Predictions for CMOs and Digital Marketing in 2015

Posted on December 5, 2014 by GilPress

In 2015, digital marketing budgets will increase by 8%, according to a recent Gartner’s CMO Spend Report, a survey of 315 marketing decision makers representing organizations with more than $500 million in annual revenue.

Customer experience is the top innovation project for 2015, continuing its role as the top priority for marketing investment in 2014. The survey also found that

  • In 79% of companies, marketing has a budget for capital expenditures — primarily, for infrastructure and software
  • Marketers are managing a P&L and generating revenue from digital advertising, digital commerce and sale of data
  • 68% of organizations have a separate digital marketing budget — it averages a quarter of the total marketing budget
  • Two-thirds of companies are funding digital marketing via reinvestment of existing marketing budgets

Earlier this year, IBM found in its worldwide survey of CMOs that CEOs increasingly call on them for strategic input. Furthermore, the CMO now comes second only to the CFO in terms of the influence he or she exerts on the CEO. The survey also found, however, that very few CMOs have made much progress in building a robust digital marketing capability: Only 20%, for example, have set up social networks for the purpose of engaging with customers, and the percentage of CMOs who have integrated their company’s interactions with customers across different channels, installed analytical programs to mine customer data and created digitally enabled supply chains to respond rapidly to changes in customer demand is even smaller. Almost all CMOs, 82% of survey respondents, felt underprepared to deal with the explosion of data.

With this as a background, here’s a summary of what digital marketing and the CMO will look like in 2015, based on observations by Scott Brinker, a leading commentator on marketing technology, Forrester, TopRank online marketing blog, Wheelhouse Advisors, and Brian Solis.

CMOs will take charge of focusing their companies on the customer

CMOs and their marketing teams will become the primary driver behind customer-centric company growth. Leveraging their knowledge of the customer and the competitive landscape, CMOs will advise and council CEOs on how to win, serve, and retain customers to grow the business. They will also lead organizational changes and new collaboration initiatives aimed at unifying all customer engagement activities across the enterprise.

CMOs will poach IT staff to help them manage a rapidly expanding digital marketing landscape

The number of digital marketing tools will grow in 2015 with new startups and large, established tech companies confusing even more that CMO with their numerous offerings. To help manage this embarrassment of riches and move their companies further on their digital marketing journey, CMOs will be poaching IT staff looking for new challenges and better salaries.

CMOs should expect heavy rains from proliferating digital marketing clouds

Digital marketing tools will be increasingly offered as a cloud-based solution (“marketing-as-a-service”) rather than licensed software. Cloud-based solutions will continue to expand their ecosystems, with many small software developers adding apps to existing cloud-based digital marketing platforms.

CMOs will invest in new digital marketing hot areas

Content marketing and predictive analytics will continue to be hot areas of interest and investment for CMOs, but they will be joined in 2015 by sales enablement, post-sale customer marketing, marketing finance, marketing talent management, and new tools based on the Internet of Things, allowing for the integration of offline and online experiences.

CMOs will become brand publishers

CMOs in 2015 will act as heads of a publishing house, overseeing the entire spectrum of brand engagement, increasing the quality of their output, and improving the perceived value of digital interactions with customers and prospects.

[First published on Forbes.com]

Posted in Predictions | Leave a comment

2015 Predictions for the Big Data Analytics Market

Posted on January 2, 2015 by GilPress

The big data and analytics market will reach $125 billion worldwide in 2015, according to IDC. Both IDC and The International Institute of Analytics (IIA) discussed their big data and analytics predictions for 2015 in separate webcasts last month. Here are the highlights:

Security will become the killer app for big data analytics

Big data analytics tools will be the first line of defense, combining machine learning, text mining and ontology modeling to provide holistic and integrated security threat prediction, detection, and deterrence and prevention programs. (IIA)

IoT analytics will be hot, with a five-year CAGR of 30%

The Internet of Things (IoT) will be the next critical focus for data/analytics services. (IDC) While the IoT trend has focused on the data generation and production (sensors) side of the equation, the “Analytics” of Things is a particular form of big data analytics that often involves anomaly detection and “bringing the data to the analytics.” (IIA)

Adoption of technology to continuously analyze streams of events will accelerate in 2015—it’s all about speed and small units of data. IoT back end as a service (BaaS) will emerge, as players—including Amazon, IBM, and Microsoft—continue to stitch together a wider variety of platform as a service (PaaS) services, including stream processing, data triggers, indexing and synchronization, and notifications, into more tightly integrated offerings directly marketed to the growing community of IoT developers. (IDC)

Buying and selling data will become the new business bread and butter

70% of large organizations already purchase external data and 100% will do so by 2019. In parallel, more organizations will begin to monetize their data by selling them or providing value added content. (IDC) Companies will double their investment in generating new and unique data. “You can’t go into a data-based business without some unique data that gives you competitive differentiation.” 2015 will mark an inflection point of intentional investment by mainstream firms in generating and monetizing new and unique data sources. (IIA)

Companies will invest in self-service, automation, and augmentation to answer the skills shortage  

Shortage of skilled staff will persist. In the U.S. alone there will be 181,000 deep analytics roles in 2018 and 5x that many positions requiring related skills in data management and interpretation. (IDC—note that data was not provided for the supply side of the equation). Visual data discovery, an important enabler of end user self-service, will grow 2.5x faster than the rest of the market, becoming by 2018 a requirement for all enterprises. (IDC)

Automated decision-making will come of age in 2015 and the organizational implications will be profound. The very way that firms operate and organize themselves will be questioned this year as common workflows become rationalized through analytics. Key to success is the transparency of the automated systems and preparing managers “to occasionally look under the cover” of established models and algorithms.  (IIA)

Google’s announced Tuesday an automated statistician research project which aims to build an “artificial intelligence for data science.” But augmentation, rather than automation, may be the better option with knowledge workers. In 2015, companies will begin considering how to augment knowledge work jobs rather than automating them—moving from artificial intelligence to intelligent augmentation. Analytics, machine learning, and cognitive computing will increasingly take over the jobs of knowledge workers, and we will become more conscious of this in 2015. (IIA)

By 2018, half of all consumers will interact with services based on cognitive computing on a regular basis. Current personal services such as Apple Siri, Microsoft Cortana, and Google Now will raise expectations for employees to seek access to similar services in the enterprise. In 2015, PaaS competitors will step up their efforts to compete in the cognitive space. (IDC)

Image, video, and audio analytics will become pervasive

Rich media analytics will at least triple in 2015 and emerge as the key driver for big data technology investment. Already half of large organizations in North America are reporting use of rich media (video, audio, image) data as part of their big data analytics projects, and all large organizations will analyze rich media in five years. (IDC)

Storytelling will be the hot new job in analytics

The most important attribute sought in candidates for big data analytics jobs is communications skills. As organizations run into obstacles in understanding and adopting analytics, they rightly place more emphasis on communication, which is not a strength of most analysts. Companies will increasingly recognize the value of putting an experienced storyteller into the mix (IIA)… possibly looking to fill these positions from the large pool of unemployed journalists?

Posted in Big Data Analytics, Predictions | Leave a comment

How to Become a Unicorn Data Scientist and Make More than $240,000

Posted on October 17, 2015 by GilPress

What makes a good data scientist? And if you are a good data scientist, how much should you expect to get paid?

Owen Zhang, ranked #1 on Kaggle, the online stadium for data science competitions, lists his skills on his Kaggle profile as “excessive effort,” “luck,” and “other people’s code.” An engineer by training, Zhang says in this ODSC interview that data science is finding “practical solutions to not very well-defined problems,” similar to engineering. He believes that good data scientists, “otherwise known as unicorn data scientists,” have three types of expertise. Since data science deals with practical problems, the first one is being familiar with a specific domain and knowing how to solve a problem in that domain. The second is the ability to distinguish signal from noise, or understanding statistics. The third skill is software engineering.

Not having formal education in statistics or software engineering, Zhang explains that he acquired his data science skills by competing in Kaggle and learning from its community. No doubt being very good at learning on your own is a required skill, to say nothing about hanging out with the right people, preferably unicorn data scientists. Galit Shmueli, Professor of Business Analytics at NTHU, told rjmetrics that her one piece of advice for data scientists just getting started is to “attend a conference or two, see what people are working on, what are the challenges, and what’s the atmosphere.”

Recent data shows that unicorn data scientists can make more than $240,000 annually. This according to the 2015 Data Science Salary Survey where O’Reilly Media’s John King and Roger Magoulas report the results of a survey of 600 “data practitioners” (reflecting the recency of the term, only one-quarter of the respondents have job titles that explicitly identify them as “data scientists”).

The median annual base salary of the survey sample is $91,000, and among U.S. respondents is $104,000, similar to last year’s results. 23% said that it would be “very easy” for them to find another position.

Keep in mind that “23% of the sample hold a doctorate degree,” and additional 44% hold a master’s. The word “sample” here means, as it does in almost all other surveys today, “the people that wanted to answer our survey.” But unlike other survey report authors, King and Magoulas make sure to issue this warning: “We should be careful when making conclusions about survey data from a self-selecting sample—it is a major assumption to claim it is an unbiased representation of all data scientists and engineers… the O’Reilly audience tends to use more newer, open source tools, and underrepresents non-tech industries such as insurance and energy.”

Still, we can learn quite a lot about the background and skills required for admission into this well-paid group of data masters. Two-thirds of respondents had academic backgrounds in computer science, mathematics, statistics, or physics.

Beyond the initial training, it is important to keep abreast of the ever-changing landscape of data science tools: “It seems likely that in the long run knowing the highest paying tools will increase your chances of joining the ranks of the highest paid,” say King and Magoulas. And the most recent additions to the data science tool pantheon provide the greatest boost to salaries: “…learning Spark could apparently have more of an impact on salary than getting a PhD. Scala is another bonus: those who use both are expected to earn over $15,000 more than an otherwise equivalent data professional.”

The bad news is that the more time spent in meetings (even for non-managers), the more money a data scientist makes. Another widely discussed unpleasant part of the job—data cleaning—is the #2 task on which data scientists spend the most time, with 39% of survey participants spending at least one hour per day on this task. The good news is that exploratory data analysis is what occupies them most, with 46% spending one to three hours per day on this task and 12% spending four hours or more.

More data on the skills employed by practicing data scientists comes from an AnalyticsWeek survey of 410 data professionals. In Optimizing Your Data Science Team, Bob E. Hayes reports that respondents were asked to indicate their level of proficiency for 25 different skills.” Solving problems with data,” says Hayes, “requires expertise across different skill areas: 1) Business, 2) Technology, 3) Programming, 4) Math & Modeling and 5) Statistics. Proficiency in each skill area is related to job role.”

All of these skills may not present themselves in a single data scientist but it’s possible to assemble all of them by putting together a top-notch data science team. In “Tips for building a data science capability” from consulting firm Booz Allen Hamilton, we learn that “rather than illuminate a single data science rock star, it is important to highlight a diversity of talent at all levels to help others self-identify with the capability. It is also a more realistic version of the truth. Very rarely will you find ‘magical unicorns’ that embody the full breadth of math and computer science skills along with the requisite domain knowledge. More often, you will build diverse teams that when combined provide you with the ‘triple-threat’ (computer science, math/statistics, and domain expertise) model needed for the toughest data science problems.”

The concept of a data science team, combining various skills and educational backgrounds, is high on the agenda of the 175-year-old American Statistical Association (ASA) which is probably looking in dismay at the oodles of funds going to establishing new data science programs and research centers at American universities, to say nothing about the salaries of data scientists as opposed to the salaries of statisticians.

The ASA issued a “policy statement” on October 1, reminding the world that statistics is one of the three disciplines “foundational to data science” (the other two being database management and distributed and parallel systems, providing a “computational infrastructure”). The statement concludes with “The next generation [of statisticians] must include more researchers with skills that cross the traditional boundaries of statistics, databases and distributed systems; there will be an ever-increasing demand for such ‘multi-lingual’ experts.”

In other words, if you aspire to a $200,000+ salary, better call yourself a data scientist and start coding.

Posted in Data Science Careers | Tagged , , | Leave a comment

3 Recent Books on Data Mining, Data Science and Big Data Analytics

Posted on May 14, 2015 by GilPress

Now that most of the hype around big data has died down, overtaken by the buzz over the Internet of Things, we are sometimes treated to serious discussions of the state-of-the-art (or science, for that matter) in data analysis. If you are planning a career as a data scientist or you are a business executive trying to understand what the data scientists are telling you, three recent books provide excellent and accessible overviews:

The Analytics Revolution: How to Improve Your Business By Making Analytics Operational In The Big Data Era by Bill Franks

Data Mining For Dummies by Meta S. Brown

Data Science For Dummies by Lillian Pierson

Bill Franks is the Chief Analytics Officer for Teradata, and his specialty is translating complex analytics into terms that business users can understand. The Analystics Revolution follows Franks’ Taming the Big Data Tidal Wave, which was listed on Tom Peters’ 2014 list of “Must Read” books.

“With all the hype around big data, it is easy to assume that nothing of interest was happening in the past if you don’t know better from experience” says Franks. The over-excitement about big data caused many organizations to re-create solutions that already exist and build new groups dedicated to big data analysis, separate from their traditional analytics functions. As a correction, Franks advocates “a new, integrated, and evolved analytics paradigm,” combining traditional analytics on traditional data with big data analytics on big data.

The focus of this new approach–and the book–is Operational Analytics. It takes us from the descriptive and predictive analytics of traditional and big data analytics to prescriptive analytics. It pays close attention to the numerous decisions and actions, mostly tactical, taking place every day in your business. Most important, it places great emphasis on the process of analytics, on embedding it everywhere, and on automating the required response to events and changing conditions.

“Of course,” says Franks, “it takes human intervention to decide that an operational analytics process is needed and to build the process.”  But once the process is designed and turned on, the process accesses data, performs analysis, makes decisions, and then actually causes actions to occur. And humans are crucial to the success of this new brand of automated analytics, not only at the design phase, but also in the on-going monitoring and tweaking of the process.

An example of operational analytics is the development of an improved maintenance schedule using sensor data. There will be no value in the Internet of Things without an automated process for data analysis and action based on that analysis. “As traditional manufacturers suddenly find themselves embedding sensors, collecting data, and producing analytics for their customers, industry lines blur. Not only are new competencies needed, but the reason customers choose a product may have less to do with traditional selection criteria than with the data and analytics offered with the product,” says Franks.

The practical advice Franks provides in the book ranges from how to set up an analytics organization to developing and maintaining a corporate culture dedicated to discovery (finding new insights in the data and quickly acting on them) to implementing operational analytics. The Analytics Revolution is an excellent guide to the new business world of blurred industry lines and innovative data products.

If you are ready to move on from understanding the why of analytics today and how to think about it in a broad business and organizational context to a more specific understanding of the how of analyzing data, Data Mining for Dummies by Meta Brown should be your first step. The book was written for “average business people,” showing them that you don’t need to be a data scientist and “you don’t need to be an expert in statistics, a scientist, or a computer programmer to be a data miner.”

Brown is a consultant, speaker and writer with hands-on experience in business analytics. She’s the creator of the Storytelling for Data Analysts and Storytelling for Tech workshops. In Data Mining for Dummies, Brown tells the story of what data miners do.

It starts with a description of a day in the life of a data miner and goes on to discuss in clear, easy-to-understand prose all the key data mining concepts, how to plan and organize for data mining, getting data from internal, public and commercial sources, how to prepare data for exploration and predictive modeling, building predictive models, and selecting software and dealing with vendors. Data Mining for Dummies is an excellent step-by-step guide to understanding data mining and how to become a data miner.

If you are ready to move on from understanding data mining and being a data miner to more advanced tools and applications for data analysis, Data Science for Dummies by Lillian Pierson should be your first step. The book was written for readers with some technical and math skills and experience, but it aims to provide a general introduction to one and all: “Although data science may be a new topic for many, it’s a skill that any individual who wants to stay relevant in her career field and industry needs to know.”

Pierson is a data scientist and environmental engineer and the founder of Data-Mania, a start-up that focuses mainly on web analytics, data-driven growth services, data journalism, and data science training services. “Data scientists,” she explains, “use coding, quantitative methods (mathematical, statistical, and machine learning), and highly specialized [domain] expertise in their study area to derive solutions to complex business and scientific problems.

Data Science for Dummies is an excellent practical introduction to the fundamentals of data science.  It provides a guided tour of the data science landscape today, from data engineering and processing tools such as Hadoop and MapReduce to supervised and unsupervised machine learning, statistics and mathematical modeling, using open-source applications such as Python and the R statistical programming language, finding resources for publicly-available data, and data visualization techniques for showcasing the results of your analysis. Stressing the importance of domain expertise for data scientists, Pierson provides detailed examples of applying data science in specific domains such as journalism, environmental intelligence, and e-commerce.

“A lot of times,” says Pierson, “data scientists get caught up analyzing the bark of the trees that they simply forget to look for their way out of the forest.” The three books reviewed here provide a handy map to the maze of data analysis and a safe conduct pass for business executives, IT staff, and students, ensuring that they successfully get in and out of the data forest. Remember, as ones and zeros eat the world, data is the new product and operational analytics, data mining, and data science is the new process of innovation.

Posted in Big Data Analytics, Data Science | Leave a comment

Will Google Own AI? (4)

Posted on April 10, 2017 by GilPress

Norm Jouppi, Google:

We’ve been using compute-intensive machine learning in our products for the past 15 years. We use it so much that we even designed an entirely new class of custom machine learning accelerator, the Tensor Processing Unit. Just how fast is the TPU, actually? Today, in conjunction with a TPU talk for a National Academy of Engineering meeting at the Computer History Museum in Silicon Valley, we’re releasing a study that shares new details on these custom chips, which have been running machine learning applications in our data centers since 2015. This first generation of TPUs targeted inference (the use of an already trained model, as opposed to the training phase of a model, which has somewhat different characteristics), and here are some of the results we’ve seen:

  • On our production AI workloads that utilize neural network inference, the TPU is 15x to 30x faster than contemporary GPUs and CPUs.
  • The TPU also achieves much better energy efficiency than conventional chips, achieving 30x to 80x improvement in TOPS/Watt measure (tera-operations [trillion or 1012 operations] of computation per Watt of energy consumed).
  • The neural networks powering these applications require a surprisingly small amount of code: just 100 to 1500 lines. The code is based on TensorFlow, our popular open-source machine learning framework.
  • More than 70 authors contributed to this report. It really does take a village to design, verify, implement and deploy the hardware and software of a system like this.
Posted in AI, deep learning | Tagged | Leave a comment

Industrial IoT Market to Reach $151.01 Billion by 2020

Posted on February 3, 2016 by GilPress

The Industrial Internet of Things (IIoT) market was valued at $93.99 Billion in 2014, to reach $151.01 Billion by 2020 and is expected to grow at a CAGR of 8.03% between 2015 and 2020.

IIoT is the integration of complex physical machinery with industrial networks and data analytics solutions to improve operational efficiency and reduce costs. It comprises advanced sensor technologies, machine-to machine communication, real-time data analytics, and machine learning algorithms to enhance the decision-making capabilities of the industries. The need to identify potential failures in machinery in advance to avoid unplanned outages by the use of predictive maintenance techniques is a key influencing factor for the adoption of IIoT solutions. Advancements in sensor technologies as well as improved reliability, coverage area, and bandwidth of cellular technologies are enabling IIoT in sectors such as manufacturing, energy & power, and healthcare among others. The implementation of IIoT is expected to give rise to new business models and provide opportunities to a wide range of new and established companies in the market.

The key players in the market include General Electric (U.S.), Cisco Inc. (U.S.), Intel Corporation (U.S.), Rockwell Automation (U.S.), ARM Holdings plc. (U.K.), ABB Ltd. (Switzerland), Siemens AG (Germany), Honeywell International Inc. (U.S.), Dassault Systèmes SA (France), Huawei Technology Co., Ltd. (China), Zebra Technologies (U.S.), IBM Corporation (U.S.), and Robert Bosch GmbH (Germany) among others.

Posted in Internet of Things, Misc | Tagged , , , | Leave a comment

Data Scientists: The Definition of Sexy

Posted on October 7, 2012 by GilPress

I put “sexy” in the title because I’m told that the words in the title make all the difference in getting noticed on the Web. That has certainly proven true for the Harvard Business Review after it included the word “sexiest” in the title of a recent article. It even got the attention, probably for the first time ever, of Geekosystem, a website devoted to geeks:

The Harvard Business Review, a noted authority on “things that are sexy,” has declared “Data Scientist“ to be the sexiest career of the 21st century. The article reflects the burgeoning mystique of the new and pocket protector friendly gig, which we have to assume narrowly edged out things like “Chippendales dancer” and “calendar firefighter” on its way to being named the sexiest of all possible careers. Because if there’s one thing that gives a job an indefinable allure, it is everybody else being kind of unsure what it is you really do — a quality that data scientists damn near embody.

Whether employers know or don’t know what data scientists do, they have been using—in rapidly-growing numbers—the term“data scientist” in job descriptions in the past two years as Indeed.com’s data demonstrates.   

Continue reading →
Posted in Data Science, Data Science History, Data Scientists | Leave a comment

62% of Enterprises will Use AI by 2018

Posted on August 13, 2016 by GilPress
AI-Survey_small.png

Artificial intelligence has replaced big data this year as the most talked about new set of technologies. As with big data five years ago—behind the hype, the confusion generated by an ill-defined term, and the record funding by VC—we are starting to see emerging investments and practical applications where it matters most—in enterprises.

A new report from Narrative Science, based on a survey of 235 business executives conducted by the National Business Research Institute (NBRI), sheds light on the state-of-AI in enterprises today and in the future: 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. Keep in mind that “AI technologies” is a broad term that includes machine and deep learning, recommendation engines, predictive and prescriptive analytics, automated written reporting and communications, and voice recognition and response.

Here are some other key findings of the survey:

  • 26% are currently using AI technologies to automate manual, repetitive tasks, up from 15% in 2015
  • 20% of those who haven’t yet adopted AI cite lack of clarity regarding its value proposition
  • 58% are using predictive analytics
  • 25% are using automated written reporting and communications
  • 25% are using voice recognition and response
  • 38% see predictions on activity related to machines, customers or business health as the most important benefit of an AI solution
  • 27% see automation of manual and repetitive tasks as the most important benefit of an AI solution
  • 95% of those who indicated that they are skilled at using big data to solve business problems or generate insights also use AI technologies, up from 59% in 2015
  • 61% of enterprises with an innovation strategy are applying AI to their data to find previously missed opportunities such as process improvements or new revenue streams
 

Big data has spawned the current interest and increased investment in artificial intelligence. The availability of large volumes of data—plus new algorithms and more computing power—are behind the recent success of deep learning, finally pulling AI out of its long “winter.” More broadly, the enthusiasm around big data (and the success of data-driven digital natives such as Google and Facebook), has led many enterprises to invest heavily in the collection, storage, and organization of data.

But what is to be done with the data? What is the value of having more data if not in new business insights? To uncover new insights, you need hard-to-find data scientists. Indeed, 59% of the respondents to the survey see the shortage of data science talent as the primary barrier to realizing value from their big data technologies. These companies are now turning to AI technologies to help augment their data science capabilities as partial solution to the talent shortage.

Narrative Science, providing software that transforms data into easy-to-read stories, is one of many startups trying to build a bridge between big data and artificial intelligence, between massive generation and collection of data and developing and applying algorithms to make sense of it.

Gartner has coined a new term—Algorithmic Business—to describe the shift of digital businesses from big data to artificial intelligence. Says Gartner: “It is only when the organization shifts from a focus on big data to ‘big answers’ that value begins to emerge… Algorithms are more essential to the business than data alone. Algorithms define action.”

IDC, another analyst firm (and another coiner of new terms), talks about “Cognitive Services” and predicts they will be embedded in new apps, with the top new investment areas over the next couple of years to be “Contextual Understanding” and “Automated Next Best Action capabilities.” Mastering “cognitive” is a must, says IDC, recommending to enterprises to make machine learning a top priority for 2016—“lots of startups in your industry are already using it to disrupt you.”

Artificial Intelligence is the new big data, not just as the reigning buzzword, but also as a new set of technologies enterprises are exploring so they can turn data into smart actions.

Originally published on Forbes.com

Posted in AI | Tagged | Leave a comment

Upcoming Big Data and Data Science Events

Posted on May 1, 2012 by GilPress

From Data to Knowledge

May 7-11, University of California, Berkeley

Continue reading →
Posted in Misc | Leave a comment

Google and Alphabet: Invention–and Commerical Success–is not Enough

Posted on August 27, 2015 by GilPress
Google-Alphabet-business-628x330

It looks like most of the publications and pundits of the world had something to say about the surprise-of-the-decade: Google’s transformation into Alphabet (Techmeme provides a sample here). For me, the numerous questions they posed only triggered further questions:  Is the new holding company going to be like Berkshire Hathaway, or GE, or AT&T or an early retirement playground for Page and Brin, playing God instead of golf? Is Larry Page saying he does not want to be Bill Gates or does he want to be Thomas Edison Plus? Is it just a simple “re-org,” so typical of large and lumbering companies, masquerading as an “unconventional move”?

In his post (not in a conventional press release) announcing the surprising metamorphosis, Larry Page made sure to remind us that “As Sergey and I wrote in the original founders letter 11 years ago, ‘Google is not a conventional company. We do not intend to become one.’”

Google, now Alphabet, is indeed an unconventional company in many respects, not the least of which is that the aforementioned founders hold 54% of the stock’s voting rights, giving them full control of the company. But at its core, I would argue, it’s a conventional company in a conventional business.

“Invention is not enough,” Page has said (see James Altucher’s post). “You have to combine both things: invention and innovation focus, plus the company that can commercialize things and get them to people.”

For Page and Brin, the key invention was a better search engine. But they brilliantly coupled it, with the help of the bright people they hired, with two other inventions that made that original invention a commercial success: Developing their own computing infrastructure capable of handling Brontobyte Data and a completely new approach to selling advertising.

By relying on advertising for its livelihood (it still accounts for over 90% of Google’s revenues), Google has become a conventional media company. It has enjoyed the growing stream of advertising dollars shifting from print and other channels to online. But it will be the victim of its own success: As online advertising becomes more dominant, growth will slow and Google’s fortunes will rise and fall with the advertising market which typically follows the rise and fall of economy (online advertising in the U.S., growing at 13%, already accounts for 28% of the overall advertising market which will grow only 3.2% this year).

In addition, relying on a segment of the advertising market which is completely dependent on ever-changing technology is a challenge in and of itself, as we have already seen in the ups and downs of display advertising and the shift from desktop to mobile. If some bright young entrepreneur (or a PhD student) finds tomorrow a way to transmit advertising to our brains without the help of devices and the Internet and we readily accept it in exchange for some new, can’t-live-without service, there will be no Google as we know it. Ditto if that proverbial kid in the garage will invent the real “disruption,” a new way to promote companies and their offerings, without what we have called “advertising” for centuries.

That may happen tomorrow or may not happen for a long time, so Page and Brin will continue to have the funds to fuel their ambitions. It’s just that now they will not have to deal at all with the day-to-day management of what has become for them a boring cash cow.

Brin has already done that for a number of years, focusing entirely on “moonshots.” But Page apparently wanted to prove to himself in 2011 (not to the world—he probably doesn’t care much about other people’s opinions) that he can also be a CEO of a large company and could make it re-invent itself. In this (the re-invention part) he completely failed. It may not be a coincidence that we learned of the final demise of Google’s grand social experiment, Page’s attempt to out-Facebook Facebook, just before the surprise Alphabet announcement. (It may also not be a coincidence that the announcement came on the 20th anniversary of When Larry Met Sergey, the first milestone in the official Google history timeline).

The failures are insignificant light of the history Page and Brin have made by giving millions of people around the world, in exchange for their data, very useful tools, at no cost. But brilliant inventions turned into commercial success, however, are not enough for the likes of Page and Brin and they never liked where the money supporting their free services came from, channeling (probably preceding) Jeff Hammerbacher’s sentiment: “The best minds of my generation are thinking about how to make people click ads.” Their version of a mid-life crisis is to remove themselves from their very successful one-trick advertising pony and immerse themselves in attempting to make very big history or Brontobyte history.

Page and Brin are sometimes mentioned—and explained—together with Amazon’s Jeff Bezos as the result of Montessori education (see here and here). But I think there is something much more important at the root of Page, Brin, and Bezos’s ambitions and successful enterprises. In the words of Harry Louis Sullivan, describing Chicago in 1875:

“Big” was the word. “Biggest” was preferred, and “the biggest in the world” was the braggart phrase on every tongue. Chicago had had the biggest conflagration “in the world.” It was the biggest grain and lumber market “in the world.” It slaughtered more hogs than any other city “in the world.” It was the greatest railroad center, the greatest this, and the greatest that… what they said was true; and had they said, in the din, we are the crudest, rawest, most savagely ambitious dreamers and would-be doers in the world, that also might be true… These men had vision. What they saw was real, they saw it as destiny.

Continuing an American tradition (how “unconventional”), Page, Brin, and Bezos saw “big” as their destiny. Page and Brin named their company after a very big number. Bezos chose the largest river in the world to stand for “the everything store.” But Bezos has taken a different route to world domination, one that is not depended on advertising and using us as the product, but on changing the way we buy and sell goods and services, inventing new ways to consume while driving down the cost of consumption. His one-trick pony, selling books online, has metamorphosed into selling everything, including computer services, serving as a platform for other sellers, creating content, designing devices, and more.

Page has said “especially in technology, we need revolutionary change, not incremental change, “and “I think as technologists we should have some safe places where we can try out new things and figure out the effect on society.” Bezos believes in incremental change and doesn’t talk much about Amazon’s impact on society. In about ten years, we should have a better idea of which approach—Alphabet’s or Amazon’s—has left a bigger and more positive impact on the world.

An earlier version of this psot was published on Forbes.com

Posted in Misc | Tagged , | Leave a comment