Text Analysis Systems Mine Workplace Emails to Measure Staff Sentiments

Image from Pixabay.com

Have you ever been employed in a genuinely cooperative and productive environment where you looked forward each day to making your contribution to the enterprise and assisting your colleagues? Conversely, have you ever worked in a highly stressful and unsupportive atmosphere where you dreading going back there nearly every day?  Or perhaps you have found in your career that your jobs and employers were somewhere in the mid-range of this spectrum of office cultures.

For all of these good, bad or indifferent workplaces, a key question is whether any of the actions of management to engage the staff and listen to their concerns ever resulted in improved working conditions and higher levels of job satisfaction?

The answer is most often “yes”. Just having a say in, and some sense of control over, our jobs and workflows can indeed have a demonstrable impact on morale, camaraderie and the bottom line. As posited in the Hawthorne Effect, also termed the “Observer Effect”, this was first discovered during studies in the 1920’s and 1930’s when the management of a factory made improvements to the lighting and work schedules. In turn, worker satisfaction and productivity temporarily increased. This was not so much because there was more light, but rather, that the workers sensed that management was paying attention to, and then acting upon, their concerns. The workers perceived they were no longer just cogs in a machine.

Perhaps, too, the Hawthorne Effect is in some ways the workplace equivalent of the Heisenberg’s Uncertainty Principle in physics. To vastly oversimplify this slippery concept, the mere act of observing a subatomic particle can change its position.¹

Giving the processes of observation, analysis and change at the enterprise level a modern (but non-quantum) spin, is a fascinating new article in the September 2018 issue of The Atlantic entitled What Your Boss Could Learn by Reading the Whole Company’s Emails, by Frank Partnoy.  I highly recommend a click-through and full read if you have an opportunity. I will summarize and annotate it, and then, considering my own thorough lack of understanding of the basics of y=f(x), pose some of my own physics-free questions.

“Engagement” of Enron’s Emails

By Enron [Public domain], via Wikimedia Commons

Andrew Fastow was the Chief Financial Officer of Enron when the company infamously collapsed into bankruptcy in December 2001. Criminal charges were brought against some of the corporate officers, including Fastow, who went to prison for six years as a result.

After he had served his sentence he became a public speaker about his experience. At one of his presentations in Amsterdam in 2016, two men from the audience approached him. They were from KeenCorp, whose business is data analytics. Specifically, their clients hire them to analyze the email “word patterns and their context” of their employees. This is done in an effort to quantify and measure the degree of the staff’s “engagement”. The resulting numerical rating is higher when they feel more “positive and engaged”, while lower when they are unhappier and less “engaged”.

The KeenCorp representatives explained to Fastow that they had applied their software to the email archives of 150 Enron executives in an effort to determine “how key moments in the company’s tumultuous collapse” would be assessed and a rated by their software. (See also the February 26, 2016 Subway Fold post entitled The Predictive Benefits of Analyzing Employees’ Communications Networks, covering, among other things, a similar analysis of Enron’s emails.)

KeenCorp’s software found the lowest engagement score when Enron filed for bankruptcy. However, the index also took a steep dive two years earlier. This was puzzling since the news about the Enron scandal was not yet public. So, they asked Fastow if he could recall “anything unusual happening at Enron on June 28, 1999”.

Sentimental Journal

Milky Way in Mauritius, Image by Jarkko J

Today the text analytics business, like the work done by KeenCorp, is thriving. It has been long-established as the processing behind email spam filters. Now it is finding other applications including monitoring corporate reputations on social media and other sites.²

The finance industry is another growth sector, as investment banks and hedge funds scan a wide variety of information sources to locate “slight changes in language” that may point towards pending increases or decreases in share prices. Financial research providers are using artificial intelligence to mine “insights” from their own selections of news and analytical sources.

But is this technology effective?

In a paper entitled Lazy Prices, by Lauren Cohen (Harvard Business School and NBER), Christopher Malloy (Harvard Business School and NBER), and Quoc Nguyen (University of Illinois at Chicago), in a draft dated February 22, 2018, these researchers found that the share price of company, in this case NetApp in their 2010 annual report, measurably went down after the firm “subtly changes” its reporting “descriptions of certain risks”. Algorithms can detect such changes more quickly and effectively than humans. The company subsequently clarified in its 2011 annual report their “failure to comply” with reporting requirements in 2010. A highly skilled stock analyst “might have missed that phrase”, but once again its was captured by “researcher’s algorithms”.

In the hands of a “skeptical investor”, this information might well have resulted in them questioning the differences in the 2010 and 2011 annual reports and, in turn, saved him or her a great deal of money. This detection was an early signal of a looming decline in NetApp’s stock. Half a year after the 2011 report’s publication, it was reported that the Syrian government has bought the company and “used that equipment to spy on its citizen”, causing further declines.

Now text analytics is being deployed at a new target: The composition of employees’ communications. Although it has been found that workers have no expectations of privacy in their workplaces, some companies remain reluctant to do so because of privacy concerns. Thus, companies are finding it more challenging to resist the “urge to mine employee information”, especially as text analysis systems continue to improve.

Among the evolving enterprise applications are the human resources departments in assessing overall employee morale. For example, Vibe is such an app that scans through communications on Slack, a widely used enterprise platform. Vibe’s algorithm, in real-time reporting, measures the positive and negative emotions of a work team.

Finding Context

“Microscope”, image by Ryan Adams

Returning to KeenCorp, can their product actually detect any wrongdoing by applying text analysis? While they did not initially see it, the company’s system had identified a significant “inflection point” in Enron’s history on the June 28, 1999 date in question. Fastow said that was the day the board had discussed a plan called “LJM”, involving a group of questionable transactions that would mask the company’s badly under-performing assets while improving its financials. Eventually, LJM added to Enron’s demise. At that time, however, Fastow said that everyone at the company, including employees and board members, was reluctant to challenge this dubious plan.

KeenCorp currently has 15 employees and six key clients. Fastow is also one of their consultants and advisors. He also invested in the company when he saw their algorithm highlight Enron’s employees’ concerns about the LJM plan. He hopes to raise potential clients’ awareness of this product to help them avoid similar situations.

The company includes heat maps as part of its tool set to generate real-time visualizations of employee engagement. These can assist companies in “identifying problems in the workplace”. In effect, it generates a warning (maybe a warming, too), that may help to identify significant concerns. As well, it can assist companies with compliance of government rules and regulations. Yet the system “is only as good as the people using it”, and someone must step forward and take action when the heat map highlights an emerging problem.

Analyzing employees’ communications also presents the need for applying a cost/benefit analysis of privacy considerations. In certain industries such as finance, employees are well aware that their communications are being monitored and analyzed, while in other businesses this can be seen “as intrusive if not downright Big Brotherly”. Moreover, managers “have the most to fear” from text analysis systems. For instance, it can be used to assess sentiment when someone new is hired or given a promotion. Thus, companies will need to find a balance between the uses of this data and the inherent privacy concerns about its collection.

In addressing privacy concerns about data collection, KeenCorp does not “collect, store on report” info about individual employees. All individually identifying personal info is scrubbed away.

Text analysis is still in its early stages. There is no certainty yet that it may not register a false positive reading and that it will capture all emerging potential threats. Nonetheless it is expected to continue to expand and find new fields for application. Experts predict that among these new areas will be corporate legal, compliance and regulatory operations. Other possibilities include protecting against possible liabilities for “allegations of visa, fraud and harassment”.

The key takeaway from the current state of this technology is to ascertain the truth about employees’ sentiments not by snooping, but rather, “by examining how they are saying it”.

My Questions

  • “Message In a Bottle”, Image from Pixabay.com

    Should text analysis data be factored into annual reviews of officers and/or board members? If so, how can this be done and what relative weight should it be given?

  • Should employees at any or all levels and departments be given access to text analysis data? How might this potentially impact their work satisfaction and productivity?
  • Is there a direct, casual or insignificant relationship between employee sentiment data and up and/or down movements in market value? If so, how can companies elevate text analysis systems to higher uses?
  • How can text analysis be used for executive training and development? Might it also add a new dimension to case studies in business schools?
  • What does this data look like in either or both of short-term and long-term time series visualizations? Are there any additional insights to be gained by processing the heat maps into animations to show how their shape and momentum are changing over time?

 


1.  See also the May 20, 2015 Subway Fold post entitled A Legal Thriller Meets Quantum Physics: A Book Review of “Superposition” for the application of this science in a hard rocking sci-fi novel.

2These 10 Subway Fold posts cover other measurements of social media analytics, some including other applications of text analytics.

Book Review of “Frenemies: The Epic Disruption of the Ad Business (and Everything Else)”

“Advertising in Times Square”, image by Dirk Knight

Every so often, an ad campaign comes along that is strikingly brilliant for its originality, execution, persuasiveness, longevity, humor and pathos. During the mid-1980’s, one of these bright shining examples was the television ads for Bartles & Jaymes Wine Coolers. They consisted of two fictional characters: Frank Bartles, who owned a winery and did all of the talking, and Ed Jaymes, a farmer who never spoke a word but whose deadpan looks were priceless. They traveled across the US to different locations in pursuit of sales, trying to somehow adapt their approaches to reflect the local surroundings. Bartles was very sincere but often a bit naive in his pitches along the way, best exemplified in this ad and another one when they visited New York.

These commercials succeeded beyond all expectations in simultaneously establishing brand awareness, boosting sales and being laugh-out-loud hilarious because Bartles’s and Jaymes’s were such charming, aw-shucks amateurs. In actuality, these ads were deftly conceived and staged by some smart and savvy creatives from the Hal Riney & Partners agency. For further lasting effect, they always had Bartles express his appreciation to the viewers at the end of each spot with his memorable trademark tagline of “Thanks for your support”. These 30-second video gems are as entertaining today as they were thirty years ago.

But those halcyon days of advertising are long gone. The industry’s primary media back then was limited to print, television and radio. Creativity was its  cornerstone and the words “data analytics” must have sounded like something actuaries did in a darkened room while contemplating the infinite. (Who knows, maybe it still does to some degree.)

Fast forwarding to 2018, advertising is an utterly different and hyper-competitive sector whose work product is largely splayed across countless mobile and stationary screens on Planet Earth. Expertly chronicling and precisely assaying the transformative changes happening to this sector is an informative and engaging new book entitled Frenemies: The Epic Disruption of the Ad Business (and Everything Else) [Penguin Press, 2018], by the renowned business author Ken Auletta. Just as a leading ad agency in its day cleverly and convincingly took TV viewers on an endearing cultural tour of the US as we followed the many ad-ventures of Bartles & Jaymes, so too, this book takes its readers on a far-ranging and immersive tour of the current participants, trends, challenges and technologies affecting the ad industry.

A Frenemy of My Frenemy is My Frenemy

Image from Pixabay

This highly specialized world is under assault from a confluence of competitive, online, economic, social and mathematical forces. Many people who work in it are deeply and rightfully concerned about its future and the tenure of their places in it. Auletta comprehensively reports on and assesses these profound changes from deep within the operations of several key constituencies (the “frenemies”, conflating “friend” and “enemy”). At first this might seem a bit too much of “inside baseball” (although the ad pitch remains alive and well), but he quickly and efficiently establishes who’s who and what’s what in today’s morphing ad markets, making this book valuable and accessible to readers both within and outside of this field.  It can also be viewed as a multi-dimensional case study of an industry right now being, in the truest sense of the word, disrupted.¹ There is likewise much to learned and considered here by other businesses being buffeted by similar winds.

Frenemies, as thoroughly explored throughout this book, are both  business competitors and partners at the same time. They are former and current allies in commerce who concurrently cooperate and compete. Today they are actively infiltrating each other’s markets. The full matrix of frenemies and their threats and relationships to each other includes the interests and perspectives of ad agencies and their clients, social media networks, fierce competition from streamers and original content producers like Netflix², traditional media in transition to digital platforms, consulting companies and, yes, consumers.

Auletta travels several parallel tracks in his reporting. First, he examines the past, present on onrushing future with respect to revenue streams, profits, client bases served, artificial intelligence (AI) driven automation, and the frenemies’ very fluid alliances. Second, he skillfully deploys the investigative journalistic strategy of “following the money” as it ebbs and flows in many directions among the key players. Third, he illuminates the industry’s evolution from Don Draper’s traditional “Mad Men” to 2018’s “math men” who are the data wranglers, analysts and strategists driven by ever more thin-sliced troves of consumer data the agencies and their corporate clients are using to achieve greater accuracy and efficiency in selling their goods and services.

A deep and wide roster of C-level executives from these various groups were interviewed for the book. Chief among them are two ad industry legends who serve as the x and y axes upon which Auletta has plotted a portion of his reporting. One is Martin Sorrell, who was the founder and CEO of WPP, the world’s largest advertising holding company.³ The other is Michael Kassan, the founder and CEO of MediaLink, a multifaceted firm that connects, negotiates and advises on behalf of a multitude of various parties, often competitors in critical matters affecting the ad business. Both of these individuals have significantly shaped modern advertising over many decades and are currently propagating some of the changes spotlighted in the book in trying to keep it vital, relevant and profitable.

Online Privacy v. Online Primacy

“Tug of War”, image by Pixabay

The established tradition of creativity being the primary driver of advertising creation and campaigns has given way to algorithm-driven data analytics. All of the frenemies and a myriad of other sites in many other parsecs of the websphere vacuum up vast amounts of data on users, their online usage patterns, and even go so far as to try to infer their behavioral attributes. This is often combined with additional personal information from third-party sources and data brokers. Armed with all of this data and ever more sophisticated means for sifting and intuiting it, including AI4, the frenemies are devising their campaigns to far more precisely target potential consumers and their cohorts with finely grained customized ads.

The high point of this book is Auletta’s nuanced coverage of the ongoing controversy involving the tension between frenemies using data analytics to increase click-through rates and, hopefully, sales versus respecting the data privacy of people as they traverse the Web. In response to this voracious data collection, millions of users have resisted this intrusiveness by adding free browser extensions such as AdBlock Plus to circumvent online tracking and ad distribution.5 This struggle has produced a slippery slope between the commercial interests of the frenemies and consumers’ natural distaste for advertising, as well as their resentment at having their data co-opted, appropriated and misused without their knowledge or consent. Recently, public and governmental concerns were dramatically displayed in the harsh light of the scandals involving Facebook and Cambridge Analytica.

Furthermore, Google and Facebook dominate the vast majority of online advertising traffic, revenues and, most importantly, the vast quantum of user information which ad agencies believe would be particularly helpful to them in profiling and reaching consumers. Nonetheless, they maintain it is highly proprietary to them alone and much of it has not been shared. Frenemies much?

Additional troubling trends for the ad industry are likewise given a thorough 3-D treatment. Auletta returns to the axiom several times that audiences do not want to be interrupted with ads (particularly on their mobile devices). Look no further than the likes of premium and the major streaming services who offer all of their content uninterrupted in its entirety. The growing ranks of content creators they engage know this and prefer it because they can concentrate on their presentations without commercial breaks slicing and dicing their narrative continuity. The still profitable revenue streams flowing from this are based upon the strengths of the subscription model.

Indeed, in certain cases advertising is being simultaneously disrupted and innovated. Some of the main pillars of the media like The New York Times are now expanding their in-house advertising staff and service offerings. They can offer a diversified array of ads and analyses directly to their advertisers. Likewise, engineering-driven operations like Google and Facebook can deploy their talent benches to better target consumers for their advertisers by extracting and applying insights from their massive databases. Why should their clients continue go to the agencies when their ads can be composed and tracked for them directly?

Adapt or Go Home

“Out with the Old, In with the New”, image by Mark

The author presents a balanced although not entirely sanguine view of the ad industry’s changes to maintain its composure and clients in the midst of this storm. The frenemy camps must be willing to make needed and often difficult adjustments to accommodate emerging technological and strategic survival methods. He examines the results of two contemporary approaches to avoiding adblocking apps and more fully engaging very specific audiences. One is called “native advertising“, which involves advertisers producing commercial content and paying for its placement online or in print to promote their own products. Generally, these are formatted and integrated to appear as though they are integrated with a site’s or publication’s regular editorial content but contain a notice that it is, in fact “Advertising”.

However, Auletta believes that the second adaptive mechanism, the online subscription model, will not be much more sustainable beyond its current successes. Consumers are already spending money on their favorite paywalled sites.  But it would seem logical that users might not be thus willing to pay for Facebook and others that have always been free. As well, cable’s cord-cutters are continuing to exhibit steady growing in their numbers and their migrations towards streaming services such as Amazon Prime.6

Among the media giants, CBS seems to be getting their adaptive strategies right from continuing to grow multiple revenue streams. They now have the legal rights and financial resources to produce and sell original programming. They have also recently launched original web programming such as Star Trek: Discovery on a commercial-free subscription basis on CBS All Access. This can readily be seen as a challenge to Netflix despite the fact that CBS also providing content to Netflix. Will other networks emulate this lucrative and eyeball attracting model?

As Auletta also concludes, for now at least, consumers as frenemies, appear to be the beneficiaries of all this tumult. They have many device agnostic platforms, pricing options and a surfeit of content from which to choose. They can also meaningfully reduce, although not entirely eliminate, ads following them all over the web and those pesky stealth tracking systems. Whether they collectively can maintain their advantage is subject to sudden change in this environment.

Because of the timing of the book’s completion and publication, the author and publisher should consider including in any subsequent edition the follow-up impacts of Sorrell’s departure from WPP and his new venture (S4 Capital), the effects of the May 2018 implementation of EU’s General Data Protection Regulation (GDPR), and the progress of any industry or government regulation following the raft of recent massive data breaches and misuses.

Notwithstanding that, however, “Frenemies” fully delivers on all of its book jacket’s promises and premises. It is a clear and convincing case of truth in, well, advertising.

So, how would Frank Bartles and Ed Jaymes 2.0 perceive their promotional travels throughout today’s world? Would their folksy personas play well enough on YouTube to support a dedicated channel for them? Would their stops along the way be Instagram-able events? What would be their reactions when asked to Google something or download a podcast?

Alternatively, could they possibly have been proto-social media influencers who just showed up decades too soon? Nah, not really. Even in today’s digital everything world, Frank and Ed 1.0 still abide. Frank may have also unknowingly planted a potential meme among today’s frenemies with his persistent proclamations of “Thanks for your support”: The 2018 upgrade might well be “Thanks for your support and all of your data”.

 


For a very enlightening interview with Ken Auletta, check out the June 26, 2018 podcast entitled Game Change: How the Ad Business Got Disrupted, from The Midday Show on WNYC (the local NPR affiliate in New York).


September 4, 2018 Update: Today’s edition of The New York Times contains an highly enlightening article directly on point with many of the key themes of Frenemies entitled Amazon Sets Its Sights on the $88 Billion Online Ad Market, by Julie Creswell. The report details Amazon’s significant move into online advertising supported by its massive economic, data analytics, scaling and strategic resources. It comprehensively analyzes the current status and future prospects of the company’s move into direct competition with Google and Facebook in this immense parsec of e-commerce. I highly recommend a click-through and full read of this if you have an opportunity.


1.   The classic work on the causes and effect of market disruptions, the disruptors and those left behind is The Innovator’s Dilemma, by Clayton Christensen (HarperBusiness, 2011). The first edition of the book was published in 1992.

2.    Netflix Topples HBO in Emmy Nominations, but ‘Game of Thrones’ Still Rules, July 13, 2018, New York Times, by The Associated Press. However, see also Netflix Drops Dud on Wall St. As Subscriber Growth Flops, July 16, 2018, New York Times, by Reuters.

3.   Sorrell is reported in the book as saying he would not leave anytime soon from running WPP. However, following the book’s publication, he was asked to step down in April 2018 following allegations of inappropriate conduct. See Martin Sorrell Resigns as Chief of WPP Advertising Agency, New York Times, by Matt Stevens and Liz Alderman, April 14, 2018. Nonetheless, Sorrell has quickly returned to the industry as reported in Martin Sorrell Beats WPP in Bidding War for Dutch Marketing Firm, New York Times, by Sapna Maheshwari, July 10, 2018.

4.  For a very timely example, see The Ad Agency Giant Omnicom Has Created a New AI Tool That is Poised to Completely Change How Ads Get Made, BusinessInsider.com, by Lauren Johnson,  July 12, 2018.

5.   Two other similar anti-tracking browser extensions in wide usage include, among others Ghostery and Privacy Badger.

6.   See also  Cord-Cutting Keeps Churning: U.S. Pay-TV Cancelers to Hit 33 Million in 2018 (Study), Variety.com, by Todd Spangler, July 24, 2018.

Single File, Everyone: The Advent of the Universal Digital Profile

Ducks at Parramatta, Image by Stilherrian

Throughout grades 1 through 6 at Public School 79 in Queens, New York, the teachers had one universal command they relied upon to try to quickly gather and organize the students in each class during various activities. They would announce “Single file, everyone”, and expect us all to form a straight line with one student after the other all pointed in the same direction. They would usually deploy this to move us in an orderly fashion to and from the lunchroom, schoolyard, gym and auditorium. Not that this always worked as several requests were usually required to get us all to quiet down and line up.

Just as it was used back then as a means to bring order to a room full of energetic grade-schoolers,  those three magic words can now be re-contextualized and re-purposed for today’s digital everything world when applied to a new means of bringing more control and safety to our personal data. This emerging mechanism is called the universal digital profile (UDP). It involves the creation of a dedicated file to compile and port an individual user’s personal data, content and usage preferences from one online service to another.

This is being done in an effort to provide enhanced protection to consumers and their digital data at a critical time when there have been so many online security breaches of major systems that were supposedly safe. More importantly, these devastating hacks during the past several years have resulted in the massive betrayals of users’ trust that need to be restored.

Clearly and concisely setting the stage for the development of UDPs was an informative article on TechCrunch.com entitled The Birth of the Universal Digital Profile, by Rand Hindi, posted on May 22, 2018. I suggest reading it in its entirety. I will summarize and annotate it, and then pose some of my own questions about these, well, pro-files.

Image from Pixabay

The Need Arises

It is axiomatic today that there is more concern over online privacy among Europeans than other populations elsewhere. This is due, in part, to the frequency and depth of the above mentioned deliberate data thefts. These incidents and other policy considerations led to the May 25, 2018 enactment and implementation of the General Data Protection Regulation (GDPR) across the EU.

The US is presently catching up in its own citizens’ levels of rising privacy concerns following the recent Facebook and Cambridge Analytica scandal.¹

Among its many requirements, the GDPR ensures that all individuals have the right to personal data portability, whereby the users of any online services can request from these sites that their personal data can be “transferred to another provider, without hindrance”. This must be done in a file format the receiving provider requires. For example, if a user is changing from one social network to another, all of his or her personal data is to be transferred to the new social network in a workable file format.

The exact definition of “personal profile” is still open to question. The net effect of this provision is that one’s “online identity will soon be transferable” to numerous other providers. As such transfer requests increase, corporate owners of such providers will likely “want to minimize” their means of compliance. The establishment of standardized data formats and application programming interfaces (APIs) enabling this process would be a means to accomplish this.²

Aurora Borealis, Image by Beverly

A Potential Solution

It will soon become evident to consumers that their digital profiles can become durable, reusable and, hence, universal for other online destinations. They will view their digital profiles “as a shared resource” for similar situations. For instance, if a user has uploaded his or her profile to a site for verification, in turn, he or she should be able to re-use such a “verified profile elsewhere”.³  

This would be similar to the Facebook Connect’s functionality but with one key distinction: Facebook would retain no discretion at all over where the digital profile goes and who can access it following its transfer. That control would remain entirely with the profile’s owner.

As the UDP enters the “mainstream” usage, it may well give rise to “an entire new digital economy”. This might include new services such as “personal data clouds to personal identity aggregators or data monetization platforms”. In effect, increased interoperability between and among sites and services for UDPs might enable these potential business opportunities to take root and then scale up.

Digital profiles, especially now for Europeans, is one of the critical “impacts of the GDPR” on their online lives and freedom. Perhaps its objectives will spread to other nations.

My Questions

  • Can the UDP’s usage be expanded elsewhere without the need for enacting GDPR-like regulation? That is, for economic, public relations and technological reasons, might online services support UDPs on their own initiatives rather than waiting for more governments to impose such requirements?
  • What additional data points and functional capabilities would enhance the usefulness, propagation and extensibility of UDPs?
  • What other business and entrepreneurial opportunities might emerge from the potential web-wide spread of a GDPR and/or UDP-based model?
  • Are there any other Public School 79 graduates out there reading this?

On a very cold night in New York on December 20, 2017, I had an opportunity to attend a fascinating presentation  by Dr. Irene Ng before the Data Scientists group from Meetup.com about an inventive alternative for dispensing one’s personal digital data called the Hub of All Things (HAT). [Clickable also @hubofallthings.] In its simplest terms, this involves the provision of a form of virtual container (the “HAT” situated on a “micro-server”), storing an individual’s personal data. This system enables the user to have much more control over whom, and to what degree, they choose to allow access to their data by any online services, vendors or sites. For the details on the origin, approach and technology of the HAT, I highly recommend a click-through to a very enlightening new article on Medium.com entitled What is the HAT?, by Jonathan Holtby, posted yesterday on June 6, 2018.


1.  This week’s news bring yet another potential scandal for Facebook following reports that they shared extensive amounts of personal user data with mobile device vendors, including Huawei, a Chinese company that has been reported to have ties with China’s government and military. Here is some of the lead coverage so far from this week’s editions of The News York Times:

2.  See also these five Subway Fold posts involving the use of APIs in other systems.

3.  See Blockchain To The Rescue Creating A ‘New Future’ For Digital Identities, by Roger Aitlen, posted on Forbes.com on January 7, 2018, for a report on some of the concepts of, and participants in, this type of technology.

Mary Meeker’s 2018 Massive Internet Trends Presentation

“Blue Marble – 2002”, Image by NASA Goddard Space Flight Center

Yesterday, on May 30, 2018, at the 2018 Code Conference being held this week in Rancho Palos Verdes, California, Mary Meeker, a world-renowned Internet expert and partner in the venture capital firm Kleiner Perkins, presented her seventeenth annual in-depth and highly analytical presentation on current Internet trends. It is an absolutely remarkable accomplishment that is highly respected throughout the global technology industry and economy. The video of her speech is available here on Recode.com.

Her 2018 Internet Trends presentation file is divided into a series of twelve main sections covering, among many other things: Internet user, usage and devices growth rates; online payment systems; content creation; voice interfaces’ significant potential;  user experiences; Amazon’s and Alibaba’s far-reaching effects; data collection, regulation and privacy concerns; tech company trends and investment analyses; e-commerce sectors, consumers experiences and emerging trends;  social media’s breadth, revenue streams and influences; the grown and returns of online advertising; changes in consumer spending patterns and online pricing; key transportation, healthcare and demographic patterns;  disruptions in how, where and whether we work; increasingly sophisticated data gathering, analytics and optimization; AI trends, capabilities and market drivers; lifelong learning for the workforce; many robust online markets in China for, among many, online retail, mobile media and entertainment services; and a macro analysis of the US economy and online marketplaces.

That is just the tip of the tip of the iceberg in this 294-slide deck.

Ms. Meeker’s assessments and predictions here form an extraordinarily comprehensive and insightful piece of work. There is much here for anyone and everyone to learn and consider in the current and trending states nearly anything and everything online. Moreover, there are likely many potential opportunities for new and established businesses, as well as other institutions, within this file.

I very highly recommend that you set aside some time to thoroughly read through and fully immerse your thoughts in Ms. Meeker’s entire presentation. You will be richly rewarded with knowledge and insight that can potentially yield a world of informative, strategic and practical dividends.


September 15, 2018 Update: Mary Meeker has left Kleiner Perkins to start her own investment firm. The details of this are reported in an article in the New York Times entitled Mary Meeker, ‘Queen of the Internet,’ Is Leaving Kleiner Perkins to Start a New Fund, by Erin Griffith, posted on September 14, 2018. I wish her the great success for her new venture. I also hope that she will still have enough time that she can continue to publish her brilliant annual reports on Internet trends.

TechDay New York 2018: 500 Local Startups’ Displays, Demos and Delights for the Crowd

All photos on this page by Alan Rothman.

Sometimes in traditional advertising for creative works like movies, TV shows, books and plays, the quoted reviews and taglines include the exclamation “This one’s got it all!” Yet this is only rarely, if ever, true.

Well, wait a minute. Let’s check that.

Last Thursday, May 10th, I had the great pleasure of attending TechDay New York 2018, held at Pier 94, on the West Side of midtown Manhattan. This is a monumental annual exhibition of 500 startups located throughout NYC almost did have it all. In addition to all of these new companies, there were separate areas set up for brief products and services demos and talks by industry experts. Even the TV show Shark Tank was on site there.

First and foremost, massive amounts of thanks to everyone at Techday for putting on such a terrifically enjoyable, informative and memorable event. Their efforts clearly showed that they worked long and hard to get everything about it right.

On to the show …

One of New York City’s greatest economic and cultural strengths has always been its incredible global diversity of it population. So, too, is that dynamic comparably evident in the breadth of it startup ecosystem. From one end of Pier 94 to the other, there was artificial intelligence this, blockchain that, and data analytics everything infused everywhere.

Just a sampling of who and what were on display, among many others, were startups in legal services, architecture, editorial software, video search, incubators and accelerators, social media support services, event planning platforms, programmer aptitude testing, intellectual property protection, pharmacy order and delivery services, office design consultants, branding and digital experience designers, augmented and virtual reality hardware and software, venture capitalist, crowdfunding services, multi-platform public relations strategists, fashion designers, food services (some displaying much chocolate!), consumer data tracking analyst, competitive intelligence trackers and analysts, restaurant reservations, media consultants, phone apps, online security planning and systems, fully integrated electronic health records and billing systems, and dedicated tech recruiters as well as exhibitors themselves looking for new talent. Whew!

Notwithstanding the vastness of the exhibition space, hundreds of startups and thousands of attendees, the organization and presentations of the startups’ display areas was well planned and easy to navigate. The startups were grouped in helpful sectors for social media, e-commerce, fintech and others into more general categories.

The three among the twelve TechDay Talks I attended were quite compelling and evinced great enthusiasm by both the speakers and their audiences. These included:

Above all other considerations, I found every entrepreneur I stopped and spoke with, asking them to tell me about their company, to be highly enthusiastic, engaging and sincere. They were knowledgeable about their markets and competitors, sounded willing to adapt to changing market conditions and, most importantly, convinced that they would become successful. At no point did any of them move on to their next visitors until they sensed that I understood what they were saying and encouraging me to follow their progress online. They were not so much giving visitors hard sales pitches, but rather, much more of the who, what, where, how and when of their businesses. My gratitude to all of them for their patience with me and many of the other attendees I saw them talking to with the same level of professionalism.

Below are some of the photos I took while I was there. I was trying to capture some sense of infectious energy and engagement being generated across entire day’s events.

My very best wishes to all 500 startups to succeed and prosper.

 


 *   For some very worthwhile deep and wide analysis of the effects of AI upon current and future employment, I highly recommend the recently published book entitled Human + Machine: Reimagining Work in the Age of AI, by Paul Daugherty (Harvard Business Review Press, 2018).


 

 

 

 

 

Taking Note of Music Tech’s VC and Accelerator Market Trends in 2017

As a part of today’s modern music industry there exists a complementary and thriving support system of venture capital firms and music tech startup accelerators who are providing a multitude of innovative services.  A fascinating examination of the current state of this ecosystem appeared in an article entitled Music Pushes to Innovate Beyond Streaming, But Investors Play It Safe: Analysis, by Cherie Hu, posted on Billboard.com on 7/24/17. I highly recommend reading it in its entirety for its insights, assessments and accompanying graphics.

I will summarize this feature here, add some links and annotations and, well, venture a few of my own questions. Also, I believe this is a logical follow to three previous Subway Fold posts about the music biz including:

Tempo

In mid-2017, the music tech market is generating signals as to its direction and viability. For example, Jawbone, the once thriving manufacturer of wearable audio devices is currently being liquidated; Soundcloud  the audio distribution platform let go of 40 percent of its staff recently only days before the firm’s tenth anniversary; and Pandora has experienced high turnover among its executives while seeking a sale.

Nonetheless, the leaders in music streaming are maintaining “the music industry’s growth”. Music tech showcases and music accelerators including SXSW Music Startup Spotlight, the Midemlab Accelerator, and Techstars Music are likewise driving market transformation.   During 2017 thus far, 54 music startups from more than 25 cities across the globe have taken part in these three entities. They have presented a range of submissions including “live music activations and automated messaging to analytics tools for labels and artists”.

While companies such as Live Nation, Balderton Capital and Evolution Media have previously invested in music startups, most investors at this mid-year point have never previously funded a company in this space. This is despite the fact that investments in this market sector have rarely returned the 30% that VCs generally seek. As well, a number of established music industry stars are participating as first-time or veteran investors this year.

Of the almost $900 million funding in music tech for the first half of this year, 75% was allocated for streaming services – – 82% of which went only to the leading four companies. However, there remains a “stark disconnect” involving the types of situations where music accelerators principally “lend their mentorship” in “hardware, virtual reality1, chatbots, label tools”, and the issues that VC concentrate the funding such as “streaming, social media, brands”.  Moreover, this situation has the potential of “stifling innovation” across the industry.

To date, music accelerators have “successfully given a platform and resources” to some sectors of the industry that VCs don’t often consider. For example, automated messaging and AI-generated music2 are both categories that music accelerators avoided until recently, now equal 15% of membership. This expansion into new categories reflects a much deeper “tech investment and hiring trends”. Leading music companies are now optimistic about virtual digital assistants (VDA) including chatbots and voice-activated systems such as Amazon Alexa3. As well, Spotify recently hired away a leading AI expert from Sony.

Rhythm

However, this “egalitarian focus” on significant problems has failed to “translate into the wider investing landscape” insofar as the streaming services have attracted 75% of music tech funding. The data further shows that licensing/rights/catalog management, social music media, and music, brands and advertising finished, in that order, in second at 11.1%, third at 7.1% and fourth at 3.9%.

These percentages closely match those for 2016. Currently, many VCs in this sector view streaming “as the safest model available”. It is also one upon which today’s music industry depends for its survival.

Turning to the number of rounds of music tech funding rather than the dollar amounts raised, by segments within the industry, a “slightly more egalitarian landscape” emerges:

  • Music hardware, AI-generated music, and VR and Immersive media each at 5.0%
  • Live music; music brands and advertising; streaming; and social music media each at 15.0%
  • Licensing, rights, and catalog management at 25% (for such companies as Kobalt Music, Stem and Dubset)

Categories that did relatively well in both their number of rounds of funding and accelerator membership were “catalog management, social music platforms, and live music”.

Those music tech startups that are more “futuristic” like hardware and VR are seen favorably by “accelerators and conference audiences”, but less so among VCs.  Likewise, while corporate giants including Live Nation, Universal Music Group, Citi and Microsoft have announced movement into music VR in the past six months, VC funding for this tech remained “relatively soft”.

Even more pronounced is the situation where musical artists and label services such as Instrumental (a influencer discovery platform) and chart monitors like Soundcharts have not raised any rounds of funding. This is so “despite unmatched attention from accelerators. This might be due to these services not being large enough to draw too “many traditional investors”.

An even more persistent problem here is that not many VCs “are run by people with experience in the music industry” and are familiar with its particular concerns. Once exception is Plus Eight Equity Partners, who are trying to address “this ideological and motivational gap”.

Then there are startups such as 8tracks and Chew who are “experimenting with crowdfunding” in this arena but who were not figured into this analysis.

In conclusion, the tension between a “gap in industry knowledge” and the VCs’ preference for “safety and convenience”, is blurring the line leading from accelerator to investment for many of these imaginative startups.

My Questions

  • Of those music startups who have successfully raised funding, what factors distinguished their winning pitches and presentations that others can learn from and apply?
  • Do VCs and accelerators really need the insights and advice of music industry professionals or are the numbers, projects and ROIs only what really matters in deciding whether or not to provide support?
  • Would the application of Moneyball principles be useful to VCs and accelerators in their decision-making processes?

 


1.  See the category Virtual and Augmented Reality for other Subway Fold posts on a range of applications of these technologies.

2.  For a report on a recent developments, see A New AI Can Write Music as Well as a Human Composer, by Bartu Kaleagasi, posted on Futurism.com on 3/9/17.

3.  Other examples of VDAs include Apple’s Siri, Google’s Assistant and Microsoft’s Cortana.

I Can See for Miles: Using Augmented Reality to Analyze Business Data Sets

matrix-1013612__340, Image from Pixabay

While one of The Who’s first hit singles, I Can See for Miles, was most certainly not about data visualization, it still might – – on a bit of a stretch – – find a fitting a new context in describing one of the latest dazzling new technologies in the opening stanza’s declaration “there’s magic in my eye”.  In determining Who’s who and what’s what about all this, let’s have a look at report on a new tool enabling data scientists to indeed “see for miles and miles” in an exciting new manner.

This innovative approach was recently the subject of a fascinating article by an augmented reality (AR) designer named Benjamin Resnick about his team’s work at IBM on a project called Immersive Insights, entitled Visualizing High Dimensional Data In Augmented Reality, posted on July 3, 2017 on Medium.com. (Also embedded is a very cool video of a demo of this system.) They are applying AR’s rapidly advancing technology1 to display, interpret and leverage insights gained from business data. I highly recommend reading this in its entirety. I will summarize and annotate it here and then pose a few real-world questions of my own.

Immersive Insights into Where the Data-Points Point

As Resnick foresees such a system in several years, a user will start his or her workday by donning their AR glasses and viewing a “sea of gently glowing, colored orbs”, each of which visually displays their business’s big data sets2. The user will be able to “reach out select that data” which, in turn, will generate additional details on a nearby monitor. Thus, the user can efficiently track their data in an “aesthetically pleasing” and practical display.

The project team’s key objective is to provide a means to visualize and sum up the key “relationships in the data”. In the short-term, the team is aiming Immersive Insights towards data scientists who are facile coders, enabling them to visualize, using AR’s capabilities upon time series, geographical and networked data. For their long-term goals, they are planning to expand the range of Immersive Insight’s applicability to the work of business analysts.

For example, Instacart, a same-day food delivery service, maintains an open source data set on food purchases (accessible here). Every consumer represents a data-point wherein they can be expressed as a “list of purchased products” from among 50,000 possible items.

How can this sizable pool of data be better understood and the deeper relationships within it be extracted and understood? Traditionally, data scientists create a “matrix of 2D scatter plots” in their efforts to intuit connections in the information’s attributes. However, for those sets with many attributes, this methodology does not scale well.

Consequently, Resnick’s team has been using their own new approach to:

  • Lower complex data to just three dimensions in order to sum up key relationships
  • Visualize the data by applying their Immersive Insights application, and
  • Iteratively label and color-code the data” in conjunction with an “evolving understanding” of its inner workings

Their results have enable them to “validate hypotheses more quickly” and establish a sense about the relationships within the data sets. As well, their system was built to permit users to employ a number of versatile data analysis programming languages.

The types of data sets being used here are likewise deployed in training machine learning systems3. As a result, the potential exists for these three technologies to become complementary and mutually supportive in identifying and understanding relationships within the data as well as deriving any “black box predictive models”.

Analyzing the Instacart Data Set: Food for Thought

Passing over the more technical details provided on the creation of team’s demo in the video (linked above), and next turning to the results of the visualizations, their findings included:

  • A great deal of the variance in Instacart’s customers’ “purchasing patterns” was between those who bought “premium items” and those who chose less expensive “versions of similar items”. In turn, this difference has “meaningful implications” in the company’s “marketing, promotion and recommendation strategies”.
  • Among all food categories, produce was clearly the leader. Nearly all customers buy it.
  • When the users were categorized by the “most common department” they patronized, they were “not linearly separable”. This is, in terms of purchasing patterns, this “categorization” missed most of the variance in the system’s three main components (described above).

Resnick concludes that the three cornerstone technologies of Immersive Insights – – big data, augmented reality and machine learning – – are individually and in complementary combinations “disruptive” and, as such, will affect the “future of business and society”.

Questions

  • Can this system be used on a real-time basis? Can it be configured to handle changing data sets in volatile business markets where there are significant changes within short time periods that may affect time-sensitive decisions?
  • Would web metrics be a worthwhile application, perhaps as an add-on module to a service such as Google Analytics?
  • Is Immersive Insights limited only to business data or can it be adapted to less commercial or non-profit ventures to gain insights into processes that might affect high-level decision-making?
  • Is this system extensible enough so that it will likely end up finding unintended and productive uses that its designers and engineers never could have anticipated? For example, might it be helpful to juries in cases involving technically or financially complex matters such as intellectual property or antitrust?

 


1.  See the Subway Fold category Virtual and Augmented Reality for other posts on emerging AR and VR applications.

2.  See the Subway Fold category of Big Data and Analytics for other posts covering a range of applications in this field.

3.  See the Subway Fold category of Smart Systems for other posts on developments in artificial intelligence, machine learning and expert systems.

4.  For a highly informative and insightful examination of this phenomenon where data scientists on occasion are not exactly sure about how AI and machine learning systems produce their results, I suggest a click-through and reading of The Dark Secret at the Heart of AI,  by Will Knight, which was published in the May/June 2017 issue of MIT Technology Review.