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Randy Bean is an industry thought leader and author, and CEO of NewVantage Partners, a strategic advisory and management consulting firm that he founded in 2001.

David Kiron is the executive editor of MIT Sloan Management Review.

Boston | New York | San Francisco | Austin | Charlotte

MIT Sloan Management Review Webinar

How Big Data and AI are Driving Business Innovation in 2018

June 27, 2018

www.newvantage.com

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How Big Data and AI are Driving Business Innovation

Randy Bean Introduction | Background

Founder | CEO | NewVantage Partners LLC

Monthly Big Data columnist: Wall Street Journal (2014-2015)

Monthly columnist: Forbes (2016-Present)

Frequent contributor: MIT Sloan Management Review | Harvard Business Review

Executive Thought-Leadership Roundtables | Boston | New York | San Francisco

Focus -> Big Data/AI Innovation | C-Executive Survey

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NewVantage Partners first conducted a survey on the state of Big Data within

Fortune 1000 companies in 2012.

The survey has been unique in providing a top-down perspectives on c-

executives priorities.

2018 Theme: “How Big Data and AI are Driving

Business Innovation”

Nearly 60 Fortune 1000 size firms participated, representing a bellwether

selection of blue-chip firms.

Participants included American Express, Capital One, Ford Motors, Goldman

Sachs, Met Life, Morgan Stanley, and Verizon, among others.

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Survey participants comprised executives representing data-intensive industries.

An overwhelming majority of executive participants (93.1%) identified themselves as C-level executive decision-makers.

Industry Participation

Financial Services | Insurance 77.2%

Pharmaceuticals | Medical 8.8%

Other Industries 14.0%

Respondent Role

Chief Data Officer 55.6%

Chief Analytics Officer | Chief Data Scientist 15.3%

Chief Information Officer 13.9%

CEO | President 5.6%

Chief Marketing Officer 2.8%

Other 6.9%

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Financial Services | Insurance

Aetna Fannie Mae Putnam Investments

AIG Farmers Insurance Regions Bank

Ally Financial Fidelity Investments Standard & Poors

American Express First Republic Bank State Street

Bank of America Freddie Mac Sun Life Financial

Bank of China Goldman Sachs Swiss Re Group

Bank of Montreal The Hartford TD Bank

Bloomberg JP Morgan TIAA

Capital One Lincoln Financial Travelers

Charles Schwab M&T Bank UBS

Cigna Met Life USAA

CitiGroup MFS Investments VISA

Citizens Bank Moody's Wells Fargo

Crawford Morgan Stanley Zions Bank

Credit Suisse Nasdaq

2018 Participating Firms

Pharma | Medical Additional Industries Astellas Estee Lauder Linked In

GlaxoSmithKline Ford Motors Motorola

H3 Biomedicine Global Energy Verizon

OptumLabs IBM

Partners Health Care Li & Fung

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Why Big Data and AI are Closely Linked 1. AI needs Big Data to build its intelligence – particularly machine learning

2. Big Data provides the data needed to train AI learning algorithms

3. AI doesn’t deduce conclusions – it learns through trial and error, which requires

massive amounts to data to teach the AI

4. It’s safe to say there is no AI without Big Data.

Source: Big Data vs. Artificial Intelligence. Datamation. May 30, 2018.

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Key Themes and Findings 1. Fear of disruption is fueling investment in Big Data and AI

2. Big Data is directly empowering AI investment and initiatives

3. Firms are reporting measurable results from Big Data and AI investments

4. Chief Data Officers are emerging champions of Big Data and AI

5. Business adoption of Big Data and AI initiatives remains a challenge.

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Theme 1: Fear of disruption is fueling investment in Big Data and AI

A majority (79.4%) of executives say that they fear disruption and potential displacement from data-driven competitors with highly agile data cultures -- up sharply from 2017.

Fear of disruption/displacement 2018 2017

Yes 79.4% 46.6%

No 20.6% 53.4%

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Companies are increasing their investments in Big Data and AI/Machine Learning initiatives in response to threats of disruption and displacement.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

ArtificialIntelligence/Machine

Learning

Cloud Computing Blockchain

2018

2017

Nearly 3/4 of executives (71.8%) believe investments in AI/Machine Learning will have a transformational and disruptive impact on their business – up from 44.3% in 2017.

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Firms (93%) have significantly increased their investment in AI/Machine Learning initiatives.

Executives see biggest threats from data-driven competitors, with highly agile data cultures.

This includes the Tech Giants – Amazon, Google, Apple, and Facebook.

Executives also fear competitors within their own industry.

A majority (54.4%) of executives report that an inability to be nimble and compete on data is

the biggest competitive threat that they face today.

Disruptive technology with greatest impact 2018 2017 Investing

Artificial Intelligence/Machine Learning 71.8% 44.3% 93.0%

Cloud Computing 12.7% 8.2% 83.1%

Blockchain 7.0% 4.9% 54.9%

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Theme 2: Big Data is directly empowering AI investment and initiatives

Investing in Big Data or AI initiatives

Yes 97.2%

No 2.8%

An overwhelming 97% of executives report that their companies are actively investing in building or launching Big Data and AI initiatives.

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There is a growing consensus that AI and Big Data initiatives go hand-in-hand.

Executives (76.5%) indicate that the proliferation and greater availability of data is directly empowering AI and cognitive initiatives.

Proliferation of data empowering AI/cognitive learning

Yes 76.5%

No 23.5%

AI initiatives directly benefit from access to richer, more granular, more complete, more extensive data -- in vastly greater volumes, varieties, and data sources.

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Investment in Big Data and AI initiatives is growing, but most Big Data and AI investment remains small and experimental, focusing on innovation around the edge.

Financial services firms have traditionally been among the largest investors in data and analytics, and are now at the forefront of investment in Big Data and AI initiatives.

Firms need no longer rely on subsets of data, and are combining Big Data, AI, machine learning

algorithms, and computing power to competitive advantage.

Total investment in Big Data and AI

Greater than $500M 12.7%

$50M -- $500M 27.0%

Under $50M 60.3%

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Theme 3: Firms are reporting measurable results from Big Data and AI investments

Yes 73%

No 27%

Measurable Results from Big Data and AI Investments

Measurable results from Big Data and AI investments

Yes 73.2%

No 26.8%

Nearly 3/4 of the executives surveyed (73.2%) report their organizations are achieving

measurable results from their Big Data and AI investments -- up from 48.4% in 2017.

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Executives (84.1%) are actively investing in Big Data and AI initiatives to enable better decisions using advanced analytics.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Top Objective of Big Data and AI Investments

#1 Goal

Started

Success

Rate

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Success rates are highest in enabling better decisions (69%), decreasing expenses (60.9%), and accelerating speed to market (54.1%).

Innovation initiatives are underway at nearly half (46.4%) of firms.

Monetization initiatives have been launched at a growing set (31.9%) of firms.

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Theme 4: Chief Data Officers are emerging champions of Big Data and AI

A majority of firms (63.4%) report having named a Chief Data Officer – up from 12% in 2012.

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Organizations are struggling to define the Chief Data Officer role.

34.0% 32.1%

15.1%

11.3%

7.5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Externalchange agent

| outsider

Companyveteran|insider

Line executivewho has beenaccountablefor results

Data scientist| analytics

background

Techexecutive |technologybackground

Background for a Successful Chief Data Officer

Background for a successful Chief Data Officer

External change agent | outsider 34.0%

Company veteran| insider 32.1%

Data scientist | analytics background 15.1%

Line executive who has been accountable for results 11.3%

Tech executive | technology background 7.5%

The Chief Data Officer may be the toughest position in industry today.

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Primary responsibility for data strategy and results

Chief Data Officer 39.4%

No Single Point of Accountability 23.9%

Chief Information Officer 15.5%

Other C-Executive 12.7%

Chief Analytics Officer | Chief Data Scientist 8.5%

Few Chief Data Officers (13%) have revenue responsibility.

Responsibility for Big Data and AI initiatives remains scattered.

A significant number of executives (23.9%) point to no single point of accountability.

Yes 13%

No 87%

Chief Data Officer with Revenue Responsibility

Managing Big Data and AI initiatives remains a challenge for most firms.

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Long term role of the Chief Data Officer

Sits on the executive committee 50.0%

Reports to the executive committee 37.1%

CDO role is interim role to be phased out | unnecessary 12.9%

The future role of the Chief Data Officer remains uncertain.

Half of firms (50%) do not see the Chief Data Officer as a top role within their firm.

A minority of executives (12.9%) believe the Chief Data Officer role is unnecessary.

Firms are now combining the Chief Data and Chief Analytics roles into an even newer “Chief Data and Analytics Officer” role.

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Theme 5: Business adoption of Big Data and AI initiatives remains a challenge.

Business adoption of data initiatives remain a challenge

Yes 64.7%

No 35.3%

Cultural issues represent the greatest obstacle to Big Data and AI adoption.

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Few executives (19.1%) believe that technology obstacles are the biggest problem.

Creating a data-driven culture 2018 Success 2017 Success

Yes | In Process 98.6% 32.4% 85.5% 37.1%

No | Not a Priority 1.4% 14.5%

Firms are striving to create data-driven cultures, but with limited success.

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10 Take Aways

1. Be Patient – think of Big Data and AI adoption as a journey, not a sprint 2. Develop a long-term plan – it’s difficult to get to a destination without a roadmap 3. Prioritize initiatives based on measurable business value – know what’s important 4. Undertake a succession of steps to establish credibility and build momentum 5. Focus on “quick wins” that will demonstrate immediate business value 6. Innovate “around the edges” – create avant-garde teams that become tomorrows

mainstream organization 7. Establish Centers of Excellence, Big Data/AI Labs, and Analytic Sandboxes 8. Migrate initiatives from test environments to production in a systematic fashion 9. Celebrate successes – results speak louder than words 10. Don’t underestimate the cultural obstacles – focus on people and process.

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Those companies that prove themselves to be adept at developing and executing

initiatives using Big Data and AI capabilities will likely be the companies best

positioned to deflect threats of agile, data-driven competitors in the decade ahead.

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“How Big Data and AI are Driving Business Innovation” – MIT Sloan Review “How Big Data Is Empowering AI and Machine Learning at Scale” – MIT Sloan Review “Companies Brace for Decade of Disruption From AI” – MIT Sloan Review “Variety, Not Volume, Is Driving Big Data Initiatives ” – MIT Sloan Review “Organizational Alignment is Key to Big Data Success” – MIT Sloan Review

“The Chief Data Officer Dilemma” -- Forbes “Setting The Table For Data Science And AI At Bank Of Montreal” – Forbes “How Verizon is Building a Big Data and AI Culture” -- Forbes “Revolutionizing Radiology with Deep Learning at Partners Healthcare -- Forbes “How AI and Machine Learning Are Helping Drive the GE Digital Transformation -- Forbes “Why Cultural Change is Necessary for Big Data Adoption” -- Forbes

“Big Companies Are Embracing Analytics, But Most Still Don’t Have a Data-Driven Culture” – Harvard Business Review “How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs” – Harvard Business Review

“Big Data Innovation: Fail Faster, Execute Smarter” – The Wall Street Journal “Making The Case for the Long-Tail of Big Data” – The Wall Street Journal

Additional Reading

http://sloanreview.mit.edu/article/how-big-data-is-empowering-ai-and-machine-learning-at-scale/









http://sloanreview.mit.edu/article/companies-brace-for-decade-of-disruption-from-ai/?utm_source=twitter&utm_medium=social&utm_campaign=sm-direct





http://sloanreview.mit.edu/article/variety-not-volume-is-driving-big-data-initiatives/?utm_source=twitter&utm_medium=social&utm_campaign=sm-direct






http://newvantage.com/wp-content/uploads/2013/04/MIT-Sloan-Review-Organizational-Alignment-is-Key-Jan-2013.pdf





https://www.forbes.com/sites/tomdavenport/2017/12/08/setting-the-table-for-data-science-and-ai-at-bank-of-montreal/#78ce5b607814



























































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