DEEP Scott Killoh-3

DEEP

C A R P A T H I A B O O K S

UNCORRECTED PROOF

Copyright ©2012 by Carpathia Books. All rights reserved.

No part of this book may be reproduced in any form without

written permission from the publisher.

Design by Carparthia Media.

Why do we assume simple is good? As you bring

order to complexity, you find a way to make the

product defer to you. Simplicity isn’t just visual style.

It’s not just minimalism or the absence of clutter.

To be truly simple, you have to go really deep.

— J O N Y I V E , A P P L E

0IntroductionConsider this data footnote from history, relating to the sinking of the

HMS Titanic.

Captain Edward J. Smith had already taken corrective action in response to

iceberg warnings, and four days out of Southampton had drawn up a new

course which took the ship slightly further southward. Little did he know that

the information he needed to safely arrive in New York harbor was already

aboard the Titanic, yet inaccessible.

That Sunday at 1:45 PM, a message from the steamer Amerika warned that

large icebergs lay in Titanic’s path, but because Marconi’s wireless radio

operators were paid to relay messages to and from the passengers, they

were not focused on relaying “non-essential” ice messages to the bridge.

DEEPS C O T T K I L L O H

Really? Can you imagine all of those meaningless messages that did get

through to the Titanic’s passengers on April 12th? It is astounding to consider

that mission-critical intelligence existed yet was given lesser value by the

operational policies of the White Star Line’s strategic communications partner,

Marconi. In a more open, multi-channel communications environment, perhaps

the information flow would have saved 1,517 people’s lives.

This is where data matters.

This book is about data. Well, a certain kind of data.

In his biography, Steve Jobs talks over and over about how important it is for

business leaders to block out noise. Data can be noise, overwhelming noise,

as we all experience in our daily lives.

But we are talking about deep data. Data that is gathered not from thin slices

of customer activity, but from an understanding of the whole of customer

behavior. Not what they chat about, not what they “like”, not what banner ad

they click on. Deep data measures, as Eloqua’s Steven Woods calls it, “digital

body language.” In other words, everything they do.


Google does it. Look at the sophistication of their contextual ads in your

search results. And we all know how powerful and successful Amazon’s

“if you liked this” feature is. An Amazon email isn’t spam, it’s likely a targeted

message that actually interests you.

When you can collect this kind of data, it gives you real time insight into your

customer’s responses and allows you to improve your product.

So now your data is deep. It’s not noise. And it’s not going to sink your ship.


1ShallowYou don’t want to be here. You do not want to be skimming the surface.

The entire world is going deeper, accommodating more and more information.

Look at the impact of Moore’s Law. Not only is computer circuit processing

power doubling every 18 months; the “soft” ability to track, analyze, segment,

re-connect and apply larger and larger sets of information is doubling right

alongside the hardware and the wiring.

Maybe there was once a valid business reason for not knowing a lot about

your customers, how they behaved, when they acted, and when they

hesitated, when and where they veered off course.

And maybe shallow worked when you could manufacture a car in just one

color, build houses based on variations of three simple floor plans, or take



fifteen years to develop and patent a blockbuster drug. But today, advances

in technology have unleashed capacity to offer infinite choices. Suddenly,

summaries are time sucks. To think in bullet points is pointless. And ignorance

is no longer bliss. It’s death.

But when you go deep, it gets quiet. Starved of daylight, opportunities loom.

Large, sustainably large margins abound. The deeper you go, the less likely

it is you will run into anyone. Very few bother, because working at depth is

intimidating and involves effort and risk.

Deep has its roots in the ancient words for “world.” To have depth is to encom-

pass the entirety, to own the whole thing at once and not worry that you will

lose it anytime soon.

AutomateIn a recent WSJ article “Software is Eating the World”, tech pioneer Marc

Andreesen points out that “more and more major businesses and industries

are being run on software and delivered as online services—from movies to

agriculture to national defense. Two decades into the rise of the modern

Internet, all of the technology required to transform industries through

software finally works and can be widely delivered at global scale.”

And he doesn’t just mean Netflix and Amazon. He points to Pixar, Google,

LinedkIn, Zynga, Spotify, Skype — even Fed Ex which he describes as “a soft-

ware network that happens to have trucks, planes and distribution hubs

attached.” Everyone from Exxon to WalMart is using software to power

logistics and distribution capabilities, crushing competition.

All good news, because software means automation. Which means efficiency,

which means savings. But what it really means is that you now can capture

data, so you have the ability to go deep.

Betting on DataGoogle’s Eric Schmidt maintains that we now create in two days as much

information as all humanity did from the beginning of recorded history

until 2003.

The more data, the more analytics matter. Just look at these investments in

business intelligence (BI) software companies, all high-profile “buy not build”

acquisitions: Business Objects by SAP for $6.8 billion, Hyperion by Oracle for

$3.3 billion, and Cognos by IBM.


The McKinsey Global Institute in a 2011 report Big Data: The next frontier

for innovation, competition, and productivity notes that “large data sets—

so-called big data—will become a key basis of competition, underpinning new

waves of productivity growth, innovation, and consumer surplus. Leaders in

every sector will have to grapple with the implications of big data, not just a

few data-oriented managers. The increasing volume and detail of information

captured by enterprises, the rise of multimedia, social media, and the Internet

of Things will fuel exponential growth in data for the foreseeable future.”

Despite these based-on-bits pronouncements, the challenge of learning and

profiting from enterprise information remains elusive.

John Jordan is a clinical professor at Penn State University, where he teaches

IT Strategy. Jordan writes an insightful column for Forbes on data topics and

his gap analysis actually bodes well for anyone building a business around

analytics:

“Despite all the money spent on ERP, on data warehousing and on “real-time”

systems, most managers still cannot fully trust their data. Multiple spread-

sheets document the same phenomena through different organizational

lenses, data quality in enterprise systems rarely inspires confidence.


“Related to this lack of confidence, risk awareness is on the rise. Whether

in product provenance (Mattel), recall management (Toyota, Safeway or CVS),

exposure to natural disasters (Allstate, Chubb), credit and default risk (any-

one), malpractice (any hospital), counterparty risk (Goldman Sachs), disaster

management or fraud (Enron, Satyam, Societe General), events of the past

decade have sensitized executives and managers to the need for rigorous,

data-driven monitoring of complex situations.”

The McKinsey study confirms and frames the case for the discipline of deep

data, transforming the business beyond the visibility, transparency, and

accuracy of information toward the creation of rich content and product

refinement: “Big data allows ever-narrower segmentation of customers and

therefore much more precisely tailored products or services. It can be used to

improve the development of the next generation of products and services….

manufacturers are using data obtained from sensors embedded in products

to create innovative after-sales service offerings such as proactive mainte-

nance, preventive measures that take place before a failure occurs or is

even noticed.”


The New Value Chain

Forget Porter’s old value chain. Business is no longer about managing

transactions.

If you have the right data, and the ability to crunch it at high-speed, then

you have analytics that give you real time insight. And using these insights

to make significant product improvement is the holy grail. The product gets

better based on what people want. And that improved product gets another

round of customer response and further refinement. It just gets better

and better.

By the way, now you’ve really learned about your brand value and promise.

Because it isn’t your senior team sitting in a boardroom deciding what the

brand was. It’s your customers.



New value chain

Old value chain

2Shallow vs. Deep

Still using a wooden suggestion box in the company break room? We

thought not. An early innovation tool, that is ultimately subjective, narrow,

disconnected from workflow, static and manual in how it collects and analyzes

data. Yet imagine what your business could become if you could pull ideas

from behavior in real-time, across every function within the entire organiza-

tion, as if you were stuffing that wooden box every second with thousands

of data points.

Here is a short, provocative list of similarly shallow innovation trends that fall

short of meaningful information. We propose these be replaced by online,

automated monitoring of customer browser and purchasing behaviors:


Shallow Flaws Deep Flaw

Social Media Subjective, Manual Represents vocal minority

Crowdsourcing Disconnected, Subjective Reduces quality and brand

Focus Groups Narrow, Manual Process Arbitrary groupings

Online Survey Narrow, Manual Process Tracks opinion not behavior

Idea Hubs Subjective, Static Management ranking subjective

Inventor Portals Subjective, Manual Disconnected from consumers

Chat Screen Manual , Intrusive Creates artificial behavior

Spreadsheet Disconnected, Manual Subjective

Deep data is discriminating, critical not only of analog data gathering tools

like the break room suggestion box but also online technologies which can be

equally flawed. Having a digital presence is no guarantee of business value.

Take blog content and that long (or short) tail beneath a blog known as the

“comment reel.” As Josh Constine recently observed on a TechCrunch blog,

“Commenting on blogs is broken.” In that same post, Constine cites those who

propose turning off comment reel, because they are “full of trolls, bile, and

spam links; there’s no way for popular sites to keep up with comments on old


posts; comment reels give random people too much visibility and distract from

primary content.”

Hiten Shah, in a Forbes innovation issue in August 2011, goes even further in

his assessment of popular social media: “This is a new medium and it’s always

hard to measure a new medium….Facebook is giving you data relevant to the

Facebook model. The page-view game is done with anyway. We want to track

people, not page views.”

Micah Sifry is co-founder of the Personal Democracy Forum, a website

that examines how technology is changing politics. As the 2012 Presidential

Primary season heats up, Sifry’s putting social media on the back burner, if not

off the stove altogether: “This isn’t to say that campaigns should ignore social

media, or that efforts by voters to influence the election by organizing online

are pointless. But just because you can count something and chart it doesn’t

mean you’ve proven anything.”

Sifry suggests that “a high numbers of retweets are just an indication of

notoriety or celebrity. Saying simple, stupid things that lots of people want

to tell their peers about can get you tons of followers and retweets. But it

doesn’t mean anything definitive about grass-roots support.”


Newt Gingrich has 1.4 million followers on Twitter, which might lead you to

believe he deserved Republican front-runner status at the end of 2011. Yet

Gingrich finished in 4th place in the January 2012 Iowa Caucuses. Where was

Twitter? Half of those Gingrich accounts aren’t in the United States, and half

of all Twitter accounts aren’t even active.

Given all the noise, distraction and flaws of social media, companies will be

better served to follow the New Value Chain: gather deep customer data,

invest in technology to provide high speed analytics, make real time decisions

for product improvement. This is objective versus subjective. It follows not

what customers say, but what they do.

The Business of Browsing

While noise has increased dramatically around social media, the actual

consumption of content and advertising has shifted to mobile. IDC shows U.S.

mobile advertising revenue growing from $877 million in 2010 to $2.1 billion in

2011, then doubling to $4.1 billion in 2012, as 65% of Americans have smart

phones and mobile devices “have gone mainstream.”

Informa Telecoms & Media projects a ten-fold increase in global mobile adver-

tising, from $2.3 billion in 2009 to $24.1 billion in 2015. The Asia Pacific region


will account for the largest share by 2015, at 30.9 percent, driven by “strong

growth” from China and India. North America will account for 18 percent of

the market in 2015, with Latin America at 6.4 percent and Western Europe

8.6 percent.

“The mobile advertising industry has now moved beyond the trial and

experimental phase and many advertisers and brands are now spending

significant sums on running mobile campaigns each month,” according to

Informa consultant Shailendra Pandey.

Those projections are predicated on mobile content that is both accessible

and highly-relevant.

Similar to traditional publishing, users expect, and are engaged by, high quality

content and spend more time inside applications. This translates into higher

advertising rates for premium space like this. Devices are mobile and more

frequently accessible, and consumers expect the same rich experience

whether online or offline.


The Biology of Browsing

How a reader browses (by device choice, by keyword search, and navigating

inside the content) correlates directly to the continuum of the reader’s inter-

ests, intent and investment, and ultimately to economic value for producers of

content. We’ll call this the “Curiosity to Cash” process, one which our brains are

wired to reinforce.

If the reader is rewarded, a pattern and perpetuation of behavior is estab-

lished. The reader is essentially saying to the content or ad provider, if you

interest and delight me, I’ll be spending more time here more often, which

means I will range wider and deeper within your domain, and once I trust your

content I might even purchase something at a later date. Venturing into the

unknown is slow and incremental, yet that is the surest and most stable way

to build loyalty and profitable customers. Familiarity breeds, well, repeatable

recurring revenue.

The field of neurobiology supports this on a simple level: neurons that “fire

together, wire together,” creating highly-efficient neural pathways. This is

powerful. Our brain activity (behind browsing, reading and purchasing) is bio-

logically predisposed to create efficient, high speed and repeatable behaviors;

creatures of habit, as we say. Our “out of the box” technology as vertebrates


is survival-mechanism enablement of sophisticated attention and retention.

The brain is in the business of transforming rarely-used and disparate foot-

paths into frequently-driven autobahns that self-repair and connect to one

another.

The companies and organizations that design content and develop access

with this innate hard-wiring in mind will be the most profitable and sustainable

in the coming age of mobile.


3Deep: How it Works in Media

Think of how deep data can transform a business ecosystem.

Take media. Please. Seriously, we all know traditional media companies are

in big trouble.

Yet, there is good news for media, even for the most traditional of all media

— newspapers. A recent McKinsey report showed that in the last four years,

news consumption has increased from 60 to 72 minutes a day. And the

growth is in readers under 35, the most coveted advertising demographic.

One catch: they are reading digitally. Smartphones. Tablets. And laptops (!)

So print just needs to move to digital, right?


Well, they tried that. Remember the frantic rush to the iPad? Now those same

publishers are stepping back and reconsidering. Results, even for initial suc-

cesses like Wired, have been generally disappointing. Why?

Going to the tablet means completely rethinking print content. Simply

dumping your magazine content on the tablet is no different than when you

dumped it on the web. It didn’t work then and it’s not working now.

The truth is that modern readers want highly targeted content, customized

to their devices. Their New York Times needs to look – and act – differently on

their phone than it does on their tablet or on the web site.

The publishing world is becoming more complex by the day as content plat-

forms proliferate into an ever wider array of mobile, tablet, and online devices.

At the same time, publishers must create, produce, and distribute content

across these channels using fewer and fewer resources.

And then the Cloud appeared. Now agile publishers can publish to any

channel—including the iPad, web, social media, and even print—from a single

consolidated platform that can be accessed anywhere, anytime, from any

connected device. These are feature-rich environments incorporate tools like

integrated search and text mining dashboards, an advanced creation workflow


engine, and the ability to mine and automate the production of new published

products based upon demographic or individual preference.

Take Trinity Mirror in the U.K., publisher of five national newspapers, 240

regional publications and 500 digital products. After seeing enormous cost

savings through implementing an end-to-end cloud platform, the publishing

group recently announced that it was (pause) hiring 20 digital editors. Richard

Wallace, editor of the Daily Mirror, said: “Our future is a multimedia one and we

need to transform ourselves into an agile media business, ready to grasp the

opportunities and challenges of the multimedia world we now inhabit.”

Across the industry we are seeing Digital First as the key to success.

The Atlantic magazine, for instance, seems an unlikely prospect for digital

prowess. Yet their digital ad revenue is up 209% in the last two years. But

most impressive is the shifting spend: digital has grown from 16% of total

ad revenue in 2008 to 45% this year. Why? Editor James Bennet says “our

front-line sales team has changed from 10% coming from outside a traditional

print background to 30% coming from outside a traditional print background.“

So finally, media can think Digital First. And Digital First means they can focus

on something much more profound. You guessed it: data.


The Financial Times is doing just that. In a recent restructuring, the paper

established a team of 11 non-newspaper people focused solely on analytics.

The team combines the disciplines of web and customer analytics across the

business:

• In editorial, they identify what is popular with what audience and why.

• In marketing, they determine how to sell online subscriptions to access content, attract new audiences, and effectively spend budgets.

• In IT, they identify site problems and analyze capacity planning.

• In advertising, they profile who their readers are, what interests them and how to give the most accurate portrait of the reader to advertisers

Maybe most importantly, the data is being used to shape the business models.

As Tom Betts, Head of Web Analytics, sees the team growing and focusing on

two areas:

“First is Predictive web analytics. Predictive analytics is already mature in

many fields, but not yet in web analytics. Using web data to predict what a

user might be interested in or what they might buy next is still quite pioneer-

ing in our industry. But not for much longer.


The second is multichannel analytics. We’re seeing a huge and rapid shift in

consumption from desktop to mobile. The development of apps, where the

user experience is native to the device, poses challenges but exciting opportu-

nities for analytics. All of a sudden, you are measuring more than the web.”


How Deep Data Helps Media

“Every Company is a Media Company”When “software eats the world,” publishers face a print/digital divide that

is not all that different from what any enterprise is encountering, or will soon

encounter. The truth is, every company creates mass volumes of content,

from marketing collateral to operational manuals to HR policies.

Tom Foremski’s site, Every Company is a Media Company, says “every company

publishes to its customers, its staff, its neighbors, its communities. It doesn’t

matter if a company makes diapers or steel girders, it must also be a media

company and know how to use all the media technologies at its disposal. In

addition to the traditional means of publishing, such as white papers, news

releases, etc, companies must now also master the ‘social media’ technolo-

gies that allow anyone, their customers, their competitors, to publish also.”

Jon Iwata, Senior VP Communications and Marketing at IBM, believes that all

companies will become publishers:

“We will go direct because we can. The tools of information development are

available to us as well. At IBM we are investing heavily in becoming a publisher,

but a very particular sense of publishing. Pumping out information only just

adds to the noise and compounds the challenge of being heard. Value will


come from providing perspective and useful information for making a contribu-

tion to our audience’s knowledge. “

He goes on to consider Apple: “They don’t just advertise, they teach. They

don’t just sell, they create learning experiences in their stores. They want you

to learn everything the product can do because then you, with great enthu-

siasm, will teach others. This is why visits to the Apple store Genius Bar are

free. They don’t pitch you, they teach you. And, in the process, they recruit

both new and loyal customers, advocates, and evangelists. Apple has become

publisher, teacher, community maker.“

He also points to a tire company, who 100 years ago, being limited in sales by

how much people drove, developed a series of guides for hotels, rest, destina-

tions. Ways for people to enhance their lives. And to drive more. Today, the

Michelin guides are a stronger brand than the tires.

So if this is true, then the lessons of how media companies communicate

and use data to refine its products may be instructive across all industries.

Let’s take a look.


Deep: How it Works in Other IndustriesAs you think through the mostly cerebral and strategic job of digital innova-

tion and business transformation, it’s easy to disconnect that from the indi-

viduals who work full-time, front-line jobs driving a skiploader, running a bank

teller window or making sure that industrial boilers are efficiently heating brick

and mortar facilities where we run our digital enterprises. Each job function is

marked by specific skills and skill levels, procedures and policies, exceptions

and exemptions, and of course, large amounts of data driving each role, and

data driven (potentially) from each action and transaction.

As you future-proof your company, your weatherproofed home with thermal

insulation is holding up and the pipes aren’t freezing because a confederation

of interested parties designed and manufactured, distributed and installed

those R-19 rolls (R-10 for the attic stairs) to a dynamic set of scientific,

economic and safety specifications. Data and content opportunities abound

along the information chain of those who conceive, manufacture, transport

and stock whatever it is you’re consuming, according to standards and a rules

engine governed by the larger marketplace and industry dynamics.

Zooming in, the amount of data driven by a single product line can be stagger-

ing. If it’s your product, you can, and should, delve into the SKU of information


and inter-connections, indefinitely. Consider the singularities of a Sharpie (39

Fine Point colors, yet only pink and yellow are sold in single packs), a Leather-

man Crunch (15 tools in your hand at once; is 16 too much to handle?), or

Baskin-Robbins (which gives a 31% discount on ice cream, only on the 31st of

the month, and only in Malaysia).

Now, zoom out. Billions of us punch some form of a clock for any one of the

millions of global operating businesses, each “going concern” featuring unique

requirements and data metrics. The World Federation of Exchanges tracks

roughly 47,000 public-stock companies across 54 stock exchanges. The US

has approximately 27 million businesses, before you try to account for the

under-the-table, underground economy.

Amazingly, there is consistency across language and geographical boundary

and company size because of standard job functions and a smaller number of

core industry categories. The North American Industrial Classification System

(NAICS) codes, which identify a firm’s primary business activity, covers 1,170

industries (including 358 new industries, 250 of which are services produc-

ing industries.) Even that number is intimidating, so most global organizations

pare it down to 25 core industry classifications.


Consumer products, media, transportation and financial services, those sec-

tors where we have daily interaction are easier to relate to in terms of content

and data requirements and where a deep approach can be meaningful, even

transformative:

How about a digital news feed delivered to my device at 11:55am, when I’m

most likely to want to read a sports section (devoted mostly to cricket and

tennis) as a mental diversion during lunch? If you do this, I will more than likely

pay more attention to the ads alongside the stories.

And wouldn’t we be more open to other offers from a manufacturer, if that off-

the-shelf $39.99 blender did away with the majority of those 12 pulse options,

including four different speeds just for smoothies?

What about a checking account that sent me an overdraft warning, at the

same time as the bank? I might actually pay attention to other content-rich

emails the bank wants to share with me.


Retail InnovationAs Harvard Business Review noted in a “Spotlight: Reinventing Retail” in

December, 2011, “leading-edge companies such as PetSmart and the UK

pharmacy chain Boots have begun applying science to the task: They are

testing digital and physical innovations with clinical-trial-style methodology,

using sophisticated software to create control groups and eliminate random

variation and other noise. All of this is costly, but it’s hard to see how retailers

can avoid doing more of it.”

From a deep data perspective, this will only take retailers (or any company

reinventing itself) so far, if not backwards in the innovation cycle. Even though

control group selection takes advantage of software automation processes,

the management of these groups and the documentation of data will be

manual, time-delayed, errant and ultimately subjective.

The very concept of a creating a control group, is shallow and limited, danger-

ously deceptive to the brand, and more akin to analog-level marketing tools

like focus groups, online surveys and Twitter/Facebook monitoring. There

might even emerge a retail “placebo effect,” where these segmented consum-

ers behave differently as a function of their control group participation, to

please or placate their scientific handlers.


Why not analyze rich content and education developed for the entire spec-

trum of pet and pharmacy consumers, to drive interaction, data tracking

and business insight? Why not use sophisticated software to un-group the

process, open participation as widely as possible and create additional depth

of information collected, and conduct the science in real-time?

Thankfully, that same issue of Harvard Business Review highlighted emerg-

ing “Next Best Offer” (NBO) strategies: “Using increasingly granular data, from

detailed demographics and psychographics to consumer’s clickstreams on the

web, businesses are starting to create highly customized offers that steer

consumers to the ‘right’ merchandise or services – at the right moment, at the

right price, and in the right channel.”

Emphasis on “starting to” - NBO’s are still in early stages, but essentially

on the right track. They’re built on the classic know-your-customer, know-

your-offering and know-the-purchasing-context intelligence that rests squarely

within deep data frameworks.

The idea of anticipating behavior and tailoring are relevant offering based on

the data is solid.


Failure points will arise and adoption will drop whenever these NBO processes

are disconnected from the entire range of content platforms that encompass

mobile, tablet, and online devices. Have you comprehensively collected all the

demographic data for each customer from all possible touch points?

Conversely, is the offer distributed to every possible channel and device facing

the customer? Is the offer content-rich and worth the attention and retention

of each consumer?

Deep Even Works in MiningMoving away from recognized industry sectors mining familiar, daily-life

data to make life better, what about something more obscure, like mining, the

open pit and underground mine business?

What is main factor transforming the mining industry? China, and that coun-

try’s demand for metals.

As PricewaterhouseCoopers notes, “These are interesting times for the min-

ing industry, with ever increasing scrutiny from governments, customers and

other stakeholders. Growing demand for its products, driven by emerging mar-

kets, highlights that supply will be the most significant challenge it will face.”


There are many wild cards that this capital- and equipment-intensive must

address at both strategic and tactical levels: emerging market miners are

outperforming traditional players; wild fluctuations in commodity prices that

boost or drag down production gains; development projects have become

more complex and are typically in more remote, unfamiliar territory.

As IT advanced from mainframes to the web to cloud computing, the mining

industry stands ready to absorb state-of-art technology into operations: esti-

mating ore reserves, bore hole monitoring, pit optimization, mine and haul road

design, as well as grade control with blending in order to achieve consistency in

the feed to the process plant. All these issues need to be communicated and

there is huge potential for analytical tools within the vertical.

More sophisticated data is needed to support geological models, to accurately

represent not only the grade, tonnage and grade distribution of the mineral

deposit, but also its boundary and the internal structure based on which the

engineers can plan for future methods of mining.

Now blend in elements such as environmental compliance, worker security

and safety (both in and out of the pit), as well as synchronized global position

monitoring and maintenance for fleets, shovels, dozers, and drills. More data,

and better communication needed.


How will you use data derived from metrics like “shovel hang time” to maxi-

mize the value in that seam, while protecting your business from commodity

fluctuations and currency exposure? How will you keep your fleets running,

your employees safe, healthy and informed? How can you reduce blinds spots

in the pit and keep drill bits properly positioned? Make revenue explosive, yet

reduce the number of errant blasts?



How Deep Data Helps Life Sciences


How Deep Data Helps Automotive

5 Dashboard of DashboardsAnalytics is a function, a discipline and in the world of deep data it can be a

business all on its own, and subject to the same cycle of deep data, retrieval,

insight and refinement. While most examples in this book are external in

nature, to collect, monitor and package consumer behavior within content,

you can easily “import” that methodology to apply to hundreds or thousands

of employees within the enterprise who interact with internal content.

Let’s say you’re the CEO of a large, publicly-traded industrial pump business.

You’ve made several acquisitions of industrial pump businesses and technolo-

gies, and you’ve vertically integrated and invested in manufacturing and distri-

bution, not just branding, marketing and sales. To refine your product means

creating the best possible industrial pump at profitable margin, correct?


In the world of Deep, one might argue that as CEO, your company is the prod-

uct. And when the enterprise needs refinement, you rely on an executive suite

run by a CFO, CIO, CMO and COO. If you’re large enough, perhaps you have in

place C-level executives for strategy, technology and customer service. All

of them marching to the drumbeat of the quarterly forecast and some form

of business plan, and as part of your transformative powers as Chief, you’ve

requested that each one manage from a simple yet sophisticated dashboard

tracking key metrics.

Employees, divisions, units, product segments, are all aligned in data initiatives

and a certified environment of continuous improvement. Your organization is

recognized widely for its data prowess and with a well-funded war chest for

executive compensation and a talent for communicating and motivating, you’re

able to retain your top executives. What more can you do with your data?

Let’s start by enlarging our concept of a dashboard. Rather than the culmina-

tion and simplification of data, dashboards are sources content also. Each

senior executive will interact with his or her dashboard in unique ways, with

unique frequency, and quite possibly, they will gravitate to certain sections of

the dashboard and downplay (or ignore) others.


Perhaps it’s time for a dashboard of dashboards, to understand what everyone

considers important, not by asking them during a meeting, or during a walk

during an off-site retreat, or other “offline” methods of communication.

What if your CEO dashboard not only told you about trends relating to your

company and industry, but also informed you that all your senior executives

are consistently working in the revenue and margin sections of their dash-

boards, even though your board of directors has repeatedly directed you to

focus the company on growing market share. You’re compensating your

executives with stock and bonuses for growing market share, but they remain

focused on “harvest” behaviors. You can see it!

As CEO, you can effectively and quickly “pump up the volume” in your pump

business by de-emphasizing revenue and margins, converting those compila-

tion sections of the C-suite dashboards to singular stats (to provide a comfort

level). Then, re-design dynamically each dashboard to highlight, emphasize,

and give greater screen space and depth to the priority market share metrics,

in the context of that executive’s role.

Your executives might not notice the new format, but your board of directors

will. The shift in behavior and recalibrated focus should make itself evident in

that institutional dashboard known as the quarterly shareholder meeting.


Elastic Company CreationTechnology has radically changed what is possible when creating a new

global business. Going from idea to company can happen in a fraction of the

time possible just 2 years ago. The reason is the availability of two main

technological breakthroughs: 1) Consolidation of thousands of web services

functions into pre-configured, next generation ERP software platforms and

2) Mission-critical cloud computing environments that support these plat-

forms. Together, these allow companies to go from an idea on a napkin to a

mature global business footprint in days.

Companies like Amazon and Google built ecosystems by plying together

thousands of functions in a decade long journey to create their own

proprietary “Web Services ERP” platforms. They literally spent 10 years and

billions of dollars creating these environments.

All of this will change dramatically over the next few years. Breakthrough

technology will change how companies perceive both technology and

business. Instead of trying to build businesses at the level of being technology

integrators, there will be pre-configured “Idea Factories” combining massive

scale web services ERP platforms with mission critical cloud computing

environments.


These Idea Factories will scale new businesses at near zero cost by

leveraging internet scale cost models. Every innovation will be shared globally

in an instantly accessible environment. Through mass scale single code bases

and shared innovation, these Idea Factories will replace bespoke platforms.

The result will be the evolution of how we think about business and technol-

ogy. Technology will no longer be the limiting factor. Instead of handling the

creation and management of internal technology “factories”, this will be pur-

chased as on demand, at a fraction of the cost or time of doing it traditionally.

Even more dramatic, a new startup will be able to leverage the near zero

variable cost per transaction of mature businesses instantly. Without having

to spend any capital up front on virtually any operating functions, new compa-

nies will launch at a tiny fraction of what it normally takes. Ideas on napkins to

global technology businesses will happen in days.

The Idea Factory operating model will dramatically change how new compa-

nies are funded. Investments at angel level can have a dramatic result. With

less than $1 million, global companies can be funded and proven before signifi-

cant capital is invested. The ability to try thousands of ideas and prove them

in the market for almost no capital will be the norm by the next decade. The

long process of building out management teams and operations models is

over. The race to launch new ideas will instead be the ultimate investor goal.


6One more thingIn this era of massive transactional and interactional social data, you’ll

begin to see more and more mislabeled, misplaced or misappropriated

data. (Remember the Titanic?) Increasingly, data detection will become a

valuable skill.

This ability to reduce noise and distortion is a rare talent. To screen and filter

data is a critical business discipline, whether that means ignoring fields on a

single report or divesting divisions of a company.

In the end, deep means leveraging your business intellect not merely for

operational efficiency, but for meaningful product refinement, developing rich

content that commands a premium, enriches the customer experience and

creates sustainability for your enterprise.


About Scott KillohScott Killoh’s entrepreneurial savvy and keen understanding of product

development in the software market led him to founding Mediaspectrum,

providing world-class advertising solutions to companies worldwide.

Previous to Mediaspectrum, Mr. Killoh was the founder of Openpages where he

served as the vice president of engineering and chairman. During his tenure,

he raised $54 million in seed and expansion funding from top tier venture firms

including Goldman Sachs and led Openpages to a market capitalization that

eclipsed $190 million. Openpages has since been acquired by IBM.

Mr. Killoh founded Openpages in 1995 when he co-developed and launched

the Openpages content management system. Mr. Killoh transformed Open-

pages into an end-to-end enterprise solution that served Fortune 500 custom-

ers including Gannett Co., Thomson Financial Media, Knight-Ridder, and the

Tribune Co. During his more than 15 years in the technology industry, his primary

focus has been on product development, support and engineering.

Mr. Killoh holds a B.A. in Finance from the University of Massachusetts at

Amherst.


About MediaspectrumRecently Mediaspectrum has teamed with SAP to provide end-to-end

cloud technologies to enable the New Value Chain of deep data, high speed

analytics, real-time insights, and product improvement.

The Mediaspectrum platform provides the inital deep data. As an end-to-end

cloud publishing platform, it streamlines business process inefficiencies

(media clients typically save 50-75% of production costs). And since every-

thing is on a single platform, it also gives the business owner unprecedented

visibility into how their customers are behaving online. This deep data

(collected both on and offline), can be displayed in dashboards in as aggregate

or granular a fashion as is required.

Crunching this kind of deep data used to be painful. But the SAP CO-PA

Accelerator dramatically improves speed and efficiency of working with large

data volumes and allows you to perform real-time profitability reporting,

conduct instant analysis of profitability data at any level of granularity,

basically achieve real-time insights to help make better decisions on the fly.

Decisions that will lead to product improvement, not based on boardroom

opinions or even what customers say — but what they do.


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