Big Data_ The Next Frontier Presentation

Big Data: The next frontier for innovation, competition, and productivity McKinsey Global Institute

Strata Summit September 20, 2011 CONFIDENTIAL AND PROPRIETARY Any use of this material without specific permission of McKinsey & Company is strictly prohibited

Key findings

The amount of data collected and stored has grown exponentially in the last decade Technology trends have converged – data is personal, everywhere, and increasingly accessible New possibilities exist to innovate and create significant value out of big data Big data will have bottom-line implications for companies and far-reaching effects on the economy There are challenges to address if the value of big data are to be realized McKinsey & Company | 1

Data storage has grown significantly – shifting markedly from analog to digital after 2000 Global installed, optimally compressed, storage

300 250 200

Data storage, 150 exabytes 100

Digital

50 0 1986

Analog 1993

SOURCE: Hilbert and López, “The world’s technological capacity to store, communicate, and compute information,” Science, 2011

2000

2007 McKinsey & Company | 2

Companies in all sectors have at least 100 terabytes of stored data in the United States; many have more than 1 petabyte Average stored data per firm with more than 1,000 employees, 2009, terabytes Securities and Investment Svs. Banking Communications and Media Utilities Government Discrete Manufacturing Insurance Process Manufacturing Resource Industries Transportation Retail Wholesale Healthcare providers1 Education Professional Services Construction Consumer and Recreation Svs.

3,866 1,931 1,792 1,507 1,312 967 870 831 825 801 697 536 370 319 278 231 >500 = WalMart data warehouse in 2004 235 = Library of Congress collection in 2011 150

SOURCE: IDC; US Bureau of Labor Statistics; McKinsey Global Institute analysis

McKinsey & Company | 3

This data has gone from being highly macro…

Americans burn 1,800 calories per day


…to very personal He burns She 2,133 burns 1,567 Americans of calories of calories per day per day

burn 1,800

She Sheburns He burns1,438 1,489 1,945 calories per day ofof ofcalories calories caloriesper per perday day day


Even physical objects are generating “exhaust” data


The number of connected nodes is increasing exponentially Estimated number of connected nodes, millions

72–215 5–14 Health care Industrials

10–30 1–3

2–6 8–23 4–12

Security Industrials Retail Travel and logistics Utilities Automotive

17–50 2–5 2–6 5–14

Energy Retail Travel and logistics

28–83

Utilities

15–45

Automotive

1–2 2–6

6–18 2010

SOURCE: Analyst interviews; McKinsey Global Institute analysis

Security


Computation capacity has risen sharply Global installed computation to handle information

Overall 1012 million instructions per second

This computational power is equivalent to almost 1.3 billion laptop computers

6.0 5.0 4.0 3.0 2.0 1.0 0 1986

1993

2000

SOURCE: Hilbert and López, “The world’s technological capacity to store, communicate, and compute information,” Science, 2011


5 ways for big data to create transformational value

Create transparency Expose variability and enable experimentation Segment populations to customize actions Replace/support human decision-making with automated algorithms Innovate new business models, products, and services McKinsey & Company | 9

Big Data companies have outperformed their respective markets and have created competitive advantage

Big data leader Other competitors

Percent, 10-year CAGR (1999 – 2009)

Revenue Grocers Online retailers Big box retailers Casinos

EBITDA

12

6

22

-15

9

5 5

9 9 8

SOURCE: Bloomberg and Datastream; annual reports; McKinsey analysis

10

2 11

Credit cards Insurance

3 24

-1

11

12

1 14

9

-1 5


Big data can generate significant value across sectors US health care ▪ $300 billion value per year ▪ ~0.7 percent annual productivity growth

US retail ▪ 60+% increase in net margin possible ▪ 0.5–1.0 percent annual productivity growth

Europe public sector administration ▪ €250 billion value per year ▪ ~0.5 percent annual productivity growth

Manufacturing ▪ Up to 50 percent decrease in product development, assembly costs ▪ Up to 7% reduction in working capital

Global personal location data ▪ $100 billion+ revenue for service providers ▪ Up to $700 billion value to end users SOURCE: McKinsey Global Institute analysis


Consumer surplus: Personal location data will generate 6 times more value for end customers than for service providers

Revenue accrued to service providers

Quantifiable value accrued to end customers

▪ Fuel savings ▪ Convenience ▪ Reduced (e.g., location congestion SOURCE: McKinsey Global Institute analysis

sharing) McKinsey & Company | 12

Relative value potential and ease of capture will vary across sectors

Bubble sizes denote relative sizes of GDP

High Health Care Providers

Utilities Manufacturing

Natural resources

Information Finance and Insurance Computer and electronic products

Professional Services Admin, Support and Waste Management Real Estate Construction and Rental

Big data ease of Capture index

Transportation and Warehousing Management of companies Wholesale trade

Accommodation and Food Retail trade Other Services Arts and Entertainment

Low

Educational services

Low

Government High

Big data value potential index SOURCE: McKinsey Global Institute analysis


To fully capture this opportunity several major issues must be addressed

Data policies ▪ Privacy concerns ▪ Data security issues ▪ Intellectual ownership and liability issues

Access to data ▪ Access to “foreign” data ▪ Integrating with own proprietary data

Technology & techniques ▪ Deployment of technologies ▪ Legacy system or inconsistent data formats ▪ Ongoing innovation

Organizational change & talent ▪ Shortage of talent ▪ Leadership that understands big data ▪ Aligned workflows and incentives


3 types of talent are needed to capture value from big data Talent needed

SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis

Potential gap by 2018

Deep analytical ▪ Actuaries ▪ Mathematicians ▪ Statisticians

~150K

Big data savvy ▪ Business managers ▪ Financial analysts ▪ Engineers

~1.5M

Supporting technology ▪ Computer programmers ▪ Computer software engineers ▪ Computer system analysts

~300K


Importance of access to data: US health care example Data pools

▪ ▪

▪ ▪

Owner: Pharmaceutical companies, academia Example datasets: clinical trials, high throughput screening (HTS) libraries

Owners: payors, providers Example datasets: utilization of care, cost estimates

SOURCE: McKinsey Global Institute analysis

Pharmaceutical R&D data

Clinical data

Integration of data pools required for major opportunities Activity (claims) and cost data

Patient behavior and sentiment data

▪ ▪

Owners: providers Example datasets: electronic medical records, medical images

▪

Owners: various including consumer and stakeholders outside health care (e.g., retail, apparel) Example data sets: patient behaviors and preferences, retail purchase history, exercise data captured in running shoes

▪


Implications for organization leaders

1 Inventory data assets, proprietary, public and purchased 2 Identify potential value creation opportunities and threats 3 Build internal capabilities to create a data-driven organization 4 Develop enterprise information strategy to implement technology 5 Address data policy issues McKinsey & Company | 17

Implications for policy makers

1 2 3 4 5 6

Build human capital for big data Align incentives to promote data sharing for the greater good Develop policies that balance the interests of companies wanting to create value from data and citizens wanting to protect their privacy and security Establish effective intellectual property frameworks to ensure innovation Address technology barriers and accelerate R&D in targeted areas Ensure investments in underlying information and communication technology infrastructure McKinsey & Company | 18

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