí î½íôĎÁę× îäĝĒęĒĝċċèĳÙîÓ
the technology.
The leading academic medical center recognized that to create
¸ĝèęĝĎÁı×ÁĎÁċÁôċèÁĝî½ÁĎĒę î½ î½ĝĒÁ ½İ î¸Á½ î èĳęÙ¸Ē
ęôí æÁ·ÁęęÁĎ½Á¸ÙĒÙôîĒɷÙęîÁÁ½Á½ęôÒô¸ĝĒôîÙęĒÙîÒôĎí ęÙôî
î½½ ę ɶʓ%îĒĝĎÁę× ęÙęʖĒ İ Ùè ·èÁɷę× ęÙęʖĒİ èÙ½ɷę× ęÙęʖĒ
ÓôİÁĎîÁ½ ċċĎôċĎÙ ęÁèĳɷ ę× ę ıÁ × İÁ ę×Á ĎÙÓ×ę ċĎô¸ÁĒĒÁĒ
Ďôĝî½ ¸¸ÁĒĒÙîÓ Ùęɷ ĝĒÙîÓ Ùęɷ Ē× ĎÙîÓ Ùęɷ ċĎôęÁ¸ęÙîÓ Ùęɷʔ Ē ĳĒ
×ĎÙĒ !ôîôİ îɷ ÁĲÁ¸ĝęÙİÁ ½ÙĎÁ¸ęôĎ ôÒ ÁîęÁĎċĎÙĒÁ ÙîÒôĎí ęÙôî
management and analytics.
Trust Advances Analytics Maturity
LÙæÁôę×ÁĎèÁ ½ÙîÓôĎÓ îÙĺ ęÙôîĒċĝĎĒĝÙîÓ ½İ î¸Á½ î èĳęÙ¸Ē
¸ ċ ·ÙèÙęÙÁĒɷę×ÁèÁİÁè î½èÙîÙ¸× Ēċè ¸Á½ ċĎÙôĎÙęĳôî
building trust — trust in the data that’s collected and stored
and trust in the analytic insights it generates. And it has seen
×ôı·ĝÙè½ÙîÓę× ęęĎĝĒę¸ îĎÁÙîÒôĎ¸Á ¸ĝèęĝĎÁę× ęęĎĝĒęĒ î½
embraces data-driven decision-making.
6Ùî½ÙîÓĒÒĎôíôĝĎĎÁ¸ÁîęĒĝĎİÁĳɷı×Ù¸×Òô¸ĝĒÁ½ôîę×Á½ ę
î½ î èĳęÙ¸Ē ċĎ ¸ęÙ¸ÁĒ ôÒ íôĎÁ ę× î ɘɷɚɖɖ ·ĝĒÙîÁĒĒ èÁ ½ÁĎĒ
î½í î ÓÁĎĒɷĝî½ÁĎĒ¸ôĎÁĒę×ÁÙíċôĎę î¸ÁôÒĒĝ¸×ċĎÙôĎÙęÙÁĒɼ
“ÁÒôĝî½ ĒęĎôîÓ¸ôĎĎÁè ęÙôî·ÁęıÁÁîę×ôĒÁı×ôĎÁċôĎę
ĝĒÙîÓę×ÁíôĒę ½İ î¸Á½ î èĳęÙ¸ĒęÁ¸×îÙčĝÁĒ î½ę×ôĒÁı×ôĒÁ
ôĎÓ îÙĺ ęÙôîĒ ¸ęÙİÁèĳÒôĒęÁĎ½ ę čĝ èÙęĳɷĒ ÒÁÓĝ Ď½½ ę
ĒĒÁęĒɷ î½·ĝÙè½ ½ ę ʌ½ĎÙİÁî¸ĝèęĝĎÁɼ
{×ÁĒÁ ôĎÓ îÙĺ ęÙôîĒ Òô¸ĝĒ ôî ½ ę čĝ èÙęĳ î½ í î ÓÁíÁîęɼ
{×ÁĳĒÁęĝċíÁ ĒĝĎÁĒÒôĎÓôİÁĎîÙîÓÙęĒċĎôċÁĎĝĒÁ î½ĒÁ¸ĝĎÙęĳɷ
î½·ĳÒôèèôıÙîÓę×ÁĒÁċĎ ¸ęÙ¸ÁĒɷę×Áĳ ¸×ÙÁİÁĎÁĒĝèęĒɼ=îę×Á¸ ĒÁ
ôÒę×ÁèÁİÁè î½èÙîÙ¸ɷę× ęíÁ îĒíôĎÁĎÁĒÁ Ď¸×ÁĎĒęĎĝĒęę×Á
data they are accessing from a centralized data lab instead of
¸ôċĳÙîÓ½ ę ęôıôĎæôîÙîę×ÁÙĎôıîɷĒÙèôÁ½ĒĳĒęÁíɼ{×ÙĒèÁ ½Ēęô
íôĎÁ¸ôîĒÙĒęÁîę½ ę ʊ î½íôĎÁċĎÁ¸ÙĒÁĎÁĒĝèęĒɷ!ôîôİ îĒ ĳĒɼ

;ôıÁİÁĎɷĒĝ¸×·ÁîÁľęĒɷ·ĎÁ½ÒĎôí·ÁĒę î èĳęÙ¸ĒċĎ ¸ęÙ¸ÁĒɷ ĎÁ
ĒęÙèèîôęıÙ½ÁĒċĎÁ ½ɶWĝĎĎÁĒÁ Ď¸×Ē×ôıĒę× ęíôĒęôĎÓ îÙĺ -
ęÙôîĒ ĎÁĒęÙèè½ÁİÁèôċÙîÓę×ÁÙĎ î èĳęÙ¸Ē¸ ċ ·ÙèÙęÙÁĒɼHĝĒęɗɛʾ
ôÒĒĝĎİÁĳĎÁĒċôî½ÁîęĒĎÁċôĎęę× ęę×ÁĳĝĒÁ ½İ î¸Á½ î èĳęÙ¸Ē
ęô ÙîÒôĎí í î ÓÁíÁîę ½Á¸ÙĒÙôîĒɼ 6ÁıÁĎ ę× î ôîÁ Ùî ɗɖ ĎÁ
ıôĎæÙîÓıÙę× ĝęôí ęÁ½ î èĳęÙ¸Ēɷ î½ôîèĳɝʾ ċċèĳí ¸×ÙîÁ
learning and artificial intelligence in decision-making or
ċĎô½ĝ¸ęÙôîıôĎæĿôıĒɼ6 ĎíôĎÁ¸ôííôîɷĎÁĒċôî½ÁîęĒĎÁèĳ
ôî·ĝĒÙîÁĒĒÙîęÁèèÙÓÁî¸ÁęôôèĒ î½ÁíċèôĳÁÁ½ Ē×·ô Ď½Ēęô
ĒĝċċôĎę½Á¸ÙĒÙôîʌí æÙîÓɼ
ęę×ÁĒ íÁęÙíÁɷıÁô·ĒÁĎİÁ½ı× ę¸ôĝè½·Á¸ èèÁ½ ʓĝęÙèÙęĳ
Ó ċʔɶ “×ÙèÁ ɝɜʾ ôÒ ĎÁĒċôî½ÁîęĒ ĎÁċôĎę ę×Áĳ × İÁ Ùî¸ĎÁ ĒÁ½
¸¸ÁĒĒęô½ ę ę×Áĳäĝ½ÓÁĝĒÁÒĝèʊı×Ù¸×ÙĒîôęĒĝĎċĎÙĒÙîÓɷÓÙİÁî
ę×ÁċĎôèÙÒÁĎ ęÙôîôÒ½ ę ę× ę ¸¸ôíċ îÙÁĒę×Á½ÙÓÙę èÙĺ ęÙôîôÒ
·ĝĒÙîÁĒĒʊę× ę ¸¸ÁĒĒ½ôÁĒîôęÁčĝ èÁíċôıÁĎíÁîęɼíĝ¸×
Ēí èèÁĎîĝí·ÁĎĒ ĳę×Áĳ ĎÁ ·èÁęôèÁİÁĎ ÓÁę× ę½ ę ɶWîèĳ
ɚəʾÒÁÁèę×ÁĳÒĎÁčĝÁîęèĳ× İÁę×ÁĎÙÓ×ę½ ę îÁÁ½Á½ęôí æÁ
½Á¸ÙĒÙôîĒɼ{×ÙĒĝęÙèÙęĳÓ ċÙĒ ċÁĎĒÙĒęÁîęęĎÁî½ɷıÙę× ĒÙíÙè Ď
Ó ċÒôĝî½Ùîę×ÁMIT Sloan Management Review ĒĝĎİÁĳÙîɘɖɗɝɗ
ʈĒÁÁ6ÙÓĝĎÁɗʉɼ
While the majority of survey
respondents reported
increased access to data in
surveys conducted in 2017
and 2018, those who believe
they have the data they
need for decision-making
remain in the minority.
Figure 1: A ‘Utility Gap’ Persists
ɗ
uɼq îĒ·ôę× í î½!ɼJÙĎôîɷʓĒÙîÓî èĳęÙ¸Ēęô=íċĎôİÁĝĒęôíÁĎ%îÓ ÓÁíÁîęɷʔ
Q={uèô îQ î ÓÁíÁîęqÁİÙÁıɷH îɼəɖɷɘɖɗɞɼ
78% 76%
44% 43%
2017 2018
Percentage of respondents
reporting somewhat or significantly
improved access to useful data
over the past year
reporting frequently or always
having the right data to inform
business decisions
reporting somewhat or
significantly improved access
to useful data over the past year
reporting frequently or always
having the right data to inform
business decisions

3
Why Closing the Trust Gap Matters
“×ÙèÁę×ÁĎÁ¸ î·Á Ď îÓÁôÒĎÁ ĒôîĒı×ĳċÁôċèÁí ĳîôęÒÁÁè
ę×Áĳ × İÁ ę×Á ʓĎÙÓ×ęʔ ½ ę ęô × î½èÁɷ ôĝĎ ĒĝĎİÁĳ ċĎô·Á½ î½
Òôĝî½ÁİÙ½Áî¸ÁÒôĎôîÁɶ ęĎĝĒęÓ ċɼWîèĳ İÁĎĳĒí èèíÙîôĎÙęĳ
ôÒĎÁĒċôî½ÁîęĒĒ ĳę×Áĳʓ èı ĳĒʔęĎĝĒę½ ę äĝ½ÓÁ½·ĳčĝ èÙęÙÁĒ
ôÒ ĎÁèÁİ î¸Áɷ ¸ôíċèÁęÁîÁĒĒɷ ęÙíÁèÙîÁĒĒɷ î½ ¸¸ĝĎ ¸ĳʁ ĒèÙÓ×ęèĳ
íôĎÁę× î× èÒĒ ĳę×Áĳäĝ½ÓÁ½ ę ęĎĝĒęıôĎę×ĳ·ĳę×ôĒÁčĝ èÙęÙÁĒ
ęèÁ ĒęʓôÒęÁîʔʈĒÁÁ6ÙÓĝĎÁɘʉɼ{×ÙĒľî½ÙîÓĒĝÓÓÁĒęĒ ĒÙÓîÙľ¸ îę
ôċċôĎęĝîÙęĳęôĒ×ôĎÁĝċ½ ę čĝ èÙęĳęô·ĝÙè½¸ôîľ½Áî¸ÁÙî½ ę
ÒôĎ î èĳęÙ¸ĒɷÙîċ ĎęÙ¸ĝè Ďı×ÁîÙę¸ôíÁĒęôÙî¸ĎÁ ĒÙîÓę×Á
èÙæÁèÙ×ôô½ę× ę½ ę ÙĒ¸ôíċèÁęÁʊę×Á ĒċÁ¸ęęĎĝĒęÁ½èÁ ĒęôÒęÁîɼ
{×ÁĎÁ ÙĒ íċèÁ ôċċôĎęĝîÙęĳ ÒôĎ ôĎÓ îÙĺ ęÙôîĒ ęô ½ô íôĎÁɶ
Wîèĳ ɘɗʾ ôÒ ĒĝĎİÁĳ ĎÁĒċôî½ÁîęĒ ĎÁċôĎę ÒôĎí è ċċĎô ¸×ÁĒ
ęô ½ ę čĝ èÙęĳɷ ı×Ù¸× ıÁ ½ÁľîÁ½ Ē ĎôĝęÙîÁèĳ íôîÙęôĎÙîÓɷ
í î ÓÙîÓɷ î½ÙíċĎôİÙîÓ½ ę čĝ èÙęĳ Ēċ ĎęôÒ ÒôĎí è½ ę
ÓôİÁĎî î¸ÁÁÒÒôĎęʈĒÁÁ6ÙÓĝĎÁəʉɼ{×Áè ĎÓÁĒęÓĎôĝċÙĒĎÁ ¸ęÙİÁ
ęôčĝ èÙęĳÙĒĒĝÁĒʊ ċĎ ¸ęÙ¸Áę× ęHÁ îîÁqôĒĒɷċĎÙî¸Ùċ è
ĎÁĒÁ Ď¸×Ē¸ÙÁîęÙĒę ęę×ÁQ={ÁîęÁĎÒôĎ=îÒôĎí ęÙôîuĳĒęÁíĒ
qÁĒÁ Ď¸×ɷ ½İÙĒÁĒ Ó ÙîĒęɼ
ʓ{×ÁıôĎĒęċè ¸ÁęôÒÙĲę×Á½ ę ÙĒı×ÁîÙęʖĒ èĎÁ ½ĳ·ÁÁî
¸ôèèÁ¸ęÁ½ɷʔĒ ĳĒqôĒĒɼ! ę čĝ èÙęĳÁÒÒôĎęĒĒ×ôĝè½Òô¸ĝĒôîę×Á
·ĝĒÙîÁĒĒċĎô¸ÁĒĒę× ęę æÁĒÙî½ ę ɷı×Áę×ÁĎę× ęÙĒÒĎôí¸ĝĒ-
ęôíÁĎĒôĎ ċ ĎęôÒę×Á·ĝĒÙîÁĒĒɼ
qôĒĒ ¸æîôıèÁ½ÓÁĒĒĝ¸×ċĎ Óí ęÙĒíę æÁĒ¸ôííÙęíÁîęɼ“×ÙèÁ
ÙęʖĒĒęĎ ÙÓ×ęÒôĎı Ď½ęôĒ ĳɷʓ6ÙĲĳôĝĎċĎô¸ÁĒĒÁĒĒôę× ęę×Á½ ę
¸ôèèÁ¸ęÙôîÙĒİÁĎĳĎÁèÙ ·èÁ î½ę×Áčĝ èÙęĳÙĒĒĝÁĒ ĎÁċĎÁęęĳíÙî-
Ùí èɷʔíÁÁęÙîÓę× ęÓô èÙĒ ¸× èèÁîÓÁɼ{× ęʖĒ·Á¸ ĝĒÁÙęę æÁĒ
ôîÓôÙîÓ½ÙĒ¸ÙċèÙîÁęôĎÁľîÁ½ ę ¸ôèèÁ¸ęÙôîċĎô¸ÁĒĒÁĒɷęÁĒęÙîÓ
½ ę čĝ èÙęĳĎÁÓĝè Ďèĳ èôîÓę×Áı ĳɼ
ĝęÙî×ÁĎĎÁĒÁ Ď¸×ɷqôĒĒ× ĒÒôĝî½ę×ÁÁÒÒôĎęċ ĳĒôÒÒɼʓ;ÁĎÁʖĒ
ę×ÁÙîęÁĎÁĒęÙîÓę×ÙîÓ ·ôĝę î èĳęÙ¸Ēɶ•ôĝĎĝîÙčĝÁôċċôĎęĝîÙęĳ
ÙĒÓôÙîÓęô·ÁôîĳôĝĎôıî½ ę ɷʔĒ×ÁĒ ĳĒɼ{×ÁċĎÙî¸ÙċèÁ ċċèÙÁĒ
ı×Áę×ÁĎ ¸ôíċ îĳÙĒĝĒÙîÓÙęĒôıîÙîęÁĎî è½ ę ôĎ ĝÓíÁîę-
ÙîÓę× ę½ ę ıÙę×ę×ÙĎ½ʌċ ĎęĳĒôĝĎ¸ÁĒɼʓ=Òĳôĝ× İÁ½ ę ɷ î½
ĳôĝĒĝċċèÁíÁîęę× ę½ ę î½ĳôĝ½ôę× ęÙîı ĳĒę× ęôę×ÁĎ
Few survey respondents are always confident in the quality of their analytics
data, although a majority of respondents often trust that it’s accurate, up
to date, and relevant. Trust in completeness of data is lowest, but trust in
accuracy is most frequent.
Percentages may not equal 100 due to rounding.
Informal: Individuals who produce or use data reactively correct for
accuracy, consistency, timeliness, and completeness
Data stewardship: Someone is responsible for proactively identifying
and correcting causes of data quality problems
7%
21%
30% 42%
Formal: Data quality is routinely monitored, managed, and improved
as part of a formal data governance effort
No data quality efforts
Just one in five organizations
takes a formal approach
to data quality, while 30%
report at least proactive
efforts. The plurality of
respondents still tackle
the issue informally.
Figure 3: Data Quality
Efforts Show Room for
Improvement
Figure 2: Data Accuracy Is Most Trusted Quality
How often do you trust that analytics data is:
40%
43%
Always
Relevant Complete Uptodate Accurate
Often Sometimes Rarely Never
6%
1%
6%
28%
42%
21%
3%
12%
44%
34%
9%
1%
9%
47%
37%
6%
1%
11%
Always Often Sometimes Rarely Never
Informal: Individuals who produce or use data reactively correct for
accuracy, consistency, timeliness, and completeness
Data stewardship: Someone is responsible for proactively identifying
and correcting causes of data quality problems
Formal: Data quality is routinely monitored, managed, and improved
as part of a formal data governance effort
No data quality efforts

¸ôíċ îÙÁĒ¸ îʖęɷôî¸Áĳôĝĝî½ÁĎĒę î½ę×Á½ ę ɷôî¸ÁĳôĝÓÁę
ÙîĒÙÓ×ęĒɷĳôĝʖĎÁ ·èÁęô½ôę×ÙîÓĒôę×ÁĎ¸ôíċ îÙÁĒ¸ôĝè½îʖęɼʔ
Grade Your Data
WĝĎĒĝĎİÁĳĎÁĒċôî½ÁîęĒĎÁ¸ôÓîÙĺÁę× ę½ ę čĝ èÙęĳ·ÁÓÙîĒ ę
×ôíÁɶ=îęÁĎî è½ ę ÙĒíôĒęôÒęÁîĒĝ·äÁ¸ęęôİÁĎÙľ¸ ęÙôîɷÒôè-
èôıÁ½·ĳ¸ĝĒęôíÁĎ½ ę ʈĒÁÁ6ÙÓĝĎÁɚʉɼ“×ÙèÁę× ęí ĳèÁ ½ęô
ę×Á×ÙÓ×½ÁÓĎÁÁôÒęĎĝĒęÙîÙîęÁĎî è½ ę ɷ¸ĝĒęôíÁĎ½ ę ʊæÁĳ
ęôí îĳ ½İ î¸Á½ î èĳęÙ¸Ē ċċèÙ¸ ęÙôîĒʊĒęÙèèè ÓĒÙîÒôĝĎę×
ċè ¸Áı×ÁîÙę¸ôíÁĒęôę×Á½ÁÓĎÁÁôÒęĎĝĒęċè ¸Á½ÙîÙęɼ
nĎ ¸ęÙęÙôîÁĎĒ î½ÁĲċÁĎęĒÙîęÁĎİÙÁıÁ½ÒôĎę×ÙĒĎÁċôĎęîôęÁę× ę
Ē íôĎÁ ¸ôíċ îÙÁĒ ½ĎÙİÁ ęô ·ĝÙè½ ½İ î¸Á½ î èĳęÙ¸Ē ¸ ċ -
·ÙèÙęÙÁĒ î½ę×ÁİôèĝíÁôÒ İ Ùè ·èÁ½ ę ¸ôîęÙîĝÁĒęôÁĲċ î½ɷ
ôĎÓ îÙĺ ęÙôîĒ ĎÁ èôôæÙîÓ ÒôĎ ı ĳĒ ęô Ùî¸èĝ½Á îÁı ÙîÒôĎí -
ęÙôîĒôĝĎ¸ÁĒÙî î èĳęÙ¸ĒĒĳĒęÁíĒɼ{ô½ôĒôÁÒÒÁ¸ęÙİÁèĳĎÁčĝÙĎÁĒ
·ĝÙè½ÙîÓ ı ĳĒ ęô íÙęÙÓ ęÁ ę×Á ĎÙĒæ ôÒ · ½ ½ ę ÓÁęęÙîÓ · æÁ½
ÙîęôċĎô¸ÁĒĒÁĒ î½½ĎÙİÙîÓÒ ĝèęĳ¸ôî¸èĝĒÙôîĒɼ
ʓ{×Á½ÁĒÙĎÁęô½ô ½İ î¸Á½ î èĳęÙ¸ĒÙĒ½ĎÙİÙîÓę×ÙĒ ċċÁęÙęÁÒôĎ
íôĎÁ½ ę ɷı×Ù¸×í æÁĒċÁôċèÁÓôôĝę î½ċĝèè½ ę ÒĎôíôę×ÁĎ
ċè ¸ÁĒɷʔĒ ĳĒ! İÙ½LôĒ×ÙîɷċĎÙî¸Ùċ è¸ôîĒĝèę îę ęJîôıèÁ½ÓÁ
=îęÁÓĎÙęĳɼ ʓ“Ùę×ôĝę ÙîĒęÙęĝęÙîÓ ÙîÒôĎí ęÙôî ÓôİÁĎî î¸Á ċĎ ¸-
ęÙ¸ÁĒɷę×ÁĳʖĎÁ ęĎÙĒæôÒîôę ¸×ÙÁİÙîÓę×ÁÓô èĒę× ęę×ÁĳĒÁęôĝę
to achieve.”
{ĎĝĒęıôĎę×ĳ î èĳęÙ¸ĒĎÁčĝÙĎÁĒĒÁęęÙîÓĝċċôèÙ¸ÙÁĒę× ę ĒĒÁĎę
ę×ÁĒę î½ Ď½ÒôĎ¸ôîľ½Áî¸ÁèÁİÁèĒÙîę×Á½ ę îôĎÓ îÙĺ ęÙôî
ıÙèèĝĒÁɷ½ÁęÁĎíÙîÙîÓę×ÁċĎôİÁî î¸ÁôÒę×Á½ ę ɷ î½ÁĒę ·-
èÙĒ×ÙîÓ ¸¸Áċę ·èÁ½ ę ĝĒÁɷLôĒ×ÙîĒ ĳĒɼuę ęÙĒęÙ¸ è î èĳĒÙĒ î½
ęÁ¸×îôèôÓĳęôôèĒ¸ î×ÁèċÙ½ÁîęÙÒĳċĎô·èÁíĒ î½¸èÁ îĝċ½ ę
ĒÁęĒʈôĎèÁ ½ęô½Á¸ÙĒÙôîĒîôęęôĝĒÁę×Áíʉɼ
{ôĎôîęôʌ· ĒÁ½ uĝî LÙÒÁ 6Ùî î¸Ù è ÓÁîÁĎ èèĳ ·ÁÓÙîĒ ıÙę× ę×Á
ċĎÁíÙĒÁę× ęîô½ ę ÙĒęĎĝĒęıôĎę×ĳɷĎÁÓ Ď½èÁĒĒôÒĒôĝĎ¸Áɷ·Á-
¸ ĝĒÁ èè½ ę × Ēčĝ èÙęĳÙĒĒĝÁĒɼuęÙèèɷ×ôı½ ę ıÙèè·ÁĝĒÁ½ èĒô
Ò ¸ęôĎĒÙîęô×ôıęĎĝĒęıôĎę×ĳÙęîÁÁ½Ēęô·ÁɷĒ ĳĒ%ĎÙ¸QôîęÁÙĎôɷ
ĒÁîÙôĎİÙ¸ÁċĎÁĒÙ½ÁîęôÒ¸èÙÁîęĒôèĝęÙôîĒ ęę×ÁÓèô· èľî î¸Ù è
ĒÁĎİÙ¸ÁĒ ¸ôíċ îĳɼ ʓ“Á ½ô ×ôè½ ½ÙÒÒÁĎÁîę · ĎĒ ÒôĎ ½ÙÒÒÁĎÁîę
ęĳċÁĒôÒĝĒÁ¸ ĒÁĒɼ6ôĎÁĲ íċèÁɷÒôĎÓÁîÁĎ è¸èÙÁîęĒÁÓíÁîę -
ęÙôîôĎÁİÁî·ĝĒÙîÁĒĒ¸ ĒÁĒı×ÁĎÁıÁ ĎÁĒÙĺÙîÓ îôċċôĎęĝîÙęĳ
î½ í æÙîÓ ĒęĎ ęÁÓÙ¸ ½Á¸ÙĒÙôîɷ ę×Á · Ď ÒôĎ čĝ èÙęĳ ÙĒ èôıÁĎ
·Á¸ ĝĒÁÙîÓÁîÁĎ èɷę×ÁÁĎĎôĎĒıÙèèÁİÁîôĝęĝċ î½½ôıîɷ î½
ĳôĝʖĎÁWJÙîę×ÁÁî½ɷʔ×ÁĒ ĳĒɼ;ôıÁİÁĎɷÙÒ½ ę ÙĒĝĒÁ½ęô½ĎÙİÁ
14%
42%
Regularly
Datafrom
sensors/IoT
Sometimes Never
44%
62%
5%
33%32%
51%
18%
39% 39%
21%
Internally
generated
data
Publicly
available
data
Regulators’
data
34%
49%
18%
Competitors’
data
42%
48%
11%
Vendor-
provided
50%
42%
9%
Customer-
provided
4%
39%
Trusted
Datafrom
sensors/IoT
Somewhat trusted Not trusted
57%
63%
2%
35%
23%
66%
11%
55%
40%
5%
Internally
generated
data
Publicly
available
data
Regulators’
data
12%
67%
21%
Competitors’
data
29%
64%
7%
Vendor-
provided
37%
58%
5%
Customer-
provided
Figure 4: Verify and Trust
How often do you verify: How much do you trust insights based on:
Most attention is paid to verifying internal and customer data. Internal data is also
the most trusted source, while that provided by customers lags in fourth place.
Regularly Sometimes Never Trusted Somewhat trusted Not trusted
4

Ùî½ÙİÙ½ĝ è ¸ôííĝîÙ¸ ęÙôîĒ ıÙę× ¸èÙÁîęĒ ʊ ĒÁî½ÙîÓ î Áí Ùè
· ĒÁ½ôîę×Á ĒĒĝíċęÙôîôÒ ¸ÁĎę ÙîèÙÒÁÁİÁîęɷÒôĎÁĲ íċèÁʊ
there must be less tolerance for error.
ʓ=ÒıÁÓôęôĳôĝ î½Ē ĳɷʕôîÓĎ ęĝè ęÙôîĒôîĎÁęÙĎÁíÁîęɷʖ î½
ÙęęĝĎîĒôĝęÁİÁîę×ôĝÓ×ĳôĝʖĎÁɜɚɷĳôĝ ĎÁîôę ę èèę×ÙîæÙîÓ
·ôĝęĎÁęÙĎÁíÁîęɷę× ęʖĒċĎÁęęĳÁí· ĎĎ ĒĒÙîÓ î½ ċĎÁęęĳ· ½
¸èÙÁîęÁĲċÁĎÙÁî¸ÁɷʔQôîęÁÙĎôċôÙîęĒôĝęɼ
The Human Factor: Partner With Domain Experts
î½ÁĎĒę î½ÙîÓı× ę½ ę ¸ î·ÁęĎĝĒęÁ½í ĳ·ÁÁ ĒÙÁĎı×Áî
î èĳęÙ¸Ē ċĎ ¸ęÙęÙôîÁĎĒ ıôĎæ ¸èôĒÁèĳ ıÙę× ½ôí Ùî ÁĲċÁĎęĒ Ùî
their organizations.
Hôĳôî ÓĝĎôɷı×ôôİÁĎĒ ı ęÁ íôÒľİÁ Ē¸×ÙÁÒ½ ę ôÒľ¸ÁĎ
for the city and county of San Francisco (she has left the orga-
îÙĺ ęÙôîĒÙî¸Á·ÁÙîÓÙîęÁĎİÙÁıÁ½ÒôĎę×ÙĒĎÁċôĎęʉɷĒ ĳĒôîÓôÙîÓ
¸ôîĒĝèę ęÙôîĒıÙę×Ùîʌ×ôĝĒÁ¸èÙÁîęĒí æÁôĎ·ĎÁ æ ċĎôäÁ¸ęɼ
{× ęʖĒ ċĎÁ¸ÙĒÁèĳ ·Á¸ ĝĒÁ ċĎôäÁ¸ę ęÁ íĒ ĎÁ ôÒęÁî ĝĒÙîÓ ½ ę
ę× ęı Ēîôę¸ôèèÁ¸ęÁ½ıÙę×íô½ÁèÙîÓÙîíÙî½ɷĒ×ÁĒ ĳĒɼ
ʓ“Á ½ÁľîÙęÁèĳ × İÁ ęô ½ô ¸èÁ îʌĝċɷ ·ĝę ę×Á ı ĳ ıÁ í î ÓÁ
čĝ èÙęĳÙĒıÁċĎô· ·èĳĒċÁî½ îĝîĝĒĝ è íôĝîęôÒęÙíÁ¸ôí-
ċ ĎÁ½ęôĳôĝĎ İÁĎ ÓÁ½ ę Ē¸ÙÁî¸ÁęÁ íôî½ôÙîÓı× ęıÁ¸ èè
ÁĲċèôĎ ęôĎĳ î èĳĒÙĒ ·ĎÙÁľîÓĒɷʔ ôî ÓĝĎô Ē ĳĒɼ ʓ{× ęʖĒ ı×ÁĎÁ
ıÁʖĎÁĎÁ èèĳ¸ôîľĎíÙîÓıÙę× ¸èÙÁîęɷʕĎÁıÁèôôæÙîÓ ęĳôĝĎ½ ę
¸ôĎĎÁ¸ęèĳɽ“×ÁĎÁ ĎÁę×ÁĎÁ½ ę čĝ èÙęĳÙĒĒĝÁĒɽ;ôıĒ×ôĝè½ıÁ
ęĎÁ ęę×ôĒÁ½ ę čĝ èÙęĳÙĒĒĝÁĒ½ĝĎÙîÓę×Áíô½ÁèÙîÓċ× ĒÁɽʖʔ
{Á íÙîÓ½ ę Ē¸ÙÁîęÙĒęĒıÙę×½ôí ÙîÁĲċÁĎęĒ î½½ ę ÁĲċÁĎęĒ
ʊı×ôĝî½ÁĎĒę î½½ ę ĒôĝĎ¸ÁĒ î½×ôıę×Áĳ¸ î·Á ĝęôʌ
í ęÁ½ʊĒ×ôĝè½·Á ·ÁĒęċĎ ¸ęÙ¸ÁÙîÁİÁĎĳ î èĳęÙ¸ĒôċÁĎ ęÙôîɷ
Ē ĳĒ!Á î··ôęęɷ¸ôʌÒôĝî½ÁĎ î½¸×ÙÁÒ½ ę Ē¸ÙÁîęÙĒę ę
uí ĎęÁĎ;pɷ î î èĳęÙ¸ĒľĎíę× ęıôĎæĒıÙę×í ĎæÁęÁĎĒɼ
6ôĎÁĲ íċèÁɷÙÒ îôîèÙîÁĎÁę ÙèÁĎÙĒĒÁÁæÙîÓęôĝî½ÁĎĒę î½½ ę
Ē×ôıÙîÓ ĒċÙæÁÙî · î½ôîÁ½ôîèÙîÁĒ×ôċċÙîÓ¸ ĎęĒɷċÁôċèÁ
ıÙę× ½ÙÒÒÁĎÁîę ÁĲċÁĎęÙĒÁ î½ ċÁĎĒċÁ¸ęÙİÁ í ĳ îôęÁ ½ÙÒÒÁĎÁîę
ċ ęęÁĎîĒ î½Ù½ÁîęÙÒĳ½ÙÒÒÁĎÁîęĎÁĒôèĝęÙôîċ ę×Ēę× ęí ĳ× İÁ
·ÁÁîíÙĒĒÁ½ıÙę×ôĝęę× ę¸ôèè ·ôĎ ęÙôîɼĎÁę ÙèÁĲċÁĎęıôĝè½
ÁĲċÁ¸ę¸ÁĎę ÙîĒ×ôċċÙîÓ·Á× İÙôĎċ ęęÁĎîĒ î½¸×Á¸æ î èĳę-
Ù¸ĒĎÁĒĝèęĒ Ó ÙîĒę ĒĒĝíċęÙôîĒ î½Ù½ÁîęÙÒĳčĝÁĒęÙôîĒęô Ēæ
ę× ę ĎÁ îôę ċ Ďę ôÒ ę×Á ÙîÙęÙ è ċĎô·Áɼ Wî ę×Á ôę×ÁĎ × î½ɷ
½ ę ÙîÒĎ ĒęĎĝ¸ęĝĎÁÁĲċÁĎęıôĝè½·Á ·èÁęôĒċôę î½ÁĲ íÙîÁ
îôí èÙÁĒ ĎÙĒÙîÓÒĎôíęÁ¸×îÙ¸ èÙĒĒĝÁĒ î½íÙÓ×ęæîôıı×Ù¸×
½äĝĒęíÁîęĒęôę×Áíô½Áèıôĝè½×ÁèċÁÙę×ÁĎĺÁĎôÙîôîę×ôĒÁ
îôí èÙÁĒôĎċĎôİÙ½Áę×ÁÙîĒÙÓ×ęęô¸ôîľ½Áîęèĳ½ÙĒĎÁÓ Ď½ę×Áíɼ
AI Built on a Bedrock of Data Governance
{×ôĒÁĒÁÁæÙîÓęôĒ×ôĎÁĝċċĎ ¸ęÙ¸ÁĒę× ęÁî ·èÁ íôĎÁĎô·ĝĒę
¸ ċ ·ÙèÙęĳÙî î èĳęÙ¸Ē î½=íÙÓ×ęèôôæęô¸ôîĒęĎĝ¸ęÙôî î½
íÙîÙîÓÁčĝÙċíÁîęÓÙ îę ęÁĎċÙèè ĎÒôĎÙîĒċÙĎ ęÙôîɼ
ęÁĎċÙèè ĎĝĒÁĒ=ęô×Áèċí ĎæÁęÁĎĒċĎÁ½Ù¸ęı×Áî¸ĝĒęôíÁĎĒ
ĎÁĎÁ ½ĳęôċĝĎ¸× ĒÁ î½ĒÙÓî èı×Áî¸ĝĒęôíÁĎĒ ĎÁèÙæÁèĳęô
Ēęôċ·ĝĳÙîÓɼnĎô½ĝ¸ę½ÁİÁèôċíÁîęÁîÓÙîÁÁĎĒĝĒÁ=ęôĒÙíĝè ęÁ
ċĎô½ĝ¸ę ċÁĎÒôĎí î¸Á ·ÁÒôĎÁ ·ĝÙè½ÙîÓ ċĎôęôęĳċÁĒ î½
Teaming data scientists with domain experts and
data experts — who understand data sources and
how they can be automated — should be a best
practice in every analytics operation.
DEAN ABBOTT, SMARTERHQ
5

ĒÁċ Ď ęÁèĳ ĒÙíĝè ęÁ ×ôı î ÁčĝÙċíÁîę ôċÁĎ ęôĎ ıÙèè ĝĒÁ
í ¸×ÙîÁ Ùî ę×Á ľÁè½ Ēô ę×Áĳ ¸ î îęÙ¸Ùċ ęÁ ¸ĝĒęôíÁĎ îÁÁ½Ēɼ
R ęĝĎ è è îÓĝ ÓÁ ċĎô¸ÁĒĒÙîÓ ĝęôí ęÁĒ ę×Á î èĳĒÙĒ ôÒ èÁÓ è
½ô¸ĝíÁîęĒɷ¸×Á¸æÙîÓę×Áİ èÙ½ÙęĳôÒ ÓĎÁÁíÁîęĒ î½ċÙîċôÙîę-
ÙîÓÙĒĒĝÁĒę× ęîÁÁ½ ęęÁîęÙôîʁÙęÙĒ èĒôĝĒÁ½ęô î èĳĺÁęÁîĒôÒ
ę×ôĝĒ î½ĒôÒ¸ĝĒęôíÁĎ î½½Á èÁĎ¸ôííÁîęĒęôċĎÁ½Ù¸ęÒĝęĝĎÁ
čĝ èÙęĳ î½ı ĎĎ îęĳÙĒĒĝÁĒɼ
ʓ{× îæĒęô=ɷę×ÙîÓĒę× ęıôĝè½× İÁę æÁî ċÁĎĒôîęıôęôę×ĎÁÁ
ıÁÁæĒęô½ôí îĝ èèĳıÁ¸ î½ôÙîɗɖíÙîĝęÁĒɷʔĒ ĳĒQôĎÓ î
’ ıęÁĎɷ¸×ÙÁÒ î èĳęÙ¸Ē½ÙĎÁ¸ęôĎ ę ęÁĎċÙèè Ďɼ
Á×Ùî½ èèôÒę×ÁĒÁ¸ ċ ·ÙèÙęÙÁĒèÙÁĒ ĒęĎôîÓ½ ę í î ÓÁíÁîę
Òôĝî½ ęÙôîɷ ’ ıęÁĎ Ē ĳĒɼ ! ę ÓôİÁĎî î¸Á ÁîĒĝĎÁĒ ę× ę ę×Á
ĎÙÓ×ę½ ę ÓÁęĒĝĒÁ½Ēôę× ęĎÁĒĝèęĒ ĎÁęĎĝĒęıôĎę×ĳɼ%îĒĝĎÙîÓ
ę× ęę×Á½ ę ÙĒ×ÙÓ×čĝ èÙęĳ î½ÙęĒċĎôİÁî î¸ÁÙĒ¸èÁ ĎèÁęĒ
î èĳęÙ¸Ē ċĎôÒÁĒĒÙôî èĒ Ēę Ďę ıÙę× Óôô½ ·ĝÙè½ÙîÓ ·èô¸æĒ ÒôĎ
ę×ÁÙĎ íô½ÁèĒɼ ʓ“Á îÁÁ½ ęô í æÁ ĒĝĎÁ ıÁ × İÁ ¸ôííôî
ęĎĝę×Ùîę×Á½ ę ɷÙî¸èĝ½ÙîÓę×Áĝî½ÁĎèĳÙîÓ½ ę ɷ×ôıÙęʖĒ·ÁÁî
ċĎô¸ÁĒĒÁ½ɷ ×ôı ÙęʖĒ ·ÁÁî î èĳĺÁ½ɷ ×ôı ½ÙİÁĎĒÁ Ùę ÙĒɷʔ Ē×Á
Ē ĳĒɼʓî½ę×ÁîɷĝèęÙí ęÁèĳɷıÁ¸ îę æÁę× ę î½·ĝÙè½íôĎÁ
¸¸ĝĎ ęÁɷĒ¸ è ·èÁ î èĳęÙ¸Ēɼʔ
ęę×Á“ôĎæċè ¸Áu ÒÁęĳ î½=îĒĝĎ î¸Áô Ď½ʈ“u=ʉWîę ĎÙôɷ
¸× ĎęÙîÓę×ÁÓôİÁĎîíÁîę ÓÁî¸ĳʖĒ¸ôĝĎĒÁęô= î½ ½İ î¸Á½
î èĳęÙ¸Ē ·ÁÓÙîĒ ıÙę× ½ ę Ď¸×ÙęÁ¸ęĝĎÁɼ LÙæÁ í îĳ Ùî ×ÁĎ
ċôĒÙęÙôîɷ×ĎÙĒęÙî ;ôĳɷİÙ¸ÁċĎÁĒÙ½ÁîęôÒ¸ôĎċôĎ ęÁ·ĝĒÙîÁĒĒ
ÙîÒôĎí ęÙôî î½ î èĳęÙ¸Ēɷ ľî½Ē ę× ę ¸× èèÁîÓÁ í ½Á íôĎÁ
¸ôíċèÁĲ·ĳí îĳèÁÓ ¸ĳĒĳĒęÁíĒ î½½ ę ɼ
ʓ“Á× İÁ èôęôÒ½ ę ę× ęʖĒÙî½ÙĒċ Ď ęÁĒĳĒęÁíĒɼċęôîôıɷıÁ
× İÁîôę× ½ ÒôĎí èĒęĎ ęÁÓĳɼ“Áı îęęô ċċĎô ¸×ę×ÙĒÙî
ę×ôĝÓ×ęÒĝèí îîÁĎɷʔ;ôĳĒ ĳĒɼu×ÁÙĒıôĎæÙîÓęô¸ĎÁ ęÁʓ ·ÁęęÁĎ
½ ę ĒęĎ ęÁÓĳ î½ î Ď¸×ÙęÁ¸ęĝĎÁę× ęıÙèè ¸ęĝ èèĳèÁęĝĒĝĒÁę×Á
ÙîÒôĎí ęÙôîę×Áı ĳÙęĒ×ôĝè½·Áęôí æÁ·ÁęęÁĎ½Á¸ÙĒÙôîĒɼʔ
“u=ÙĒĒęĎôîÓ ę½Ù ÓîôĒęÙ¸ î½½ÁĒ¸ĎÙċęÙİÁ î èĳęÙ¸Ēɷ;ôĳĒ ĳĒɷ
·ĝę ę×Á ÓÁî¸ĳ ĒÁÁĒ ę×Á ôċċôĎęĝîÙęĳ ęô ½ô íôĎÁ ęô ôċ-
ÁĎ ęÙôî èÙĺÁ ċĎÁ½Ù¸ęÙİÁ î èĳęÙ¸Ēɼ =ę × Ē èĒô ¸ôîİÁîÁ½ î
ÁĲċèôĎ ęôĎĳ=ıôĎæÙîÓÓĎôĝċɼÁÒôĎÁÙę¸ îę æÁ ½İ îę ÓÁôÒ
Ēĝ¸× ½İ î¸Á½ęÁ¸×îôèôÓÙÁĒɷ;ôĳĒ ĳĒɷʓıÁîÁÁ½ęôí æÁĒĝĎÁ
ôĝĎ½ ę ÙĒıÁèèʌĒęĎĝ¸ęĝĎÁ½ɼ•ôĝ¸ îʖę½ôÙęÙÒę×ÁÒôĝî½ ęÙôîÙĒ
×Áè½ęôÓÁę×ÁĎıÙę×½ĝ¸ęę ċÁ î½Ēę ċèÁĒɼʔ
;ôĳÙîİÁĒęĒęÙíÁÙî¸ôííĝîÙ¸ ęÙîÓ î½Á½ĝ¸ ęÙîÓęôÓ ÙîĒĝċ-
ċôĎę î½·ĝ½ÓÁęÒôĎę×ÙĒ¸ĎÙęÙ¸ èÁÒÒôĎęÒĎôíıÙę×Ùîę×Á ÓÁî¸ĳ
î½ ÒĎôí í î ÓÁíÁîęɼ ʓ=Ò ę×Áĳ ¸ îʖę ĒÁÁ ı× ę =ʖí ęĎĳÙîÓ ęô
¸×ÙÁİÁɷ=ıôîʖę·ÁĒĝ¸¸ÁĒĒÒĝèɷʔĒ×ÁĒ ĳĒɼʓ{×Áĳ× İÁęô·Á ·èÁ
ęôİÙĒĝ èÙĺÁ ĒıÁèè Ē=¸ îı× ę=ʖíęĎĳÙîÓęô ¸×ÙÁİÁɼ=ę×Ùîæ
ę× ęʖĒíĳ·ÙÓÓÁĒęô·äÁ¸ęÙİÁʊęô¸ôííĝîÙ¸ ęÁę× ęİÙĒÙôîɼʔ
;ôĳĒôíÁęÙíÁĒĝĒÁĒ îÙ¸Á·ÁĎÓ î èôÓĳɷèÙæÁîÙîÓę×ÁɗɖʾĿô ę-
ÙîÓ ·ôİÁę×Áı ęÁĎęôę×Á î èĳęÙ¸ĒĎÁĒĝèęĒę× ęèÁ ½ÁĎĒîÁÁ½ɷ
ı×ÙèÁʓę×ÁɟɖʾôÒę×ÁÙ¸Á·ÁĎÓę× ęĿô ęĒĝî½ÁĎę×Áı ęÁĎÙĒíĳ
½ ę Ď¸×ÙęÁ¸ęĝĎÁɼʔî½Ē×ÁÓÁęĒĎÁĒĝèęĒɶʓ{×Áĳĝî½ÁĎĒę î½ıÁ
actually have to have a data strategy to enable analytics and
decision-making.”
Commit to Treating Data as an Asset
“×ÙèÁ í îĳ ¸ôĎċôĎ ęÁ èÁ ½ÁĎĒ ÓĎÁÁ ę× ę ½ ę ÙĒ î ÙíċôĎę-
îę ĒĒÁęɷ ę×ôĒÁ ı×ô · ¸æ ĝċ ę× ę İÙÁı ıÙę× ¸ôííÙęęÁ½
organizational resources may be faster to gain advantage from
= î½ ½İ î¸Á½ î èĳęÙ¸Ēɼę Ď¸è ĳĒuɷ ĒÁęôÒí î ÓÁíÁîę
decisions to treat data as an asset underlies the success of such
ÁÒÒôĎęĒɷ ¸¸ôĎ½ÙîÓęô’ÙĒ× èQôĎ½ÁɷİÙ¸ÁċĎÁĒÙ½ÁîęôÒ½ ę Ē¸Ù-
Áî¸Á î½ ½İ î¸Á½ î èĳęÙ¸Ēɼ6ôĎÁĲ íċèÁɷ×ÙĒ·ĝĒÙîÁĒĒĝîÙęʖĒ
¸×ÙÁÒ½ ę ôÒľ¸ÁĎĎÁċôĎęĒ½ÙĎÁ¸ęèĳęôę×Á%Wɼî½ę×Á· îæÁĒ-
ę ·èÙĒ×Á½ ½ ę í î ÓÁíÁîę¸ôĝî¸Ùèęô¸ ę èôÓ èè½ ę ĒĒÁęĒɷ
Á ¸× ĒĒÁęʖĒôıîÁĎɷ î½ċôèÙ¸ÙÁĒÒôĎı×ôÓÁęĒęôĝĒÁę×Á½ ę ɼ
This governance structure creates internal understanding
·ôĝę ę×Á ÙíċôĎę î¸Á ôÒ Ēôĝî½ ½ ę í î ÓÁíÁîę î½ Áî-
ĒĝĎÁĒęĎĝĒęÙîę×Á¸ôíċ îĳʖĒ½ ę ĎÁĒôĝĎ¸ÁĒɷQôĎ½ÁĒ ĳĒɼ{×Á
“Thanks to AI, things that would
have taken a person two to three
weeks to do manually we can do
in 10 minutes.”
MORGAN VAWTER, CATERPILLAR
6

ċċĎô ¸× ÙĒ ¸Ďĝ¸Ù è ÒôĎ ę×Á ¸ôîĒĝíÁĎ · îæʖĒ ! ę u¸ÙÁî¸Á
ÁîęÁĎôÒ%Ĳ¸ÁèèÁî¸Áɷı×ÁĎÁę×ÁÒô¸ĝĒÙĒôîĎÁ ¸×ÙîÓ¸ĝĒęôíÁĎĒ
ıÙę×¸ôíċÁèèÙîÓôÒÒÁĎĒı×ÙèÁí Ùîę ÙîÙîÓ ·ĝĒÙîÁĒĒĎÁè ęÙôî-
Ē×Ùċ·ĝÙèęôîęĎĝĒęɼ
QôĎ½ÁʖĒęÁ í× ĒĝĒÁ½ ½İ î¸Á½ęÁ¸×îÙčĝÁĒĒĝ¸× Ēí ¸×ÙîÁ
èÁ ĎîÙîÓɷ ½ÁÁċ èÁ ĎîÙîÓɷ î ęĝĎ è è îÓĝ ÓÁ ċĎô¸ÁĒĒÙîÓ èÓô-
ĎÙę×íĒɷ ęôċÙ¸ íô½ÁèÙîÓɷ î½ ĒÁîęÙíÁîę î èĳĒÙĒ ęô ÁĲ íÙîÁ
¸ĝĒęôíÁĎ¸ôíċè ÙîęĒɼÒęÁĎľî½ÙîÓċ ęęÁĎîĒÙîę×ÙĒęÁĲęʌ· ĒÁ½
½ ę ɷę×Á· îæ¸× îÓÁ½ĒôíÁôÒÙęĒċôèÙ¸ÙÁĒɼ
ʓ{×Áî ęĝĎ èè îÓĝ ÓÁċĎô¸ÁĒĒÙîÓ èèôıÁ½ĝĒęôĝî¸ôİÁĎę×ôĒÁ
½ÁÁċɷ×Ù½½ÁîÙîĒÙÓ×ęĒɼ“ÁıÁĎÁ ·èÁęôę×Ùîæ ·ôĝęÙęİÁĎĳ¸ôí-
ċĎÁ×ÁîĒÙİÁèĳ ÒĎôí ę×Á ¸ĝĒęôíÁĎʖĒ ċôÙîę ôÒ İÙÁı î½ ¸ęĝ èèĳ
í ½ÁĒôíÁę îÓÙ·èÁÙíċ ¸ęôîę×Á¸ĝĒęôíÁĎÁĲċÁĎÙÁî¸ÁɷʔQôĎ-
½ÁĒ ĳĒɼÒęÁĎę×ÁĒÁ¸× îÓÁĒɷ¸ôíċè ÙîęĎ ęÁĒ½ÙċċÁ½ęôę×ÁÙĎ
èôıÁĒęċôÙîęÙîÒôĝĎĳÁ ĎĒɼî½Ùîę×ÁɘɖɗɞHɼ!ɼnôıÁĎĎÁ½Ùę
Ď½u ęÙĒÒ ¸ęÙôîuęĝ½ĳɷ Ď¸è ĳĒuíôİÁ½ÒĎôíĒÁİÁîę×ċôĒÙ-
ęÙôîęôę×ÙĎ½ċôĒÙęÙôîɼ
Ē ę×ÁĒÁ èÁ ½ÙîÓ ċĎ ¸ęÙęÙôîÁĎĒ ¸æîôıèÁ½ÓÁɷ Òô¸ĝĒ ôî ½ ę
čĝ èÙęĳĎÁčĝÙĎÁĒ¸ôííÙęęÙîÓĎÁĒôĝĎ¸ÁĒ î½·ĝ½ÓÁęɼÓ ÙîɷôĝĎ
ĎÁĒÁ Ď¸×Òôĝî½ Ò ÙĎèĳíô½ÁĒęíÙîôĎÙęĳÙîę×Áİ îÓĝ Ď½ɶHĝĒę
ɗɛʾĎÁċôĎęÁ½ĒÙÓîÙľ¸ îę·ĝ½ÓÁęÙî¸ĎÁ ĒÁĒÒôĎ½ ę čĝ èÙęĳÙî
ę×Áċ ĒęĳÁ ĎʈĒÁÁ6ÙÓĝĎÁɛʉɼ“×ÙèÁÙęʖĒÓôô½îÁıĒę× ęɚɖʾĒęÙèè
Ē ı ĒôíÁ Ùî¸ĎÁ ĒÁ Ùî ·ĝ½ÓÁęɷ ÙęʖĒ èÙæÁèĳ ę× ę ę×Á ĎÁí Ùî½ÁĎɷ
ı×ôĒÁ·ĝ½ÓÁęĒĒę ĳÁ½Ŀ ęôĎ½Á¸ĎÁ ĒÁ½ɷí ĳľî½Ùę ·ÙęíôĎÁ
challenging to advance their analytics maturity.
ÁęęÁĎîÁıĒí ĳ·Áę× ę Òĝèèę×ÙĎ½ôÒĒĝĎİÁĳĎÁĒċôî½ÁîęĒîôı
ıôĎæÙîôĎÓ îÙĺ ęÙôîĒę× ęÁíċèôĳ ¸×ÙÁÒ½ ę ôÒľ¸ÁĎôĎ¸×ÙÁÒ
î èĳęÙ¸ĒôÒľ¸ÁĎɼ“ÁÒôĝî½ę× ęę×ôĒÁĎÁĒċôî½ÁîęĒ ĎÁĒÙÓîÙľ-
¸ îęèĳíôĎÁèÙæÁèĳęô èĒôĎÁċôĎę·ÁÙîÓôîę×ÁĎÙÓ×ęĒÙ½ÁôÒę×Á
ĝęÙèÙęĳÓ ċʊíÁ îÙîÓę×Áĳ× İÁę×ÁĎÙÓ×ę½ ę ęôÙîÒôĎíę×ÁÙĎ
business decisions.O
Data quality needs funding
to back up the commitment:
Only a relatively small per-
centage of companies gave
these efforts a markedly
higher priority in budgets
over the past year.
Figure 5: Putting
Their Money Where
Their Data Is
40%
15%
38%
5%
2%
Significantly
increased
Somewhat
increased
No
change
Somewhat
decreased
Significantly
decreased
7

8
Cleveland Clinic’s Centralized Data Store
Helps Build Trust in Analytics
INDUSTRY SNAPSHOT
{×ÁèÁ ½ÁĎĒôÒèÁİÁè î½èÙîÙ¸ʖĒɚʌĳÁ Ďʌôè½ÁîęÁĎċĎÙĒÁ î èĳęÙ¸ĒÙîÙęÙ ęÙİÁ ĎÁÒô¸ĝĒÁ½
ôî·ĝÙè½ÙîÓęĎĝĒęÙîę×Á½ ę ę×ÁôĎÓ îÙĺ ęÙôîí æÁĒ İ Ùè ·èÁęôĒĝċċôĎę½Á¸ÙĒÙôîĒɼ
ĎÁ ęÙîÓ ¸ÁîęĎ èċè ęÒôĎíÙĒôîÁĒęĎ ęÁÓĳęô ½İ î¸Áę×ôĒÁÁÒÒôĎęĒɼ
ÁÒôĎÁę×ÁÁÒÒôĎę·ÁÓ îɷèÁİÁè î½èÙîÙ¸× ½ İÁĎĳ½Á¸ÁîęĎ èÙĺÁ½ ċċĎô ¸×ɷıÙę×
ęÁ íĒ·ĝÙè½ÙîÓę×ÁÙĎôıî½ ę ĒęôĎÁĒ î½½ÁİÁèôċÙîÓę×ÁÙĎôıî î èĳęÙ¸ĒċĎôäÁ¸ęĒ
ıÙę×Ùî¸ôîĒÙĒęÁîęĎÁĒĝèęĒɼ
¸ÁîęĎ èÙĺÁ½ î èĳęÙ¸Ēċè ęÒôĎíı Ē èĒô ·ôĝęÁĒę ·èÙĒ×ÙîÓôîÁĒÁęôÒ½ ę ÒôĎę×Á
ôĎÓ îÙĺ ęÙôîɷĒ ĳĒ×ĎÙĒ!ôîôİ îɷÁĲÁ¸ĝęÙİÁ½ÙĎÁ¸ęôĎôÒÁîęÁĎċĎÙĒÁÙîÒôĎí ęÙôî
í î ÓÁíÁîę î½ î èĳęÙ¸Ēɼʓ=ÒıÁí æÁę× ęċè ęÒôĎí¸ôíċÁèèÙîÓÁîôĝÓ×ÙîęÁĎíĒôÒ
ċÁĎÒôĎí î¸Áɷ î½ĎÁ èèĳċ ĎęîÁĎıÙę×ę×Áí î½Á½ĝ¸ ęÁę×ÁíÙîęÁĎíĒôÒę×Á½ ę ę× ę
ıÁ× İÁ İ Ùè ·èÁɷıÁÁèÙíÙî ęÁę× ęîÁÁ½ęô¸ôċĳ½ ę èèôİÁĎę×Áċè ¸Á î½ċÁôċèÁ
·ÁÓÙîęôęĎĝĒęę× ę¸ÁîęĎ è½ ę ĒęôĎÁɷʔ!ôîôİ îĒ ĳĒɼ
{×Á ċċĎô ¸×ÙĒıôĎæÙîÓɼ{Á íĒÒĎôí Ďôĝî½ę×Á¸èÙîÙ¸ ĎÁíôİÙîÓĒôíÁôÒę×ÁÙĎıôĎæ
Ùîęôę×Áċè ęÒôĎíɷı×Ù¸×ÙĒ½ÁĒÙÓîÁ½ęôÁî ·èÁę×Áíęô× İÁ ½íÙîÙĒęĎ ęÙİÁĎÙÓ×ęĒęô
ę×Á½ ę Ùî ½Á½Ù¸ ęÁ½ ĎÁ ôÒı× ę!ôîôİ î¸ èèĒ ½ ę è ·ɼęÁ íîÁÁ½Ē ċċĎôİ è
ęô¸ĎÁ ęÁÙęĒĒċ ¸ÁÙîę×Áè ··ĝęîôęęô½ô î èĳęÙ¸ĒıôĎæę×ÁĎÁɼ
{×ÁĎÁ ĎÁĒôíÁ·ÙÓ·ÁîÁľęĒɼ6ÙĎĒęɷęÁ íĒę× ęĝĒÁę×Á¸ÁîęĎ èÙĺÁ½ċè ęÒôĎí ĎÁîô
èôîÓÁĎ ĒæÙîÓ!ôîôİ îʖĒÓĎôĝċÒôĎ¸ôċÙÁĒôÒ¸èÙîÙ¸½ ę ĒÁęĒęôÁĲċÁĎÙíÁîęıÙę×ɼuÁ¸ôî½ɷ
ę×Áċè ęÒôĎíÙíċĎôİÁĒę×Áčĝ èÙęĳôÒę×Á½ ę ·Á¸ ĝĒÁċÁôċèÁ ¸¸ÁĒĒÙîÓÙę½ôîʖę× İÁęô
ıôĎĎĳı×Áę×ÁĎę×Áĳ ĎÁÓÁęęÙîÓę×ÁíôĒęĝċʌęôʌ½ ęÁİÁĎĒÙôîôÒę×Á½ ę ʊę× ęʖĒæîôıîɼ
î½ę×ÙĎ½ɷę×Áċè ęÒôĎíÁî× î¸ÁĒę×Á¸ĝèęĝĎÁÒôĎ î èĳęÙ¸Ēɼ
ʓ{×ôĒÁİÁĎĳę îÓÙ·èÁ¸× îÓÁĒÙî·Á× İÙôĎÙî½Ù¸ ęÁęôíÁę× ęıÁʖĎÁ·ĝÙè½ÙîÓę× ęęĎĝĒęɷʔ
Donovan says.
Á¸ ĝĒÁċÁôċèÁ ĎÁĝĒÙîÓę×Á¸ÁîęĎ è½ ę ĒęôĎÁɷ!ôîôİ îʖĒęÁ íîôı× ĒÙîĒÙÓ×ęÙîęô
ĝċ½ ęÁĒ î½íô½Ùľ¸ ęÙôîĒę× ę ĎÁí ½Áęôę×Á½ ę ɼʓ“Á¸ î× İÁ ¸ôîİÁĎĒ ęÙôî
·ôĝęɷʕ=Ēę×ÙĒĎÁ èèĳę×Á½ ę ę× ęʖĒıĎôîÓɽ=Ēę×ÙĒ îÙîęÁĎċĎÁę ęÙôîÙĒĒĝÁɽʖʔ×ÁĒ ĳĒɼ{× ę
æÙî½ôÒ ı ĎÁîÁĒĒ¸ î×Áèċ¸èÁ Ďĝċ¸ôîÒĝĒÙôî ·ôĝę½ÙÒÒÁĎÙîÓĝĒÁĒôÒęÁĎíÙîôèôÓĳ î½
èÁ ½ęô ÓĎÁÁíÁîę ·ôĝę½ ę ½ÁľîÙęÙôîĒɼĳıÙîîÙîÓęĎĝĒęÙî ¸ÁîęĎ è½ ę ĒęôĎÁɷę×Á
¸èÙîÙ¸Áî ·èÁ½ ÒÁÁ½· ¸æèôôċę× ę¸ îèÁ ½ęô îôę×ÁĎ·ÁîÁľęɶ·ÁęęÁĎ½ ę í î ÓÁíÁîęɼ
HEALTH
CARE
Chris Donovan,
executive director of
enterprise information
management and analytics,
Cleveland Clinic
Those very tangible changes in behavior indicate
to me that we’re building that trust.
”
“

9
ʓ“Á× İÁęôí æÁĒĝĎÁę× ęıÁʖĎÁîôęĒæÙċċÙîÓæÁĳċ ĎęĒôÒę×ÁäôĝĎîÁĳɷʔ’ ıęÁĎÁĲċè ÙîĒɼ
ʓ•ôĝ¸ îʖęäĝĒęĒæÙċ½ÁĒ¸ĎÙċęÙİÁ î èĳęÙ¸Ēɷ·ĝÙè½ÙîÓ½ Ē×·ô Ď½Ēɷĝî½ÁĎĒę î½ÙîÓ½ ę ɷ î½äĝíċ
ĎÙÓ×ęęôċĎÁ½Ù¸ęÙİÁ î½ċĎÁĒ¸ĎÙċęÙİÁ î èĳęÙ¸Ēɼ“Áı îęęôí æÁĒĝĎÁę× ęıÁʖĎÁ×ÁèċÙîÓę×Áí
ęôĝî½ÁĎĒę î½ę×ÁÙĎ½ ę ęę×ÁÒôĝî½ ęÙôî î½ę×Áî ½İ î¸Áę×Áíĝċę×Áí ęĝĎÙęĳ¸ĝĎİÁɼʔ
“Ùę× îôİÁĎ Ď¸×ÙîÓ ċċĎô ¸×ę× ęċĎôİÙ½ÁĒ Óèô· èİÙÁıôÒ èèę×Á¸ôíċ îĳʖĒÁÒÒôĎęĒɷ
ęÁĎċÙèè Ď¸ îę æÁĒĝ¸¸ÁĒĒÒĝèċĎôäÁ¸ęĒ î½ ċċèĳę×Áíę×ĎôĝÓ×ôĝęę×Á¸ôíċ îĳɼnĎÁİÙôĝĒèĳɷ
î èĳęÙ¸ĒÁĲ¸ÁèèÁî¸Áí ĳ× İÁĒċĎĝîÓĝċÙîċô¸æÁęĒıÙę×ÁÒÒôĎęĒĒĝ¸× ĒĒĝċċèĳ¸× Ùî
î èĳęÙ¸ĒôĎí ĎæÁęÙîÓ î èĳęÙ¸Ēɷ·ĝęę×ôĒÁęôôèĒí ĳîôę× İÁę×Áî·ÁÁî ċċèÙÁ½ę×ĎôĝÓ×
ę×Á·ĝĒÙîÁĒĒĝîÙęɼʓWĝĎ î èĳęÙ¸ĒĎô ½í ċĒ× İÁĎÁ èèĳĒ×ôıÁ½ę×ÁċôıÁĎôÒɷʕ;ÁĳɷÙÒ=ʖí
½ôÙîÓ î èĳęÙ¸Ē×ÁĎÁɷÙęÙíċ ¸ęĒę×ÙĒ î½ÙęÙíċ ¸ęĒę×ÁÁîęÙĎÁİ èĝÁ¸× Ùîɼʖî½ĒôıÁʖİÁ
ĒÁÁî èôęôÒĒ¸ èÁ½ î èĳęÙ¸Ē Ē ĎÁĒĝèęôÒ× İÙîÓę×ôĒÁĒęĎ ęÁÓÙÁĒɷʔ’ ıęÁĎĒ ĳĒɼ
MANUFACTURING
Morgan Vawter,
chief analytics director,
Caterpillar
INDUSTRY SNAPSHOT
Caterpillar Tailors Analytics Strategies
to Business Unit Needs
{ô·ĝÙè½ ¸ĝèęĝĎÁôÒ½ ę ʌ½ĎÙİÁî½Á¸ÙĒÙôîʌí æÙîÓ î½Ùîîôİ ęÙôîɷ ęÁĎċÙèè ĎʖĒ î èĳęÙ¸Ē
ÓĎôĝċıôĎæĒ¸èôĒÁèĳıÙę×·ĝĒÙîÁĒĒĝîÙęèÁ ½ÁĎĒę×ĎôĝÓ×ôĝęę×Áʛɚɛ·ÙèèÙôî¸ôíċ îĳɼ{×ÙĒ
×ÁèċĒÁîĒĝĎÁę× ę î èĳęÙ¸ĒĒĝċċôĎęĒ·ĝĒÙîÁĒĒîÁÁ½Ē î½ıÙîĒÁĲÁ¸ĝęÙİÁĒĝċċôĎęɼ
QRUJDQ’ ıęÁĎɷ¸×ÙÁÒ î èĳęÙ¸Ē½ÙĎÁ¸ęôĎ ęę×Á¸ôíċ îĳɷĒ ĳĒ×ÁĎęÁ íĒÙęĒ½ôıîıÙę×ę×Á
èÁ ½ÁĎĒôÒĝîÙęĒęôę ÙèôĎĒęĎ ęÁÓÙÁĒęôÒÙęę×ÁîÁÁ½ĒôÒÁ ¸×ɼʓ=ęʖĒí æÙîÓĒĝĎÁę× ęɷ·Á¸ ĝĒÁ
ıÁ× İÁ İÁĎĳ½ÙİÁĎĒÁ·ĝĒÙîÁĒĒɷıÁ½ôîʖęäĝĒę× İÁ î î èĳęÙ¸ĒĒęĎ ęÁÓĳÒôĎę×Á¸ôíċ îĳʊ
ıÁ× İÁ î èĳęÙ¸ĒĒęĎ ęÁÓÙÁĒę× ęÁî ·èÁę×Á·ĝĒÙîÁĒĒĝîÙęĒęĎ ęÁÓÙÁĒɷʔ’ ıęÁĎĒ ĳĒɼʓèĒôɷ
ę× ęʖĒĝĒÁÒĝè·Á¸ ĝĒÁÙęÓÁęĒę× ęÁĲÁ¸ĝęÙİÁ·ĝĳʌÙîɼ{×Áĳ·Á¸ôíÁ ½İô¸ ęÁĒÒôĎÙęɷ î½ę× ę
¸ Ē¸ ½ÁĒ½ôıî î½ĎÁ èèĳ¸ĎÁ ęÁĒ èÁİÁèôÒôıîÁĎĒ×ÙċÙîę×Á·ĝĒÙîÁĒĒĝîÙęÒôĎĝĒÙîÓ
analytics to enable business success.”
ęÁĎċÙèè ĎĒÁĎİÁĒÙî½ĝĒęĎĳĒÁÓíÁîęĒÙî¸èĝ½ÙîÓÁîÁĎÓĳɷęĎ îĒċôĎę ęÙôîɷ¸ôîĒęĎĝ¸ęÙôîɷ î½
íÙîÙîÓɷ î½× Ē ĝîÙęÒôĎ¸ĝĒęôíÁĎ î½½Á èÁĎĒĝċċôĎęɼ%îĒĝĎÙîÓę× ęÁ ¸×ÓĎôĝċ× ĒÙęĒ
ôıî î èĳęÙ¸ĒĒęĎ ęÁÓĳÁî ·èÁĒ’ ıęÁĎʖĒôĎÓ îÙĺ ęÙôîęôę ÙèôĎ î èĳęÙ¸ĒċĎôäÁ¸ęĒęôí ĲÙíÙĺÁ
İ èĝÁÒôĎÁ ¸×ĝîÙęʊ î½ęôľęÙęĒ î èĳęÙ¸Ēí ęĝĎÙęĳèÁİÁèɼ
We want to make sure that we’re
helping them to understand their data
at the foundation and then advance
them up the maturity curve.
”
“

10
Success With Customer
Data Depends on Keeping
Customers’ Trust
P
ractitioners in our recent Data Analytics survey have
ÓôęęÁîę×ÁíÁíôôî½ ę ĒÁ¸ĝĎÙęĳɶî èĳęÙ¸Ē½ÁċÁî½
ôî½ ę ʊ î½ÙÒę× ę½ ę ÙĒèôĒęôĎĒęôèÁîɷôĎÙÒ¸ĝĒ-
ęôíÁĎĒ î½ċ ĎęîÁĎĒ·Á¸ôíÁĎÁèĝ¸ę îęęôĒ× ĎÁ½ ę ·Á¸ ĝĒÁ
ôÒ¸ôî¸ÁĎîĒôİÁĎ×ôıÙęıÙèè·Á× î½èÁ½ɷę×Á½ ę ʌ½ĎÙİÁîÁîęÁĎ-
ċĎÙĒÁÙĒ ęĎÙĒæɼ
ôííôîʌĒÁîĒÁĒÁ¸ĝĎÙęĳċĎ ¸ęÙ¸ÁĒ ĎÁıÙ½ÁĒċĎÁ ½ íôîÓĒĝĎ-
İÁĳ ĎÁĒċôî½ÁîęĒɼ ĒęĎôîÓ í äôĎÙęĳ ʊ ɜəʾ ʊ ÁÙę×ÁĎ × İÁ ôĎ
ĎÁÙíċèÁíÁîęÙîÓ ĎÁĒċôîĒÁċè îÙî¸ ĒÁę×ÁÙĎôĎÓ îÙĺ ęÙôî
ĒĝÒÒÁĎĒ ½ ę ·ĎÁ ¸×ɼuÁİÁîôĝęôÒɗɖÁÙę×ÁĎęĎ ¸æôĎ ĎÁ¸ĎÁ-
ęÙîÓę×ÁíÁ îĒęôęĎ ¸æı×ÁĎÁĒÁîĒÙęÙİÁ½ ę ÙĒĒęôĎÁ½ɼuÙí-
Ùè Ďí äôĎÙęÙÁĒæÁÁċôĎ ĎÁċè îîÙîÓęô¸ĎÁ ęÁĝċ½ ęÁ½èÙĒęĒôÒ
ĒÁîĒÙęÙİÁ½ ę ę×Áĳ¸ôèèÁ¸ę î½ęĎ Ùî èèÁíċèôĳÁÁĒÙî={ĒÁ¸ĝĎÙęĳ
ċĎ ¸ęÙ¸ÁĒʈĒÁÁ6ÙÓĝĎÁɜʉɼ
The Pivotal Roles of Data Security and Privacy
%İÁîíôĎÁĒôċ×ÙĒęÙ¸ ęÁ½íÁ ĒĝĎÁĒ ĎÁÙîċè ¸ÁôĎĝî½ÁĎı ĳ
ęíôĒęôĎÓ îÙĺ ęÙôîĒɼĒèÙÓ×ęí äôĎÙęĳ× İÁÙíċèÁíÁîęÁ½ôĎ
ĎÁ ċè îîÙîÓ ęô ½Áċèôĳ ½İ î¸Á½ î èĳęÙ¸Ē ęô ċĎÁ½Ù¸ę ¸ĳ·ÁĎʌ
ÙîęĎĝĒÙôîĎÙĒæĒɼQôĎÁę× î× èÒʈɛɚʾʉ ĎÁĝĒÙîÓôĎÙíċèÁíÁîęÙîÓ
¸ĳ·ÁĎĒÁ¸ĝĎÙęĳÒĎ íÁıôĎæɼî½əɝʾ× İÁ ¸×ÙÁÒÙîÒôĎí ęÙôî
ĒÁ¸ĝĎÙęĳôÒľ¸ÁĎęôèÁ ½ę×ÁĒÁÁÒÒôĎęĒɷıÙę× îôę×ÁĎɗɞʾċè î-
îÙîÓôĎÙíċèÁíÁîęÙîÓę× ęĎôèÁʈĒÁÁ6ÙÓĝĎÁɝʉɼ
î èĳęÙ¸ĒċĎ ¸ęÙęÙôîÁĎĒîÁÁ½ęô·Á ı ĎÁôÒę×ÁÁîİÙĎôîíÁîę
Ùî ı×Ù¸× ę×Áĳ ½ô ·ĝĒÙîÁĒĒɼ %İÁî ¸ôíċ îÙÁĒ Ùî ę×Á ·ĝĒÙ-
îÁĒĒʌęôʌ·ĝĒÙîÁĒĒí ĎæÁęę× ę ĎÁîôęęÙÁ½ęôîÁÓ ęÙİÁċĝ·èÙ¸Ùęĳ
— think of consumer data breaches or controversies about
ę×Áı ĳĒô¸Ù èíÁ½Ù îÁęıôĎæĒí î ÓÁĝĒÁĎ½ ę ʊíĝĒęîôęÁ
¸ĝĒęôíÁĎĒʖ ×ÁÙÓ×ęÁîÁ½ ¸ôî¸ÁĎîĒ ·ôĝę ½ ę ĝĒÁɼ ʓ“×Áî
19%
Currently do this
Haveresponse
planinplace
incaseofdata
breach
Implementing Considering
44%
12%
14%
Trackwhereall
dataisstored
Keepupdated
listofsensitive
datatypes
collected
Trainall
employeesinIT
securityrisks
andpractices
Planning No activity
11%
47%
23%
11%
10%10%
43%
20%
13% 13%
11%
Currently do this
Applyadvanced
analyticsto
predictcyber-
intrusionrisks
22%
16%
15%
Usecybersecu-
rityframeworks
(e.g.,PCI,NIST)
Employachief
information
securityofficer
33%
39%
15%
11%
37%
7%
13%
44%
20%
12%
13%
11%
14%
16%
20%
11%
33%
Figure 6: Data Breach Defenses Are Up
Figure 7: Security Frameworks and CISOs Take Hold
Organizations are moving toward solid, baseline data security practices,
although many have yet to fully implement these measures.
A slight majority of survey respondents are increasing their data security
maturity via implementation of security frameworks, and nearly half have or
are hiring a CISO. A minority are using more sophisticated measures such as
applying analytics and AI to security.
19%
Currently do this
Haveresponse
planinplace
incaseofdata
breach
4%
12%
14%
Trackwhereall
dataisstored
Keepupdated
listofsensitive
datatypes
collected
Trainall
employeesinIT
securityrisks
andpractices
11%
47%
23%
11%
10%10%
43%
20%
13% 13%
11%
Currently do this
Applyadvanced
analyticsto
predictcyber-
intrusionrisks
22%
16%
15%
Usecybersecu-
rityframeworks
(e.g.,PCI,NIST)
Employachief
information
securityofficer
33%
39%
15%
11%
37%
7%
13%
44%
20%
12%
13%
11%
14%
16%
20%
11%
33%
Currently do this Implementing Planning Considering No activity

11
¸ôîĒĝíÁĎĒĒÁÁ· ½îÁıĒĒęôĎÙÁĒôĝęę×ÁĎÁ ·ôĝęę×ÙîÓĒę× ę
× ċċÁîɷ ę× ę ¸ î × İÁ îÁÓ ęÙİÁɷ ĒċÙĎ èÙîÓ ÁÒÒÁ¸ę ôî ôę×ÁĎ
¸ôíċ îÙÁĒ î½ ôę×ÁĎ Ùî½ĝĒęĎÙÁĒɷʔ Ē ĳĒ QôĎÓ î ’ ıęÁĎɷ ¸×ÙÁÒ
î èĳęÙ¸Ē½ÙĎÁ¸ęôĎ ę ęÁĎċÙèè Ďɼ
ʓ“Á¸ îʖę½ô îĳôÒę×ÙĒıôĎæıÙę×ôĝęęĎĝĒęıÙę×ôĝĎ¸ĝĒęôíÁĎĒ
î½ę×Á·ĝĒÙîÁĒĒÁĒę× ęıÁĒÁĎİÁɷʔ’ ıęÁĎ ½½Ēɼʓî½ĒôıÁʖĎÁ
í æÙîÓĒĝĎÁę× ęıÁ× İÁ¸èÁ ĎèĳÁĒę ·èÙĒ×Á½ĎÙÓ×ęĒęôĝĒÁę×Á
information and ensuring that that lineage and that right and
ę× ęċÁĎíÙĒĒÙôîÙĒİÁĎĳ¸èÁ ĎıÙę×ÁİÁĎĳęĳċÁôÒ½ ę ę× ęıÁ
ĝĒÁ î½ÁİÁĎĳ½ ę ĒôĝĎ¸Áę× ęıÁ ¸¸ÁĒĒɼʔ
“×ÙèÁôĎÓ îÙĺ ęÙôîĒ ĎÁċĝĎĒĝÙîÓ½ ę ĒÁ¸ĝĎÙęĳıÙę×ĒôíÁ
ĝĎÓÁî¸ĳɷę×ÁÙĎÁÒÒôĎęĒôîċĎÙİ ¸ĳè ÓɼHĝĒęɚɗʾôÒĎÁĒċôî½ÁîęĒ
Ē ĳę×ÁĳæÁÁċ¸ĝĒęôíÁĎĒÙîÒôĎíÁ½ ·ôĝę½ ę ¸ôèèÁ¸ęÙôî î½
× İÁÙîęÁĎî è¸ôîęĎôèĒÙîċè ¸ÁɼʈĒÁÁ6ÙÓĝĎÁɞʉɼ
The Opportunity to Build Customer Trust Based on Data
“×ÙèÁĒôíÁí ĳİÙÁıċĎÙİ ¸ĳĎÁÓĝè ęÙôîĒĒĝ¸× Ēę×Á%ĝĎôċÁ-
îîÙôî7ÁîÁĎ è! ę nĎôęÁ¸ęÙôîqÁÓĝè ęÙôîʈ7!nqʉ Ē ½½Á½
·ĝĎÁ ĝ¸Ď ęÙ¸ô·èÙÓ ęÙôîĒɷôę×ÁĎĒĒÁÁę×Áí Ē îôċċôĎęĝîÙęĳęô
·ĝÙè½ęĎĝĒęıÙę×¸ĝĒęôíÁĎĒɼuĝĒ î%ęèÙîÓÁĎɷ îÙî½ĝĒęĎĳ î èĳĒę
ęę×ÁèęÙíÁęÁĎ7ĎôĝċɷÒ èèĒÙîęôę×Áè ęęÁĎ¸ íċɼ
Etlinger observes that both business leaders and consumers
ĎÁ·Á¸ôíÙîÓíôĎÁ ı ĎÁôÒę×Áí îĳı ĳĒę×ÁÙĎ½ ę ÙĒĝĒÁ½
î½·ÁÓÙîîÙîÓęôĝî½ÁĎĒę î½ę×ÁÙíċ ¸ęę× ę½ ę ĝĒÁ× Ēôî
ę×ÁÙĎèÙİÁĒɷę×ÁÙĎ·ĝĒÙîÁĒĒÁĒɷ î½Ēô¸ÙÁęĳɼĒĒĝ¸×ɷę×ÁıÙ½Áèĳ
ċĝ·èÙ¸ÙĺÁ½ ɘɖɗɞ ¸ôíċèÙ î¸Á ½Á ½èÙîÁ ÒôĎ ę×Á 7!nq ¸ ĝÓ×ę
ę×Á ęęÁîęÙôî ôÒ íôĒę ôĎÓ îÙĺ ęÙôîĒɷ ıÙę× íôĎÁ ę× î × èÒ
Ē ĳÙîÓ ęę×ÁęÙíÁôÒę×ÁĒĝĎİÁĳę× ęę×Áĳ× ½ÁÙę×ÁĎľîÙĒ×Á½
ıÙę×¸ôíċèÙ î¸ÁɷıÁĎÁıôĎæÙîÓôîÙęɷôĎıÁĎÁċè îîÙîÓęôıôĎæ
ôîÙęʈĒÁÁ6ÙÓĝĎÁɟʉɼ
ʓ{×ÙĒ ÙĒ ¸ęĝ èèĳ ęĎÁíÁî½ôĝĒ íôíÁîę ęô ĎÁÙí ÓÙîÁ ı× ę
½ ę ʌ¸ÁîęĎÙ¸ôĎÓ îÙĺ ęÙôîĒĒ×ôĝè½èôôæèÙæÁ î½ı× ęę×Á¸ĝĒ-
ęôíÁĎ ÁĲċÁĎÙÁî¸Á Ē×ôĝè½ ·Áɷʔ Ē×Á Ē ĳĒɼ “×ÙèÁ ôĎÓ îÙĺ ęÙôîĒ
íĝĒę ¸ę Ùî ¸¸ôĎ½ î¸Á ıÙę× ę×ÁÙĎ ľ½ĝ¸Ù Ďĳ ĎÁĒċôîĒÙ·ÙèÙęÙÁĒ
ęô¸ôíċèĳıÙę×ĎÁÓĝè ęÙôîĒɷ·ĝĒÙîÁĒĒèÁ ½ÁĎĒ¸ î èĒôĒę Ďęęô
Ùí ÓÙîÁ ıôĎè½Ùîı×Ù¸×ĎÁĒċÁ¸ęÙîÓċĎÙİ ¸ĳ î½·ĝÙè½ÙîÓęĎĝĒę
ıôĎæĒęôę×ÁÙĎ ½İ îę ÓÁɼ
=î ę×ÙĒ ÁîİÙĎôîíÁîęɷ ¸ôíċ îÙÁĒ ę× ę ľÓĝĎÁ ôĝę ı ĳĒ ęô ĝĒÁ
½ ę ę× ęċĎôİÙ½Áİ èĝÁ î½Áî× î¸Áę×ÁęĎĝĒęôÒę×ÁÙĎ¸ĝĒęôí-
ÁĎĒıÙèèÓ Ùî îÁ½ÓÁɼ7ÙİÁîę×ÁôċęÙôîʓęô· îæıÙę× ¸ôíċ îĳ
ı×ÁĎÁĳôĝ½ôîʖęæîôıı×ÁĎÁĳôĝĎ½ ę ʖĒÓôÙîÓ î½ĳôĝ½ôîʖę
æîôı×ôıÙęʖĒ·ÁÙîÓĝĒÁ½ôĎʆęô½ô·ĝĒÙîÁĒĒʇıÙę×ôîÁı×ôĒ ĳĒɷ
ʕ•ôĝ æîôıɷ ıÁ ¸ęĝ èèĳ ı îę ċ ĎęîÁĎĒ×Ùċ ıÙę× ĳôĝ î½ ıÁ
ı îęęô·Á ·èÁęôôÒÒÁĎĳôĝę×Á·ÁĒęċôĒĒÙ·èÁÁĲċÁĎÙÁî¸Áɷ î½
ęô½ôę× ęɷ×ÁĎÁʖĒı× ęıÁ Ēæ î½ę×ÙĒÙĒĳôĝĎ¸×ôÙ¸ÁɷʖʔċÁôċèÁ
ıÙèè·ÁíôĎÁÙî¸èÙîÁ½ęô¸×ôôĒÁę×ÁĒÁ¸ôî½ɷ%ęèÙîÓÁĎĒ ĳĒɼ
Trust Is Fragile — Handle With Care
íôîÓ èÁ ½ÙîÓ î èĳęÙ¸Ē ċĎ ¸ęÙęÙôîÁĎĒɷ ę×ÁĎÁ ĎÁ îĝí·ÁĎ
ı×ôĒęĎÙİÁęô¸ĎÁ ęÁĒĝ¸×ęĎĝĒęÙîÓĎÁè ęÙôîĒ×ÙċĒɼĒíÙÓ×ę·Á
ÁĲċÁ¸ęÁ½ɷ í îĳ ¸ î ·Á Òôĝî½ Ùî Ùî½ĝĒęĎÙÁĒ èÙæÁ ľî î¸Ù è
41% Notify customers how we
collect, use, and share their
information, and have
internal controls over how
employees use the data
20%
25%
14%
Notify customers how we
collect, use, and share
their information
Implemented data privacy
measures but have not yet
communicated them
externally
It’s not an issue we are
concerned with
16%
27%
14%
25%
18%
We are fully compliant
with GDPR
We are actively working
on GDPR compliance
We are planning to
comply with GDPR
GDPR is not a requirement
but may guide our
privacy policy
We have no plans
for compliance
Privacy efforts lag security efforts, with just 41% keeping customers
informed about data collection and use practices and also having
internal controls in place.
Figure 8: Privacy Controls Have Room to Grow
Figure 9: GDPR Has Gained Attention
41% Notify customers how we
collect, use, and share their
information, and have
internal controls over how
employees use the data
20%
25%
14%
their information
measures but have not yet
communicated them
externally
concerned with
16%
27%
14%
25%
18%
with GDPR
on GDPR compliance
We are planning to
comply with GDPR
GDPR is not a requirement
but may guide our
privacy policy
We have no plans
for compliance
GDPR has the attention of most survey respondents, with more than half
saying they have finished or are planning or working on compliance.
Notify customers how
we collect, use, and share
their information, and
have internal controls
over how employees
use the data
their information
measures but have not
yet communicated them
externally
concerned with
with GDPR
on GDPR compliance
We are planning to
comply with GDPR
GDPR is not a require-
ment but may guide our
privacy policy
We have no plans for
compliance

12
ĒÁĎİÙ¸ÁĒ î½×Á èę×¸ ĎÁɼ“×ÙèÁę×ÁĒÁ ĎÁɷôÒ¸ôĝĎĒÁɷÓôİÁĎîÁ½
·ĳ ĒęÙÒÒÁĎ ĎÁÓĝè ęÙôîĒɷ Ēĝ¸¸ÁĒĒ Ùî ę×ÁĒÁ ĒÁ¸ęôĎĒ × Ē èôîÓ
½ÁċÁî½Á½ôîÁ ĎîÙîÓ î½ĎÁę ÙîÙîÓ½ÁÁċęĎĝĒęɷı×Áę×ÁĎıÙę×
¸ĝĒęôíÁĎʖĒèÙÒÁĒ İÙîÓĒôĎ ċ ęÙÁîęʖĒ×Á èę×ɼ
ęuĝîLÙÒÁ6Ùî î¸Ù èɷ½ ę í î ÓÁíÁîęèÁ ½ÁĎĒİÙÁı7!nq Ē
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ĒÁîÙôĎİÙ¸ÁċĎÁĒÙ½ÁîęôÒ¸èÙÁîęĒôèĝęÙôîĒɼ{×Áľî î¸Ù èĒÁĎİÙ¸ÁĒ
¸ôíċ îĳ× Ē ¸×ÙÁÒċĎÙİ ¸ĳôÒľ¸ÁĎɷ½ ę ·ĎÁ ¸×îôęÙľ¸ ęÙôî
ċĎôęô¸ôèĒɷ î½ ċĎÙİ ¸ĳ Ùíċ ¸ę ĒĒÁĒĒíÁîę ʊ èè ÁèÁíÁîęĒ
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ęôı Ď½ ÙíċĎôİÙîÓ ę×ÁÙĎ ¸è ĎÙęĳ î½ íÙîÙíÙĺÙîÓ èÁÓ èÁĒÁɼ {×Á
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ľ½Áî¸ÁʊċĎÁ½ ęÁ½ę×ÁîÁıĎĝèÁĒɼ
ʓnÁôċèÁÓÙİÁĝĒę×ÁÙĎÓĎ î½æÙ½Ēʖ î½ÓĎÁ ęʌÓĎ î½æÙ½ĒʖíôîÁĳɼ
uôę×ÁĳĎÁ èèĳîÁÁ½ęô·Á ·èÁęôęĎĝĒęĝĒɷʔQôîęÁÙĎôĒ ĳĒɼʓ{ĎĝĒę
is one of those things that is very hard to build and very easy to
èôĒÁɷ ĒıÁ× İÁ èèĒÁÁîÙîĎÁ¸ÁîęÁİÁîęĒÙîę×ÁíÁ½Ù ɼʔ
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models to “nudge” clients to take actions that are in their best
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Óĝ Ď½ÙîÓ¸ĝĒęôíÁĎ½ ę ɼ!Ďɼ{Ùíôę×ĳĎôîÁɷíÁ½Ù¸ è½ÙĎÁ¸ęôĎɷ
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èÙîÙ¸ɷ Ē ĳĒ ę×Á ôĎÓ îÙĺ ęÙôî ę æÁĒ ¸ ĝęÙôĝĒ ċċĎô ¸× ıÙę×
ÙęĒ½ ę í î ÓÁíÁîęɷÁİÁî ĒôċċôĎęĝîÙęÙÁĒęôĒ× ĎÁ½ ę ıÙę×
ôĝęĒÙ½Áċ ĎęÙÁĒċĎôíÙĒÁęôĳÙÁè½îÁı×Á èę×ÙîĒÙÓ×ęĒę× ę¸ôĝè½
·ÁîÁľęċ ęÙÁîęĒʊ î½ÁİÁî ĒĒôíÁÁîę×ĝĒÙ ĒęÙ¸ċ ęÙÁîęĒı îę
to share more of their data.
“×ĳɽÁ¸ ĝĒÁɷ ĒĎôîÁÁĲċè ÙîĒɷÁİÁîÙÒċ ęÙÁîęĒęĎĝĒę×Á èę×
¸ ĎÁ ċĎôİÙ½ÁĎĒ íôĎÁ ę× î ĒôíÁ ôę×ÁĎ ·ĝĒÙîÁĒĒÁĒ ę× ę í æÁ
×Á ½èÙîÁĒıÙę×½ ę ·ĎÁ ¸×ÁĒɷʓ=ę×Ùîæę× ęɷ ęę×ÙĒċôÙîęɷę× ęʖĒ
ċĎÙİÙèÁÓÁıÁĒęÙèè× İÁę×ÁôċċôĎęĝîÙęĳęôèôĒÁɼʔ
{× ęÙĒ ċĎĝ½Áîę ċċĎô ¸×ɼ“× ęę×ÁîÁı7!nqĎÁčĝÙĎÁíÁîęĒ
ĎÁÁÒÒÁ¸ęÙİÁèĳ½ôÙîÓÙĒċĝĒ×ÙîÓĎÁę ÙèÁĎĒ î½ôę×ÁĎ¸ôíċ îÙÁĒ
ęô·ÁíôĎÁèÙæÁôĎÓ îÙĺ ęÙôîĒÙî×Á èę×¸ ĎÁ î½ľî î¸Ù èĒÁĎ-
İÙ¸ÁĒɷĒ ĳĒ!Á î··ôęęɷ¸ôʌÒôĝî½ÁĎ î½¸×ÙÁÒ½ ę Ē¸ÙÁîęÙĒę ę
SmarterHQ.
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ę×ÁĎÁʖĒ èôęôÒ·ÁĒęċĎ ¸ęÙ¸ÁĒÙîę×ÁĎÁÓĝè ęÙôîĒɷʔ··ôęęĒ ĳĒɷ
îôęÙîÓ ę× ę ę×Á ½½ÙęÙôî è ęĎ îĒċ ĎÁî¸ĳ ĎÁċĎÁĒÁîęĒ ĒęĎôîÓ
ĒęÁċɼ=ęíÁ îĒę× ę½ ę Ē¸ÙÁîęÙĒęĒ× İÁęô·ÁĎÁ ½ĳęôîôęôîèĳ
äĝĒęÙÒĳę×ÁÙĎĝĒÁôÒ¸ĝĒęôíÁĎ½ ę Ùî èÓôĎÙę×íĒɷ·ĝę·ÁĎÁ ½ĳ
ęô¸ôííĝîÙ¸ ęÁ×ôı î½ı×ĳę×Áĳ ĎÁ½ôÙîÓÙęɼ{×ÁÙíċôĎ-
ę î¸ÁôÒę× ęęĎ îĒċ ĎÁî¸ĳ î½½ ę ĒęÁı Ď½Ē×ÙċôîèĳÓĎôıĒ Ē
ôĎÓ îÙĺ ęÙôîĒʖĝĒÁôÒ î èĳęÙ¸ĒÁİôèİÁĒęô=·ĳ ĝęôí ęÙîÓę×Á
models and adding learning elements.
î½ ę× ę íÁ îĒ î èĳęÙ¸Ē ċĎôÒÁĒĒÙôî èĒ îÁÁ½ ęô íôîÙęôĎ ę×Á
ÁİôèİÙîÓĒô¸Ù è¸ôîęĎ ¸ęÒôĎĝĒÙîÓ½ ę ęô¸ĝĒęôíÁĎĒʖ·ÁîÁľęɼ
ʓ“ÁİÙÁıôĝĎĒÁèİÁĒ Ē ¸ĝĒęôíÁĎʌľĎĒę¸ôíċ îĳɷ î½ıÁ ĎÁ
îôę×ÙîÓıÙę×ôĝęôĝĎ¸ĝĒęôíÁĎĒʖĒĝ¸¸ÁĒĒʊ î½ę×Áîę×ÁÙĎęĎĝĒęɷ
ĝèęÙí ęÁèĳɷʔĒ ĳĒ ęÁĎċÙèè ĎʖĒ’ ıęÁĎɼʓî½ÙÒıÁʖĎÁîôę¸ĎÁ ęÙîÓ
İ èĝÁÒĎôíę×Á½ ę ę× ęıÁʖĎÁ¸ôèèÁ¸ęÙîÓÒĎôíę×Áíɷę×ÁîıÁ
shouldn’t be using it at all.” O
“We view ourselves as a customer-
first company, and we are nothing
without our customers’ success —
and then their trust, ultimately.”
MORGAN VAWTER, CATERPILLAR

13
DataSF Teaches the Art of Asking
Analytical Questions
INDUSTRY SNAPSHOT
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ÙíċĎôİÁċĝ·èÙ¸ĒÁĎİÙ¸ÁĒɼ
{× ę ċċĎô ¸×× ĒèÁ½ęôċĎôäÁ¸ęĒĒĝ¸× ĒıôĎæıÙę×ę×Á!Áċ ĎęíÁîęôÒnĝ·èÙ¸;Á èę×ęô
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½ ę Ē¸ÙÁî¸ÁıÙę×Ùîę×ÁÙĎôıîİÁĎęÙ¸ èĒɼ
ʓ“ÁʖİÁ½ÁİÁèôċÁ½ĒôíÁę×ÙîÓ¸ èèÁ½ ċĎôäÁ¸ęęĳċôèôÓĳę× ęıÁĝĒÁęô×ÁèċĒôèÙ¸Ùę î½½ÁľîÁ
½ ę Ē¸ÙÁî¸ÁčĝÁĒęÙôîĒıÙę×ôĝĎ½Áċ ĎęíÁîęĒɼuôıÁ½ôîʖęĒ ĳɷʕ;Áĳɷ½ôĳôĝı îęęô½ô= î½
½ ę Ē¸ÙÁî¸Áɽʖ“ÁĒ ĳɷʕ;ÁĎÁ ĎÁę×ÁæÙî½ĒôÒčĝÁĒęÙôîĒɷ î½×ÁĎÁ ĎÁ ·ĝî¸×ôÒÁĲ íċèÁĒ
ę× ęıÁ¸ î×Áèċĳôĝ îĒıÁĎɷʖʔôî ÓĝĎôÁĲċè ÙîĒɼʓ=ÒıÁęĎ ÙîôĝĎ½Áċ ĎęíÁîęĒęôĒċôę½ ę
Ē¸ÙÁî¸ÁôċċôĎęĝîÙęÙÁĒɷę×Áîę× ęʖĒ×ôııÁĒċĎÁ ½Ùęę×ĎôĝÓ×ôĝęę×ÁôĎÓ îÙĺ ęÙôîɼʔ
ôî ÓĝĎô¸ĎÁ½ÙęĒę×ÁıôĎæôÒôę×ÁĎĒÙîę×Áċĝ·èÙ¸ĒÁ¸ęôĎɷÙî¸èĝ½ÙîÓRÁıWĎèÁ îĒʖRWL-
èĳęÙ¸Ēɷı×Ù¸×½ÁİÁèôċÁ½ î î èĳęÙ¸ĒęôċôèôÓĳę× ęĒċÁ æĒęô¸ôííôî·ĝĒÙîÁĒĒċĎô·èÁíĒɼ
! ę u6× ĒċôĒęÁ½ îôîèÙîÁÙîÒôĎí ęÙôîĒ×ÁÁęę× ę¸ ęÁÓôĎÙĺÁĒę×ÁæÙî½ĒôÒċĎô·èÁíĒ½ ę
Ē¸ÙÁî¸Á¸ îĒôèİÁɷĒĝ¸× Ēľî½ÙîÓę×ÁîÁÁ½èÁÙî × ĳĒę ¸æʈľÓĝĎÙîÓôĝęı×ÁĎÁęô½ÙĎÁ¸ę
èÙíÙęÁ½ĎÁĒôĝĎ¸ÁĒʉɷċĎÙôĎÙęÙĺÙîÓ · ¸æèôÓɷĿ ÓÓÙîÓÙíċôĎę îęÙęÁíĒÁ ĎèĳÒôĎ ¸ęÙôîɷʂ
ęÁĒęÙîÓʈęôľî½ôĝęı×Ù¸×¸ôííĝîÙ¸ ęÙôîĒęĳèÁıôĎæĒ·ÁĒęʉɷ î½ôċęÙíÙĺÙîÓĎÁĒôĝĎ¸ÁĒɼ
GOVERNMENT
Joy Bonaguro,
former chief data officer,
city and county of
San Francisco
If we train our departments to spot data
science opportunities, then that’s how we
spread it throughout the organization.
”
“

14
Cross-Functional Teamwork Improves
Predictive Models at Barclays US
INDUSTRY SNAPSHOT
nĝęęÙîÓ î èĳęÙ¸ĒÁĲċÁĎęĒĒ×ôĝè½ÁĎęôĒ×ôĝè½ÁĎıÙę×·ĝĒÙîÁĒĒ½ôí ÙîÁĲċÁĎęĒ½ôÁĒîʖęäĝĒę
·ĝÙè½ ĒęĎôîÓÁĎ¸ĝèęĝĎÁɼę Ď¸è ĳĒuɷÙę× Ē·ĝÙèę·ÁęęÁĎ î èĳęÙ¸Ēɼ
{×ÁċĎÙí ĎĳÒô¸ĝĒôÒ î èĳęÙ¸Ē ęę×Á· îæ î½¸ĎÁ½Ùę¸ Ď½ÙĒĒĝÁĎÙĒę×Á¸ĝĒęôíÁĎäôĝĎîÁĳ
ʊÓÁęęÙîÓęôę×ÁĎÙÓ×ę¸ĝĒęôíÁĎ ęę×ÁĎÙÓ×ęęÙíÁıÙę×ę×ÁĎÙÓ×ęæÙî½ôÒôÒÒÁĎɷ ¸¸ôĎ½ÙîÓęô
’ÙĒ× èQôĎ½ÁɷİÙ¸ÁċĎÁĒÙ½ÁîęôÒ½ ę Ē¸ÙÁî¸Á î½ ½İ î¸Á½ î èĳęÙ¸Ēɼʓ=Òĳôĝ¸ î ¸ęĝ èèĳ
¸×ÙÁİÁę× ęɷÙęıÙèèí æÁĳôĝĎ·ĝĒÙîÁĒĒíôĎÁċĎôľę ·èÁɼî½ĳôĝĎ¸ĝĒęôíÁĎĒıÙèè·Á× ċċĳ
·Á¸ ĝĒÁę×ÁĳʖĎÁÓÁęęÙîÓı× ęę×ÁĳʖĎÁĎÁ èèĳèôôæÙîÓÒôĎɼʔ
æÁĳ¸ôíċôîÁîęôÒę×Á¸ôíċ îĳʖĒ î èĳęÙ¸Ē ċċĎô ¸×ÙĒęôę ċ·ĝĒÙîÁĒĒ½ôí ÙîÁĲċÁĎęÙĒÁ
ę×ĎôĝÓ×ôĝę ċĎôäÁ¸ęɷQôĎ½ÁĒ ĳĒɼ
Ď¸è ĳĒu× ĒÙîęÁÓĎ ęÁ½·ĝĒÙîÁĒĒċÁôċèÁÙîęôę×Á î èĳęÙ¸ĒċĎô¸ÁĒĒ·ĳ¸ĎÁ ęÙîÓ ½ ę Ē¸Ù-
Áî¸Áè · î½ĒÁęęÙîÓĝċ ¸ĎôĒĒʌÒĝî¸ęÙôî èęÁ íę× ęÙî¸èĝ½ÁĒ·ĝĒÙîÁĒĒôıîÁĎĒɼʓ{×ÁĳıÁĎÁ
ċ ĎęôÒę×Á¸ĎÁ ęÙôîôÒę×ÙĒ½ ę Ē¸ÙÁî¸Áè ·ɷʔQôĎ½ÁÁĲċè ÙîĒɼʓÁ¸ ĝĒÁę×ÁĳʖĎÁÒ ÙĎèĳÙîęÁ-
ÓĎ ęÁ½ĝċÒĎôîęɷıÁ¸ôĝè½ ¸ęĝ èèĳîôıĒÁęĝċę×ÙĒı×ôèÁÁîİÙĎôîíÁîęɷĒÁęĝċċĎôäÁ¸ęĒı×Ù¸×
ıôĝè½ ¸ęĝ èèĳ½ÁèÙİÁĎİ èĝÁ î½×Áèċę×ÁíęôĒôèİÁę×Á·ĝĒÙîÁĒĒċĎô·èÁíɼʔ
6ôĎÁĲ íċèÁɷ ċĎôäÁ¸ęęô¸ĎÁ ęÁ ·ÁęęÁĎċĎÁ½Ù¸ęÙİÁíô½ÁèÒôĎ½ÁęÁĎíÙîÙîÓı×ôıÙèèĎÁĒċôî½
ęô ¸ÁĎę ÙîæÙî½ôÒôÒÒÁĎÙî¸èĝ½Á½·ôę×½ ę Ē¸ÙÁîęÙĒęĒ î½ ¸čĝÙĒÙęÙôîí ĎæÁęÙîÓĒę ÒÒɼʓ{×Á
½ ę Ē¸ÙÁîęÙĒęıôĝè½Ē ĳɷʕ;Áĳɷ=ʖíèôôæÙîÓ ęę×ÙĒ½ ę ɷ î½ę×ÁĎÁʖĒĒôíÁĒÁ Ēôî èÙęĳęôÙęɼʖ
î½ ·ĝĒÙîÁĒĒċÁĎĒôîıÙèèĒ ĳɷʕ•Á ×ɷę× ęí æÁĒĒÁîĒÁɼ=ę× Ēęô·Áę×Á×ôèÙ½ ĳĒÁ Ēôîɼ
{× ęʖĒı×ÁĎÁċÁôċèÁĒę ĎęĎÁĒċôî½ÙîÓɼʖʔ
Á¸ ĝĒÁÙîęÁĎ ¸ęÙôîĒèÙæÁę×ÙĒıÁĎÁ× ċċÁîÙîÓÁ ĎèÙÁĎÙîę×ÁċĎô¸ÁĒĒɷ·ÁÒôĎÁ½ ę Ē¸ÙÁîęÙĒęĒ
¸ęĝ èèĳċĎô½ĝ¸Á½ íô½Áèɷę×ÁęÁ íĒıÁĎÁ ·èÁęôċĎô½ĝ¸Á·ÁęęÁĎíô½ÁèĒíôĎÁčĝÙ¸æèĳɷ
QôĎ½ÁĒ ĳĒɼĳę ċċÙîÓę×Á¸ôîĒĝíÁĎÙîĒÙÓ×ę î½ĒôíÁôÒę×Á îÁ¸½ôę è×ĳċôę×ÁĒÁĒę× ę
·ĝĒÙîÁĒĒÁĲċÁĎęĒ× İÁɷ î½ęÁĒęÙîÓę×ÁíôĝęıÙę× ½İ î¸Á½½ ę Ē¸ÙÁî¸ÁíÁę×ô½Ēɷ Ď¸è ĳĒ
uı Ē ·èÁęôÙíċĎôİÁċĎÁ½Ù¸ęÙôîċôıÁĎĒÙÓîÙľ¸ îęèĳʊÙîĒôíÁ¸ ĒÁĒ·ĳɛɖʾɷ×Á ½½Ēɼ
“ÙîĒèÙæÁę× ę½ôîʖę¸ôíÁÒĎôíäĝĒę½ ę ɷíÁę×ô½ĒɷôĎ î èĳęÙ¸ĒęôôèĒɷQôĎ½ÁĒ ĳĒɶʓ=ęı Ē
ę× ęıÁıÁĎÁ ·èÁęôęĎ îĒÒÁĎĒôíÁôÒę×Á½ôí ÙîæîôıèÁ½ÓÁÒĎôíę×Áí ĎæÁęÙîÓÒôèæĒÙîęô
ôĝĎíô½ÁèĒɼ•ôĝʖĎÁ ¸ęĝ èèĳÙî¸ôĎċôĎ ęÙîÓĳÁ ĎĒ î½ĳÁ ĎĒôÒÁĲċÁĎęæîôıèÁ½ÓÁę× ęċÁôċèÁ
Ó ę×ÁĎÁ½ ·ôĝę¸ôîĒĝíÁĎ·Á× İÙôĎ î½¸ôîĒĝíÁĎîÁÁ½Ē î½ı îęĒɼʔ
FINANCIAL
SERVICES
Vishal Morde,
vice president of data
science and advanced
analytics, Barclays US
“
You’re actually incorporating years and years of
expert knowledge that people gathered about consumer
behavior and consumer needs and wants.
”

Building Trust in
Innovation by Creating
a Culture of Inquiry and
Experimentation
G
ame-changing insights can result from investments in
½ ę ÙîÒĎ ĒęĎĝ¸ęĝĎÁɷę èÁîęɷ î½ęÁ¸×îôèôÓĳɼĝęĒÙíċèĳ
ÓÁîÁĎ ęÙîÓ î èĳęÙ¸ èľî½ÙîÓĒÙĒîôęę×ÁĝèęÙí ęÁÓô è
ôÒę×Á î èĳęÙ¸ĒèÁ ½ÁĎĒıÁÙîęÁĎİÙÁıÁ½ÒôĎę×ÙĒĳÁ ĎʖĒ! ę ˂î-
èĳęÙ¸ĒĎÁċôĎęɼèèę×ôĒÁÙîİÁĒęíÁîęĒęĎĝèĳċ ĳôÒÒı×ÁîċÁôċèÁ
ę×ĎôĝÓ×ôĝę îôĎÓ îÙĺ ęÙôî ¸¸Áċęę×ôĒÁÙîĒÙÓ×ęĒ î½ċĝęę×Áí
ęôıôĎæęôí æÁ½Á¸ÙĒÙôîĒɷĎÁ½ÁĒÙÓî·ĝĒÙîÁĒĒċĎô¸ÁĒĒÁĒɷ î½ĎÁ-
ę×ÙîæĒęĎ ęÁÓĳɼuôɷı× ę ĎÁĒôíÁę ¸ęÙ¸ĒĝĒÁ½ęô¸ĎÁ ęÁ ¸ĝèęĝĎÁ
Ùîı×Ù¸×ċÁôċèÁ ĒæčĝÁĒęÙôîĒɷĒÁÁæ½ ę î½ î èĳęÙ¸Ēę× ę¸ î
×Áèċ îĒıÁĎę×ôĒÁčĝÁĒęÙôîĒɷ î½ę×Áî ċċèĳę×ÁĎÁĒĝèęĒɽ
Ď¸è ĳĒu ċċôÙîęÁ½ î î èĳęÙ¸ĒèÁ ½ÁĎÒôĎÁ ¸×·ĝĒÙîÁĒĒĝîÙę
î½ÙîĒę èèÁ½¸ĎôĒĒʌÒĝî¸ęÙôî èęÁ íĒí ½ÁĝċôÒ î èĳęÙ¸Ē î½
·ĝĒÙîÁĒĒÁĲċÁĎęĒÙî ½ ę Ē¸ÙÁî¸Áè ·ɼ ęÁĎċÙèè ĎĎĝîĒ î îîĝ è
ʓî èĳęÙ¸ĒRôıʔ¸ôîÒÁĎÁî¸ÁÒôĎĒę æÁ×ôè½ÁĎĒ ¸ĎôĒĒę×Á¸ôíċ -
îĳęôèÁ Ďî ·ôĝę¸ ċ ·ÙèÙęÙÁĒ î½Ē×ôı¸ ĒÁĒĝ¸¸ÁĒĒÒĝèċĎôäÁ¸ęĒɷ
ı×ÙèÁĒÁÁæÙîÓÙîċĝę î½¸ôèè ·ôĎ ęÙôîÒĎôí î ½İÙĒôĎĳ¸ôĝî¸Ùè
ôÒÁîÓÙîÁÁĎĒɷę èÁîęí î ÓÁĎĒɷ î½ôę×ÁĎ½ôí ÙîÁĲċÁĎęĒɼ{×Á
èÁİÁè î½èÙîÙ¸× ĒĒÁęĝċÙęĒôıî¸ôĝî¸ÙèɷıÙę× îôċÁî½ôôĎęô
ċ ĎęÙ¸Ùċ ęÁ î½ċĎôİÙ½ÁÒÁÁ½· ¸æôîÙęĒ î èĳęÙ¸ĒċĎôÓĎ íɼ
WĝĎĒĝĎİÁĳÙîİÁĒęÙÓ ęÁ½ Ď îÓÁôÒċĎ ¸ęÙ¸ÁĒ ĒĒô¸Ù ęÁ½ıÙę×
·ĝÙè½ÙîÓ ¸ĝèęĝĎÁôÒ î èĳęÙ¸ĒɷĒĝ¸× Ē ¸ęÙôîĒ î½íÁĒĒ ÓÙîÓ
ÒĎôíèÁ ½ÁĎĒ×ÙċɷıôĎæÒôĎ¸Á½ ę èÙęÁĎ ¸ĳÁÒÒôĎęĒɷ î½ôĎÓ îÙĺ -
ęÙôî è¸×ôÙ¸ÁĒɼWîę×Áı×ôèÁɷę×ôĒÁı×ôĎÁċôĎęę×ÁíôĒę ¸ęÙİÙęĳ
on these fronts are also more likely to exhibit the most trust
Ùî½ ę î½ î èĳęÙ¸Ē î½·Áôîę×ÁıÙîîÙîÓĒÙ½ÁôÒę×ÁʓĝęÙèÙęĳ
Ó ċɷʔíÁ îÙîÓę×Áĳîôęôîèĳ× İÁ ¸¸ÁĒĒęô½ ę ·ĝę× İÁę×Á
right data to inform their business decisions.
{ıôôĎÓ îÙĺ ęÙôî èÒ ¸ęôĎĒÁíÁĎÓÁ½ę× ęí ĳ× İÁ ·Á ĎÙîÓ
ôî½ĎÙİÙîÓ î èĳęÙ¸Ēí ęĝĎÙęĳɶ
1. WîÁ ÙĒ × İÙîÓ ¸èÁ Ď èÁ ½ÁĎ ÒôĎ ę×Á ¸ôíċ îĳʖĒ î èĳęÙ¸Ē
ÁÒÒôĎęɶəəʾôÒę×ôĒÁı×ôĎÁċôĎęę× ęę×ÁÙĎôĎÓ îÙĺ ęÙôî× Ē ¸×ÙÁÒ
½ ę ôÒľ¸ÁĎôĎ ¸×ÙÁÒ î èĳęÙ¸ĒôÒľ¸ÁĎ ĎÁíôĎÁèÙæÁèĳęôĒ ĳę×Áĳ
ÒĎÁčĝÁîęèĳôĎ èı ĳĒ× İÁę×Á½ ę ę×ÁĳîÁÁ½ÒôĎ½Á¸ÙĒÙôîĒɼ{×Á
Ē íÁ¸ôĎĎÁè ęÙôîÙĒċĎÁĒÁîęÒôĎę×ÁəɟʾôÒĎÁĒċôî½ÁîęĒı×ôĎÁċôĎę
that their organizations have centralized data analytics functions.
ʈ!ÁĒċÙęÁę×ÙĒľî½ÙîÓɷôîÁĒÙĺÁí ĳîôęľę èèɶuôíÁ î èĳęÙ¸Ē
èÁ ½ÁĎĒľî½ ½ÙĒęĎÙ·ĝęÁ½ ċċĎô ¸×ÙĒĎÙÓ×ęÒôĎę×ÁÙĎÁîęÁĎċĎÙĒÁɼʉ
2. {×Á ĒĝĎİÁĳ èĒô Òôĝî½ ę× ę Ùî í îĳ ôĎÓ îÙĺ ęÙôîĒɷ èÁ ½ÁĎĒ
ĎÁċè ĳÙîÓ îÙíċôĎę îęĎôèÁÙîĝĒÙîÓ î½¸× íċÙôîÙîÓ î èĳę-
Ù¸Ēɼ;ôıÁİÁĎɷèÁ ½ÁĎĒʖ ¸ęÙôîĒí ĳ·ÁĒèÙÓ×ęèĳèôĝ½ÁĎę× îę×ÁÙĎ
ıôĎ½Ēɶ{×Áĳ ċċÁ Ďęô·ÁíôĎÁèÙæÁèĳęôĒÁÁæôĝę½ ę î½ĝĒÁÙę
Ùî½Á¸ÙĒÙôîʌí æÙîÓę× îęô¸× íċÙôî î èĳęÙ¸ĒôĎ¸ĎÁ½ÙęÙęıÙę×
½ÁèÙİÁĎÙîÓ·ĝĒÙîÁĒĒĎÁĒĝèęĒʈĒÁÁ6ÙÓĝĎÁɗɖʉɼLÁ ½ÁĎĒ×ÙċĒĝċċôĎęÙĒ
èĒôĒôÒęÁĎı×ÁîÙę¸ôíÁĒęôċĎÙôĎÙęÙĺÙîÓÙîİÁĒęíÁîęÙî î èĳęÙ¸Ēɼ
11%
25%
33%
21%
9%
13%
29%
30%
20%
8%
19%
32%
30%
15%
4%
17%
36%
33%
11%
3%
19%
40%
29%
3%
9%
3%
9%
16%
40%
32%
Prioritize
investments in
analytics tools
Credit positive
business outcomes
to analytics in
internal messages
or presentations
Champion the value
and use of analytics
Incorporate data
and analytics in
decision-making
Seek data and
analytics support
decisions
Understand
insights presented
by analytics
specialists
Figure 10: Leaders Set the Tone for Analytics Adoption
How often do leaders:
Leaders at
the majority
of companies
oftenchampion
the value of
data and
activelyseek
to apply it
when making
decisions.
Percentages may
not equal 100 due
to rounding.
Prioritize
investments in
analytics tools
Credit positive
business outcomes
to analytics in
internal messages
or presentations
Champion the
value and use of
analytics
Incorporate data
and analytics in
decision-making
Seek data
and analytics
to support
decisions
Understand
insights
presented
by analytics
specialists
15

Driving Data Literacy Through the Workforce
WîÁ ĎÁ ı×ÁĎÁèÁ ½ÁĎĒ×ÙċíÙÓ×ę½ôíôĎÁÙĒ î èĳęÙ¸ĒĒæÙèèĒÙî
ę×ÁıôĎæÒôĎ¸Áɶ{×ÙĒı Ē¸ÙęÁ½ Ē ęôċ¸× èèÁîÓÁęôÙîîôİ ęÙîÓ
·ĳəɟʾôÒĎÁĒċôî½ÁîęĒɷĒÁ¸ôî½ôîèĳęô¸ôíċÁęÙęÙôîÒĎôíôę×ÁĎ
ċĎÙôĎÙęÙÁĒɼ“ÁÒôĝî½ę× ęı×ÙèÁ Ď îÓÁôÒÁÒÒôĎęĒęô·ĝÙè½½ ę
èÙęÁĎ ¸ĳÙîę×ÁıôĎæÒôĎ¸ÁÙĒ ęèÁ ĒęÙîę×Áċè îîÙîÓĒę ÓÁĒ ę
í äôĎÙęĳôÒĎÁĒċôî½ÙîÓôĎÓ îÙĺ ęÙôîĒɷÒÁıÁĎę× îôîÁÙîľİÁ ĎÁ
¸ęÙİÁèĳÁîÓ ÓÁ½Ùîę× ę ¸ęÙİÙęĳʈĒÁÁ6ÙÓĝĎÁɗɗʉɼuèÙÓ×ęèĳíôĎÁ
ĎÁ×ÁèċÙîÓ î èĳęÙ¸ĒÁĲċÁĎęĒ·ĝÙè½·ĝĒÙîÁĒĒ½ôí ÙîÁĲċÁĎęÙĒÁɼ
%î¸ôĝĎ ÓÙîÓ ½ ę ʌ½ĎÙİÁî¸ĝèęĝĎÁíÁ îĒÁî¸ôĝĎ ÓÙîÓċÁôċèÁ
to use the data the business is collecting instead of siloing
Ùę ı ĳÙîę×Á={½Áċ ĎęíÁîęɷĒ ĳĒJÙĎæôĎîÁɷċĎÙî¸Ùċ è½ ę
scientist and executive advisor at consultancy Booz Allen Ham-
Ùèęôîɼʓ{×ÙĒ¸ĝèęĝĎÁôÒÁĲċÁĎÙíÁîę ęÙôîĒĝċċôĎęĒ îÙîîôİ ęÙôî
¸ĝèęĝĎÁÙî ·ĝĒÙîÁĒĒɼ=ęʖĒı×ÁĎÁċÁôċèÁ× İÁę×ÁÒĎÁÁ½ôíęô
Ēæɷʕ7ÙİÁîôĝĎ½ ę ɷ×ôı¸ îıÁ½ô·ÁęęÁĎɽʖʔ×ÁĒ ĳĒɼ
WîÁÁĲ íċèÁôÒ·ĎÙîÓÙîÓ½ ę èÙęÁĎ ¸ĳęôę×ÁÒĎôîęèÙîÁĒôÒ·ĝĒÙ-
îÁĒĒ¸ôíÁĒÒĎôíHÁ îîÁqôĒĒɷ ċĎÙî¸Ùċ èĎÁĒÁ Ď¸×Ē¸ÙÁîęÙĒę ę
ę×ÁQ={ÁîęÁĎÒôĎ=îÒôĎí ęÙôîuĳĒęÁíĒqÁĒÁ Ď¸×ɼ
{×Á ¸ÁîęÁĎ Ēęĝ½ÙÁ½ ę×Á ɝʌ%èÁİÁî ¸ôîİÁîÙÁî¸Á ĒęôĎÁ ¸× Ùî Ùî
H ċ îɷı×Ù¸×ÁíċèôĳÁ½¸ôĝîĒÁèôĎĒęô×ÁèċɘɖɖɷɖɖɖĒ èÁĒċÁô-
ċèÁÙîęÁĎċĎÁę î½èÁ ĎîÒĎôíę×Á½ ę ę×Á¸ôíċ îĳ¸ôèèÁ¸ęÁ½
·ôĝę½ ÙèĳĒ èÁĒɼu èÁĒĒę ÒÒæîôı æÁĳĒĝ¸¸ÁĒĒÒ ¸ęôĎÙĒę×ÁÙĎ
Ē×ôċʖĒ ·ÙèÙęĳęô× İÁĎ ċÙ½ÙîİÁîęôĎĳęĝĎîôİÁĎɷĒôę×ÁĳíôîÙęôĎ
sales of different items in their assigned section of the store.
ôĝîĒÁèôĎĒİÙĒÙęÁ½ę×ÁĒęôĎÁĒęôęÁ ¸×ę×ÁĒ èÁĒċÁôċèÁ×ôıęô
ę×Ùîæ î èĳęÙ¸ èèĳɷĝĒÙîÓ½ ę ·ôĝęĒ èÁĒôÒ½ÙÒÒÁĎÁîęÙęÁíĒÙî
ĎÁ¸Áîę½ ĳĒ¸ôíċ ĎÁ½ęôę×ÁĳÁ ĎÁ ĎèÙÁĎ î½¸ôíċ ĎÁ½ęô½ ĳĒ
ıÙę×ĒÙíÙè ĎıÁ ę×ÁĎċ ęęÁĎîĒɼ
ʓ{×Á ¸ôĝîĒÁèôĎĒ Ē ĳ ęô ę×Áíɷ ʕuôɷ ı× ę ½Ù½ ĳôĝ ×ĳċôę×ÁĒÙĺÁ
·ôĝęı× ęĳôĝʖ½ĒÁèèè ĒęıÁÁæɽʖ{×Áĳæîôıę×Á îĒıÁĎęôę×Á
čĝÁĒęÙôî·Á¸ ĝĒÁÙęʖĒı× ęę×ÁĳôĎ½ÁĎÁ½ɷʔqôĒĒĒ ĳĒɼʓ{×Áîę×Áĳ
Ē ĳɷʕ;ôı½Ù½ĳôĝ½ôɽ;ôı½Ù½ĳôĝĎ×ĳċôę×ÁĒÁĒęĝĎîôĝęɽʖ“Áèèɷ
ę×Áĳ× İÁę×Á îĒıÁĎęôę× ęčĝÁĒęÙôîĒÙęęÙîÓÙîÒĎôîęôÒę×Áíɷ
too. They have the data.” Then the counselors and sales staff
½ÙĒ¸ĝĒĒĒęĎ ęÁÓÙÁĒÒôĎÙíċĎôİÙîÓĒ èÁĒɼ
Fostering Collaboration Drives Culture Change
=ęʖĒı×Áîę×Á î èĳęÙ¸ĒÁĲċÁĎęíÁÁęĒę×Á·ĝĒÙîÁĒĒ½ôí Ùî î½
true collaboration ensues that the culture really begins to
ęĎ îĒÒôĎíɷ ¸¸ôĎ½ÙîÓęôİÙĎęĝ èèĳ èèę×ÁċĎ ¸ęÙęÙôîÁĎĒıÁÙîęÁĎ-
İÙÁıÁ½ÒôĎę×ÙĒĎÁċôĎęɼ=îęÁĎÁĒęÙîÓèĳɷôĝĎĒĝĎİÁĳĎÁĒċôî½ÁîęĒ
“Encouraging a data-driven culture
means encouraging people to use the
data the business is collecting instead
of siloing it away in the IT department.”
KIRK BORNE, BOOZ ALLEN HAMILTON
Currently do this Implementing
17%
18%
19%
18%
21%
19%
15%
16%
17%
18%
30%
19%
16%
14%
17%
19%
22%
20%
15%
Line-of-business
experts receive
training or
immersion in
analytics
Analytics
specialists receive
training or
immersion in
operational areas
Training programs
are widely available
to develop data and
analytical skills
Workforce data
literacy is regularly
assessed
Internal messaging
promotes data
literacy as a valued
skill
Planning
Considering No activity
30%
29%
17%
35%
17%
25%
Manyorganizationshaveanopportunitytodomoretotackletheanalyticsskills
shortage.Thegoodnewsisthatamajorityaretakingactiontobuildadata-driven
workforce,withprogramseitherrunningorintheplanningorimplementationstages.
Figure 11: Educate to Innovate
Line-of-business
experts receive
training or immersion
in analytics
Analytics specialists
receive training
or immersion in
operational areas
Training programs
are widely available
to develop data and
analytical skills
Workforce data
literacy is regularly
assessed
Internal messaging
promotes data
literacy as a
valued skill
16

17
·ĳ ĒèÙÓ×ęí ĎÓÙîċôÙîęęôÙî½ÙİÙ½ĝ èĒ î½ęÁ íĒÙîôċÁĎ ęÙîÓ
ĝîÙęĒ Ē·ÁÙîÓíôĒęèÙæÁèĳęô½ĎÙİÁÙîîôİ ęÙôîıÙę×ÁíÁĎÓÙîÓ
ęÁ¸×îôèôÓÙÁĒɷ ×Á ½ôÒęôċèÁ ½ÁĎĒ×Ùċ î½ÁİÁî={ʈĒÁÁ6ÙÓĝĎÁ
ɗɘʉɼ{× ęĝî½ÁĎĒ¸ôĎÁĒı×ĳ·ĝÙè½ÙîÓęÙÁĒıÙę×ę×Á·ĝĒÙîÁĒĒí ĳ
·Á ıÙîîÙîÓĒęĎ ęÁÓĳÒôĎ î èĳęÙ¸ĒèÁ ½ÁĎĒɼ
Establishing and maintaining a culture that embraces analytics
Ēę ĎęĒ ıÙę× Ù½ÁîęÙÒĳÙîÓ ôċċôĎęĝîÙęÙÁĒ î½ ĝĒÁ ¸ ĒÁĒ ę× ę ıÙèè
make a difference.
ęÙĎ·î·ɷ ċè ęÒôĎíę× ę× Ē½ÙĒĎĝċęÁ½ę×Á×ôĒċÙę èÙęĳÙî½ĝĒ-
ęĎĳɷÁİÁĎĳę×ÙîÓ·ÁÓÙîĒıÙę× î èĳęÙ¸Ē î½ ĒęĎôîÓęôċʌ½ôıî
½ ę ¸ĝèęĝĎÁɷ ¸¸ôĎ½ÙîÓęô• Ē×J î½ĳ è ɷ×Á ½ôÒÓèô· è·ĝĒÙ-
îÁĒĒ î èĳęÙ¸ĒÒôĎ¸ôííĝîÙęĳĒĝċċôĎęɼ
RôîÁę×ÁèÁĒĒɷJ î½ĳ è ĒęÙèèîÁÁ½Ēęô× İÁę×ÁĎÙÓ×ę½ ę î½Áî-
Ó ÓÁę×ÁĎÙÓ×ęċÁôċèÁÙîôĎ½ÁĎęôľî½ôċċôĎęĝîÙęÙÁĒÒôĎ î èĳę-
Ù¸Ēęô½ĎÙİÁĒęĎ ęÁÓĳɼęÙĎ·î·ɷÙęʖĒ íĝèęÙ½ÙĎÁ¸ęÙôî è½ĳî íÙ¸ɼ
uôíÁęÙíÁĒ¸ôèèÁ ÓĝÁĒ¸ôíÁıÙę×čĝÁĒęÙôîĒ î½ı îę îĒıÁĎĒɼ
ęôę×ÁĎęÙíÁĒɷ×ÙĒęÁ íí ĳîôęÙ¸Á îÙíċôĎę îęÙîĒÙÓ×ęÙîę×Á
½ ę î½ĒÁÁæęôÓ ÙîÁĲÁ¸ĝęÙİÁĒʖ ęęÁîęÙôî î½ĒĝċċôĎęɼ
J î½ĳ è Ē ĳĒ Ó ÙîÙîÓ ÙîĿĝÁî¸Á ÒôĎ î èĳęÙ¸Ē ÙîĒÙÓ×ęĒ ÓÁî-
ÁĎ ęÁ½·ĳ×ÙĒęÁ í¸ î·Á¸× èèÁîÓÙîÓɶʓ•ôĝí ĳ× İÁ İÁĎĳ
¸ôôèÙ½Á ɷ·ĝęÙÒÙęʖĒîôęęĳÙîÓ· ¸æęôæÁĳĒę æÁ×ôè½ÁĎĒʖÓô èĒôĎ
ĒęĎ ęÁÓĳɷę×Áîę×ÁĎÁʖĒÓôÙîÓęô·ÁİÁĎĳèÙęęèÁ ęęÁîęÙôîċ Ù½ęô
Ùęɼʔ;ÁĒÁÁæĒęôÙ½ÁîęÙÒĳċĎôäÁ¸ęĒę× ę¸ î×Áèċ½ĎÙİÁÙíċôĎę îę
Óô èĒÒôĎę×Á¸ôíċ îĳɼʓ{× ę¸ îÓô èôîÓı ĳęô¸ĎÁ ęÁę×ôĒÁ
ôċċôĎęĝîÙęÙÁĒ î½ċĎôäÁ¸ęĒı×Ù¸×ıÙèè·Á· ĒÁ½ôî î èĳęÙ¸Ē Ē
the backbone and that are tied to a business outcome.”
Analytics Expertise: Centralize vs. Decentralize
ĒîôęÁ½ɷ¸ÁîęĎ èÙĺ ęÙôîÙĒîʖęÒôĎÁİÁĎĳôîÁɼęuĝîLÙÒÁ6Ùî î¸Ù èɷ
Áí·Á½½ÙîÓ î èĳęÙ¸Ē ¸ ċ ·ÙèÙęÙÁĒ Ùî ę×Á ·ĝĒÙîÁĒĒ Ď ę×ÁĎ ę× î
ÁíċèôĳÙîÓ ¸ÁîęĎ èÙĺÁ½ î èĳęÙ¸Ē Òĝî¸ęÙôî ÁîĒĝĎÁĒ ĒęĎ ęÁÓÙ¸
èÙÓîíÁîę î½ęĎ îĒċ ĎÁî¸ĳɼ
ʓ{×ÁÙ½Á ôÒ¸ÁîęĎ èÙĺÙîÓ î½¸ĎÁ ęÙîÓ ·ÙÓÒĝî¸ęÙôî î½ċĝęęÙîÓ
èôęôÒíôîÁĳÙîÙę¸ÁîęĎ èèĳÙĒİÁĎĳ èèĝĎÙîÓɷ·Á¸ ĝĒÁÙęĒôĝî½Ē
èÙæÁĳôĝ ĎÁÓôÙîÓęôĒôèİÁ èèôÒĳôĝĎ½ ę ċĎô·èÁíĒɷʔĒ ĳĒ%ĎÙ¸
QôîęÁÙĎôɷĒÁîÙôĎİÙ¸ÁċĎÁĒÙ½ÁîęôÒ¸èÙÁîęĒôèĝęÙôîĒɼʓ{×Á·ÁîÁľę
ôÒîôę× İÙîÓ½ôîÁę× ęÙĒę× ęÙęʖĒċĎÁęęĳİÙĒÙ·èÁęôĝĒı× ęę×Á
Ùíċ ¸ęÙĒı×Áîę×Á î èĳęÙ¸ĒÙĒıÙę×ę×Á·ĝĒÙîÁĒĒ î½ÒôĎę×Á
·ĝĒÙîÁĒĒɷ î½èÙîæÁ½Ùîęôę×ÁċĎô¸ÁĒĒÁĒę× ę ĎÁĎÁčĝÙĎÁ½ɼʔ
uĝîLÙÒÁ î èĳęÙ¸Ē î½·ĝĒÙîÁĒĒèÁ ½ÁĎĒ× İÁ¸ô½ÙľÁ½ę×ÁÙĎ¸ôè-
è ·ôĎ ęÙôî ôî ¸ ĒÁʌ·ĳʌ¸ ĒÁ · ĒÙĒɼ %İÁĎĳ î èĳęÙ¸Ē ċĎôÓĎ í
ĎÁčĝÙĎÁĒ½ÁİÁèôċÙîÓ îqW=¸ ĒÁ î½ıÙîîÙîÓ·ĝ½ÓÁę ċċĎôİ èÒĎôí
business decision-makers before it starts. There are also regular
ċĎôÓĎÁĒĒĎÁċôĎęĒıÙę×·ĝĒÙîÁĒĒôıîÁĎĒęô¸×Á¸æôîĎÁĒĝèęĒɼ
!ÁĒċÙęÁ ę×Á ½Á¸ÁîęĎ èÙĺÁ½ ÁÒÒôĎęɷ QôîęÁÙĎô Ē ĳĒ ĎÁĒĝèęĒ ¸ î
ÓĎôıÒĎôíôîÁċĎôäÁ¸ęÙîęô í äôĎôċÁĎ ęÙôî è ĎÁ ɼ{× ęʖĒ×ôı
ę×Á¸ôíċ îĳʖĒ½ÙÓÙę è·ÁîÁľęĒ ĒĒÙĒę îęĒę ĎęÁ½ɼ=ęı ĒôĎÙÓÙ-
î èèĳ î î èĳęÙ¸ĒÁĲċÁĎÙíÁîęęôċÁĎĒôî èÙĺÁĎÁ¸ôííÁî½ ęÙôîĒ
ÒôĎ ¸ĝĒęôíÁĎĒɷ Ēĝ¸× Ē ęÙċĒ ôî ĎÁęÙĎÁíÁîę ¸ôîęĎÙ·ĝęÙôîĒɼ =ęʖĒ
·ÁÁîĒôĝĒÁÒĝèę× ęÙęʖĒ·Á¸ôíÁ í äôĎĝî½ÁĎę æÙîÓɼʓ“ÁʖİÁÓôę
½ôĺÁîĒôÒċÁôċèÁę× ęıôĎæôîÙęɷ î½ÙęʖĒ ½ÙÓÙę è¸× îîÁè î½
ĎÁċôĎęĒèÙæÁ îĳ½ÙĒęĎÙ·ĝęÙôî¸× îîÁèɷʔ×ÁĒ ĳĒɼ
Figure 12: Innovation Is Often a Grassroots Effort
Individualsclosetospecificbusinessneedsareoftenthedrivers
ofinnovationaroundemergingtechnologies.
Who is most likely to champion the use of emerging
technologies such as AI/machine learning, internet
of things (IoT), and blockchain?
24%
Topleadership
26%
9%
13%
23%
5%
Individual/teams
inoperatingunits
Marketing
RD
IT
Other

18
{×ÁĎÁÙĒ ¸× èèÁîÓÁ ĒĒô¸Ù ęÁ½ıÙę×ę×Áè ¸æôÒ ¸ÁîęĎ èÙĺÁ½
î èĳęÙ¸ĒÒĝî¸ęÙôîɷQôîęÁÙĎô ½½Ēɶı× ęęô½ôı×Áîę×ÁĎÁÙĒ
í äôĎÙîİÁĒęíÁîęĎÁčĝÙĎÁ½ę× ęÓôÁĒ·Áĳôî½ ęĳċÙ¸ èċĎôäÁ¸ęɼ
6ôĎÁĲ íċèÁɷuĝîLÙÒÁÙĒèôôæÙîÓęôĎÁ Ď¸×ÙęÁ¸ęĒôíÁôÒÙęĒ½ ę
ĒôÙę¸ îíôİÁęôę×Á¸èôĝ½ɷı×Ù¸×ĎÁčĝÙĎÁĒíôĎÁÙîİÁĒęíÁîęÙî
architecture and infrastructure. “The incrementalism is a risk
ę× ęıÁ× İÁęôí î ÓÁɷʔ×ÁĒ ĳĒɼ
Communication and Education Encourage an Analytics Mindset
½İ î¸ÙîÓ ¸ĝèęĝĎÁôÒÙîîôİ ęÙôîĎÁčĝÙĎÁĒîôęäĝĒę·ÁÙîÓ×Á Ď½
·ĝęèÙĒęÁîÙîÓɼHôĳôî ÓĝĎôɷı×ôı Ē¸×ÙÁÒ½ ę ôÒľ¸ÁĎÒôĎę×Á
¸Ùęĳ î½ ¸ôĝîęĳ ôÒ u î 6Ď î¸ÙĒ¸ô ę ę×Á ęÙíÁ Ē×Á ı Ē ÙîęÁĎ-
İÙÁıÁ½ÒôĎę×ÙĒĎÁċôĎęɷĒ ĳĒíĝ¸×ôÒę×ÁıôĎæôÒ½ ę Ē¸ÙÁî¸Á
ÙĒ ·ôĝę¸ĝèęĝĎÁ î½¸× îÓÁí î ÓÁíÁîęɷ×ÁèċÙîÓÙîęÁĎî è¸èÙ-
ÁîęĒĝî½ÁĎĒę î½ę×Áİ èĝÁôÒ½ ę î½×ôıÙę¸ îèÁ ½ęôîÁı
î½ Ùîîôİ ęÙİÁ ı ĳĒ ôÒ ę×ÙîæÙîÓɼ
key element of the effort is making
sure that data scientists listen.
ʓ{×Á ı ĳ ıÁʖİÁ ċċĎô ¸×Á½ ¸ĝè-
ęĝĎÁ¸× îÓÁɷ½ĎÙİÙîÓ½ ę ĝĒÁÙîę×Á
¸Ùęĳɷ× Ē·ÁÁîęôĎÁ èèĳĒċÁî½ęÙíÁ
to understand the challenges and
barriers to using data across the
¸Ùęĳɼ{×ÁîɷèÁęʖĒĒ× ċÁôĝĎĒÁĎİÙ¸ÁĒ
Ďôĝî½ ę× ę Ēô ċÁôċèÁ ÒÁÁè ×Á Ď½ɷʔ
ôî ÓĝĎô Ē ĳĒɼ ʓî½ ı×Áî ċÁôċèÁ
ÒÁÁè×Á Ď½ɷę×Áîę×ÁĳĒę ĎęęôęĎĝĒę
ĳôĝ î½ę×Áĳı îęęôıôĎæıÙę×ĳôĝ
ôîîÁıę×ÙîÓĒɷÙî¸èĝ½ÙîÓę×ÙîÓĒę× ę
ĎÁí ĳ·Á èÙęęèÁĎÙĒæÙÁĎôĎĒ¸ ĎÙÁĎɷ
èÙæÁ½ ę Ē¸ÙÁî¸ÁċĎôäÁ¸ęĒɼʔ
7ÁęęÙîÓ ęô ę× ę ċôÙîę ¸ íÁ ÒęÁĎ
îĝí·ÁĎ ôÒ ÁÒÒôĎęĒɷ Ùî¸èĝ½ÙîÓ ċĎô-
viding training via the organiza-
ęÙôîʖĒ! ę ¸ ½ÁíĳʈĒÁÁ6ÙÓĝĎÁɗəʉɷ
½ÁíôîĒęĎ ęÙôî ċĎôäÁ¸ęĒ èÙæÁ ½ Ē×-
·ô Ď½Ēɷ î½ ę×Á è ĝî¸× ôÒ î ôċÁî
½ ę ċôĎę èɷ ôî ÓĝĎô ½½Ēɼ WİÁĎ
ęÙíÁɷ ę×ĎôĝÓ× ę×ÁĒÁ Á½ĝ¸ ęÙôî è ÁÒÒôĎęĒ î½ ·ĳ × İÙîÓ ½ ę
ÁĲċÁĎęĒıôĎæÙîÓĒÙ½Á·ĳĒÙ½ÁıÙę×Ēę ÒÒɷę×Á ÓÁî¸ĳʖĒÙîęÁĎî è
¸èÙÁîęĒ× İÁÓ ÙîÁ½ę×ÁęôôèĒęôÙ½ÁîęÙÒĳę×Á ċċĎôċĎÙ ęÁ½ ę
Ē¸ÙÁî¸Á ċĎôäÁ¸ęĒ ÒôĎ ę×Áí î½ ę×Áî ċċèĳ ÒôĎ ½ ę Ē¸ÙÁî¸Á
ĎÁĒôĝĎ¸ÁĒɼ {×ÙĒ ċċĎô ¸× èĒô ×ÁèċĒ î èĳęÙ¸Ē ċĎôÒÁĒĒÙôî èĒɷ
ı×ô ĒÁĎİÁ Ēĝ¸× ½ÙİÁĎĒÙęĳ ôÒ ÙîęÁĎî è ¸ĝĒęôíÁĎĒ ę× ę ę×Áĳ
¸ îʖę èĒô·Á½ôí ÙîÁĲċÁĎęĒʊôî ÓĝĎôċôÙîęĒôĝęę× ęu î
6Ď î¸ÙĒ¸ôʖĒ½Áċ ĎęíÁîęĒĎĝîÒĎôíʓęôœɷ î ÙĎċôĎęęô ĺôôɼʔ
WîÁĒĝ¸¸ÁĒĒı Ē ! ę u6ċĎôäÁ¸ęıÙę×ę×Á¸ôĝîęĳ!Áċ ĎęíÁîę
ôÒ nĝ·èÙ¸ ;Á èę× ęô ÙîİÁĒęÙÓ ęÁ ı×ĳ ċ ĎęÙ¸Ùċ ęÙôî Ď ęÁĒ × ½
Ò èèÁî·ĳɗɜʾ íôîÓíôę×ÁĎĒ î½· ·ÙÁĒÙîę×ÁÒÁ½ÁĎ è“ôí-
Áîɷ =îÒ îęĒɷ î½ ×Ùè½ĎÁî ʈ“=ʉ îĝęĎÙęÙôî ċĎôÓĎ í ÒĎôí ɘɖɗɗ
ęôɘɖɗɝɼÒęÁĎ î èĳĺÙîÓĒÙĲĳÁ ĎĒôÒ½ ę ·ôĝęċ ĎęÙ¸Ùċ ęÙôîɷ
ċĎôÓĎ íĒę ÒÒÙîęÁĎ ¸ęÙôîĒıÙę×íôę×ÁĎĒɷċ ĳíÁîęĒÙĒĒĝÁ½ɷ î½
Strongly Agree
DataSF's Data Academy Assessment: LeadersSettheToneforAnalytics
Adoption
Agree Neither Agree nor Disagree Disagree Strongly Disagree
32% 48% 15%
0% 20% 40% 60% 80% 100%
18% 28% 23%
0% 20% 40% 60% 80% 100%
Daily Weekly Monthly Rarely Never
22% 9%
Figure 13: DataSF’s Data Academy Assessment
Do you feel that your skills improved after taking this Data Academy course?
How often do you use the information or skills you learned in your own work?
DataSF, the analytics group for the city and county of San Francisco, is expanding data literacy throughout
the agencies it serves via its Data Academy. It shares success metrics — such as attendees’ assessments
of skills gained and how frequently those are applied — via a public dashboard.

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