Exploiting Disruptive Technologies 101 Peter Coffee VP and Head of Platform Research salesforce.com inc.

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Safe Harbor

In Other Words: Everything That

You See Here is Real

Specifically, “Disruptive” Is*

• Technologically straightforward

• Simpler than prior approaches

• Less than expected by established market

• Attractive in new ways to emerging market

• Initially focused on low-profit customers

• Innovative at faster pace than incumbents

*Bower, Joseph L. & Christensen, Clayton M. "Disruptive Technologies: Catching the Wave" Harvard Business Review, Jan–Feb 1995

In Memory of Disruptees Past

• Imaging: Film CCD CMOS

• Printing: Offset Laser Inkjet

• Storage: 8”5¼”3½”USB keyCloud

• Connection: Circuits Packets

• Knowledge: Britannica Wikipedia

Will Today’s Disruptors Sign In, Please?

• Mobility

• Social Computing

• Big Data

Mobility: More Than a Handle

There are three parts of user

experience to increase convenience:

immediacy, simplicity and context.

The three parts make up a customer’s

mobile context, or the overall feedback

of what a customer has told you and is

experiencing during engagement.

The Future Of Mobile Is User Context Context Transforms Product Opportunities For Consumer Product Strategists

The Opposite is “Antisocial”

• ‘Social’ is not non-business

• ‘Social’ is not non-serious

• ‘Social’ is a set of behaviors

– Sensitive to the

user’s context

– Adaptive to interests

– Driven by events to

offer proactive aid

Big Data Is Patterns, Not Records

“By combing through 7.2 million of our electronic medical records, we have created a disease network to help illustrate relationships between various conditions and how common those connections are. Take a look by condition or condition category and gender to uncover interesting associations.”

visualization.geblogs.com/visualization/network/

What If We Put Them Together?

What do you get from

• Patterns in big data

derived from

• Social networks

(people/devices)

via

• Ubiquitous mobile

devices/connections?

“Inform” is a Verb

• Wiener: information = bits

Shannon: information = entropy

Information behavior change

• “Even the word ‘library’ is getting

hazy…as the number of media grew,

and the methods for searching became

more sophisticated, there was no

substantive difference between the

Library of Congress and the Central

Intelligence Agency. So they merged.”

– Neal Stephenson, Snow Crash (1992)

Social Systems ‘Close the Loop’

• Customers: records communities

• Employees: appraisals collaborations

• Partners: supply chain value network

• Financials: transactions scenarios

• USAF OODA:

“Observe, Orient,

Decide, Act”

Social Systems Demand ‘Big Data’ Power

• Islands of data are cheap, but low-value

• Integrate data with apps: cloud-platform (PaaS) strength

– Data.com: 2 million participants, 1 million updates/month

– Radian6: massive data flows distilled into understanding

– Heroku + Treasure Data Hadoop: 0 to Warehouse in 3 minutes

• Data-driven expertise for developers and managers

Social Systems Demand ‘Big Data’ Power

What’s Taking So Long?

• “The typical large organization, twenty years hence, will be

composed largely of specialists who direct and discipline

their own performance through organized feedback from

colleagues and customers.”

• “It will be a knowledge-based organization.”

Peter F. Drucker, in The New Realities

…in 1989

Barriers to Becoming Knowledge-Based

• Complex legacy IT portfolios have made mere integration of data an

overwhelming task

• Cumbersome and brittle integrations have relegated end users to

roles as mere consumers

• Path of least resistance

has led to over-emphasis

on complex measures…

…based on historical data

• Mere thin-client redesign

attacked the form, not

the substance, of these problems

Let’s Disrupt Our Notion of Normal

• On spec, on time, on budget deployment of a fully tested,

proven cloud capability: trusted security and global availability

• Modern applications, driven by user feedback for continuing

improvement – with “clicks, not code” customization

• “No Software”: what’s paid for is function, not code. Continuous

scrutiny of operations, maintenance of facilities, and world-class

security are literally “part of the service”

• Multiple upgrades per year: no disruption, shrinking deployment

times, backward compatibility to previous API releases

• “The future is already here – just not evenly distributed”

- William Gibson

Let’s Talk About ‘Why’ – not ‘How’

Fast: no delays of capital budgeting; upgrades part of the service

Focused: no ‘keep the lights on’ software maintenance tasks

Field-ready: deliver coherent data & logic in any user context

Federated: apps market with click-to-try, click-to-integrate

For any organization, anywhere, of any size

Can Disruption Be Forecast?

Can Disruption Be Forecast?

According to the researchers, Moore’s Law and other models such as Kryder’s Law and Gompertz’ Law predict a smooth increasing exponential curve for the improvement in performance of various technologies. In contrast, the authors found that the performance of most technologies proceeds in steps (or jumps) of big improvements interspersed with waits (or periods of no growth in performance)… While no one law applies to every market, Tellis and his co-authors looked at 26 technologies in six markets from lighting to automobile batteries, and found that the SAW model worked in all six, in contrast to several other competing models.

Excuse me, sir, about that rathole… An example of how the SAW model could have saved a company from decline is Sony's investment in TVs. Sony kept investing in cathode ray tube technology (CRT) even after liquid crystal display technology (LCD) first crossed CRT in performance in 1996... Sony introduced the FD Trinitron/ WEGA series, a flat version of the CRT. CRT out-performed LCD for a few years, but ultimately lost decisively to LCD in 2001. In contrast, by backing LCD, Samsung grew to be the world's largest manufacturer of the better performing LCD… "Prediction of the next step size and wait time using SAW could have helped Sony's managers make a timely investment in LCD technology," according to the study.

Excuse me, sir, about that rathole… An example of how the SAW model could have saved a company from decline is Sony's investment in TVs. Sony kept investing in cathode ray tube technology (CRT) even after liquid crystal display technology (LCD) first crossed CRT in performance in 1996... Sony introduced the FD Trinitron/ WEGA series, a flat version of the CRT. CRT out-performed LCD for a few years, but ultimately lost decisively to LCD in 2001. In contrast, by backing LCD, Samsung grew to be the world's largest manufacturer of the better performing LCD… "Prediction of the next step size and wait time using SAW could have helped Sony's managers make a timely investment in LCD technology," according to the study.

Abstract: The estimates of the model provide four important results. First, Moore's Law and Kryder's law do not generalize across markets; none holds for all technologies even in a single market. Second, SAW produces superior predictions over traditional methods, such as the Bass model or Gompertz law, and can form predictions for a completely new technology, by incorporating information from other categories on time varying covariates. Third, analysis of the model parameters suggests that: i) recent technologies improve at a faster rate than old technologies; ii) as the number of competitors increases, performance improves in smaller steps and longer waits; iii) later entrants and technologies that have a number of prior steps tend to have smaller steps and shorter waits; but iv) technologies with long average wait time continue to have large steps. Fourth, technologies cluster in their performance by market.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2115237

“It is now possible for more people than ever to collaborate and compete

in real time...

“It is now possible for

more people than ever

to collaborate and

compete in real time...

The World is How Flat?

…with more other people…

…on more different kinds of work…

…from more different corners of the planet…

…and on a more equal footing…

…than at any previous time in the history of the world.”

Build on Best Practices

Call on Trusted Advisors

• In 1908, 4,700 hours of factory labor would

pay for a Model T Ford;

• Today, the same amount of labor will earn the

price of a Porsche Cayman.

That doesn’t make the Porsche

the commuter vehicle of choice…

…and even a Prius is not tomorrow’s

solution.

Do Not Think Incrementally

• In 1908, 4,700 hours of factory labor would

pay for a Model T Ford;

• Today, the same amount of labor will earn the

price of a Porsche Cayman.

That doesn’t make the Porsche

the commuter vehicle of choice…

…and even a Prius is not tomorrow’s

solution.

But in 1908, New York City’s

first subway was only four years old.

• Are you thinking in terms of the

network that will create value?

Do Not Think Incrementally

Connect With Customers

in a Whole New Way

Connected

Partners

Connected

Customers

Connected

Employees

Connected

Products

Thank You petercoffee

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pcoffee@salesforce.com