Data, Analytics, & AI: How Trust Delivers Value...2 Building Trust in Data: How Analytics Leaders...

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 ContentsExecutive Summary .......................................................................................................................................................................................1

Section I: Building Trust in Data: How Analytics Leaders Get ‘the Right Stu�’ ...................................................................................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

collected for analytics must be trusted. Much like the need for

sterility in clinical laboratories or a clear chain of evidentiary

-

viduals throughout the organization must understand the care

given to data management so that they trust those insights —

-

1. Better data governance is needed.

-

data for analytics.

2. Data privacy emerges as an opportunity.

3. Fostering an analytics culture improves innovation.

-

in lines of business.

Organizational choices — such as centralizing the analytics

For leaders of organizations still striving to achieve analytics

-

trust delivers value.

Executive Summary


2

Building Trust in Data: How Analytics Leaders Get ‘the Right Stuff’

W-

the technology.

The leading academic medical center recognized that to create

management and analytics.

Trust Advances Analytics Maturity

building trust — trust in the data that’s collected and stored

and trust in the analytic insights it generates. And it has seen

embraces data-driven decision-making.

data they are accessing from a centralized data lab instead of

-

learning and artificial intelligence in decision-making or

MIT Sloan Management Review

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

78% 76%

44% 43%

2017 2018

Percentage of respondents reporting somewhat or significantly improved access to useful data over the past year

Percentage of respondents reporting frequently or always having the right data to informbusiness decisions

Percentage of respondents reporting somewhat or significantly improved access to useful data over the past year

Percentage of respondents reporting frequently or always having the right data to informbusiness decisions


3

Why Closing the Trust Gap Matters

-

-

-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 e�ort

No data quality e�orts

Just one in five organizations takes a formal approach to data quality, while 30% report at least proactive e�orts. The plurality of respondents still tackle the issue informally.

Figure 3: Data Quality E�orts 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 Up to date 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 e�ort

No data quality e�orts


Grade Your Data

-

-

-

-

to achieve.”

-

-

-

14%

42%

Regularly

Data from 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

Data from 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

there must be less tolerance for error.

The Human Factor: Partner With Domain Experts

their organizations.

for the city and county of San Francisco (she has left the orga-

-

-

AI Built on a Bedrock of Data Governance

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


-

-

-

-

actually have to have a data strategy to enable analytics and

decision-making.”

Commit to Treating Data as an Asset

-

organizational resources may be faster to gain advantage from

decisions to treat data as an asset underlies the success of such

-

-

This governance structure creates internal understanding

-

“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

-

-

-

-

-

challenging to advance their analytics maturity.

-

business decisions.

Data quality needs funding to back up the commitment: Only a relatively small per-centage of companies gave these e�orts a markedly higher priority in budgets over the past year.

Figure 5: Putting Their Money Where Their Data Is

40%

15%

38%

5%

2%

Significantlyincreased

Somewhatincreased

Nochange

Somewhatdecreased

Significantlydecreased

7


8

Cleveland Clinic’s Centralized Data Store Helps Build Trust in Analytics

I N D U S T R Y S N A P S H O T

Donovan says.

HE

ALT

H C

AR

E

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

MA

NU

FAC

TU

RIN

G

Morgan Vawter, chief analytics director,

Caterpillar


Caterpillar Tailors Analytics Strategies to Business Unit Needs

analytics to enable business success.”

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

Practitioners in our recent Data & Analytics survey have

-

-

-

-

-

The Pivotal Roles of Data Security and Privacy

-

-

— think of consumer data breaches or controversies about

19%

Currently do this

Have responseplan in place

in case of data breach

Implementing Considering

44%

12%14%

Track where all data is stored

Keep updated list of sensitive

data types collected

Train all employees in IT

security risks and practices

Planning No activity

11%

47%

23%

11%10%10%

43%

20%

13% 13%11%

Currently do this

Apply advanced analytics to

predict cyber-intrusion risks


22%

16%15%

Use cybersecu-rity frameworks (e.g., PCI, NIST)

Employ a chief information

security o�cer


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

Have responseplan in place

in case of data breach


44%

12%14%

Track where all data is stored

Keep updated list of sensitive

data types collected

Train all employees in IT

security risks and practices


11%

47%

23%

11%10%10%

43%

20%

13% 13%11%

Currently do this

Apply advanced analytics to

predict cyber-intrusion risks


22%

16%15%

Use cybersecu-rity frameworks (e.g., PCI, NIST)

Employ a chief information

security o�cer


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



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information and ensuring that that lineage and that right and

The Opportunity to Build Customer Trust Based on Data

-

Etlinger observes that both business leaders and consumers

-

-

Trust Is Fragile — Handle With Care

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 e�orts lag security e�orts, 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%




16%

27%

14%

25%

18%




GDPR is not a requirement but may guide our privacy policy


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







GDPR is not a require-ment but may guide our privacy policy



12

-

is one of those things that is very hard to build and very easy to

-

models to “nudge” clients to take actions that are in their best

-

to share more of their data.

-

SmarterHQ.

-

models and adding learning elements.

shouldn’t be using it at all.”

“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


-

GO

VE

RN

ME

NT

Joy Bonaguro, former chief data o§cer,

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.”

“


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Cross-Functional Teamwork Improves Predictive Models at Barclays US


-

-

FIN

AN

CIA

LS

ER

VIC

ES

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

Game-changing insights can result from investments in

-

-

-

-

on these fronts are also more likely to exhibit the most trust

right data to inform their business decisions.

1.

that their organizations have centralized data analytics functions.

2.

-


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 often champion the value of data and actively seek to apply it when making decisions.



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

to use the data the business is collecting instead of siloing

scientist and executive advisor at consultancy Booz Allen Ham-

-

-

sales of different items in their assigned section of the store.

too. They have the data.” Then the counselors and sales staff

Fostering Collaboration Drives Culture Change

true collaboration ensues that the culture really begins to

-

“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%

Many organizations have an opportunity to do more to tackle the analytics skills shortage. The good news is that a majority are taking action to build a data-driven workforce, with programs either running or in the planning or implementation stages.


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

Establishing and maintaining a culture that embraces analytics

make a difference.

-

-

-

-

-

the backbone and that are tied to a business outcome.”

Analytics Expertise: Centralize vs. Decentralize

-

business decision-makers before it starts. There are also regular

-

Figure 12: Innovation Is Often a Grassroots E�ort

Individuals close to specific business needs are often the drivers of innovation around emerging technologies.

Who is most likely to champion the use of emerging technologies such as AI/machine learning, internet of things (IoT), and blockchain?

24%Top leadership

26%

9%

13%

23%

5%

Individual/teamsin operating units

Marketing

R&D

IT

Other


18

architecture and infrastructure. “The incrementalism is a risk

Communication and Education Encourage an Analytics Mindset

-

-

key element of the effort is making

sure that data scientists listen.

-

to understand the challenges and

barriers to using data across the

-

viding training via the organiza-

-

-

Strongly Agree

DataSF's Data Academy Assessment: Leaders Set the Tone for Analytics 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.


19

-

-

going to take time and you’re really going to have to engage.”

-

-

-

-

-

-

-

-

Predictive Analyt-

ics: The Power to Predict Who Will Click, Buy, Lie, or Die

-

-

-

for a data-driven culture.


20

About the Research

MIT SMR MIT Sloan Management Review

-

organizations are taking to advance analytical maturity and build data-driven cultures.

Dean Abbott, co-founder and chief

Joy Bonaguro, city and county of San Francisco

Kirk Borne,

Timothy Crone, MD,

Chris Donovan, executive director of

Susan Etlinger,

Caroline Viola Fry,

Michael S. Goldberg,

Christina Hoy,

Yash Kandyala, head of global business analytics

David Loshin,

Eric Monteiro,

Vishal Morde,

Jeanne Ross,

Beatriz Sanz Saiz, global data and analytics

Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

Morgan Vawter,

Acknowledgments


21

The annual MIT Sloan Management Review

-

data. Successful organizations also must trust in their ability

-

you’ll do right by their data.

other data.

Why Advancing Technology Demands Building Trust

Randy Guard, executive vice president and chief marketing o§cer, SAS

S P O N S O R ’ S V I E W P O I N T

About SASThrough innovative analytics, business intelligence, and data management software and services, SAS helps customers make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®.

To learn more about how technology and trust go hand in hand, visit us at www.SAS.com/innovation.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. © 2019, SAS Institute Inc. All rights reserved. 110173 _G93935.0119

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