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.

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.

Pushing the Envelopes: New US Postal Service Report Assesses Possible Blockchain Applications

"Vibrant US Air Mail Stamp", Image by Nicolas Raymond

“Vibrant US Air Mail Stamp”, Image by Nicolas Raymond

Way before the advent of email, when people exclusively wrote letters on paper and mailed them to each other (yes, this really did happen once upon a time), there was a long-running scam known as the “chain letter“. Recipients who received such a letter were asked, often through manipulative language, to copy it and send it on to as many other people as possible. In effect, these were structured as fraudulent pyramid schemes that ultimately would collapse in on themselves.

Sometimes chain letters involved illegal financial dealings and other hoaxes, also producing unwanted emotional effects on who mistakenly fell for them. Variations of the chain letter still survive today online and operate using email, texting and social media.

However, an emerging new form of virtual chain, in conjunction with the mail service, might soon appear – – namely using the blockchain – – within the U.S. Postal Service (USPS). However, this combination could potentially produce four very positive improvements in services. These exciting prospects were the subject of a most interesting new post on Quartz.com on May 24, 2016 entitled Even the US Postal Service Wants to Start Using Blockchain Tech, by Ian Kar. I recommend reading this article in its entirety. I will summarize and annotate it, and pose some questions of my own (but without any additional postage due).

While blockchain technology has been getting a great deal of press coverage recently involving innovative new development initiatives in, among other fields, finance, law, government and the arts, this story illustrates how it also might affect something as routine and mundane as mail service with possibly dramatic results. Such changes could produce significant economic and logistical advances that would affect just about anyone who checks their real world mailbox every day.

(These six Subway Fold posts cover just a small sampling of blochchain projects.)

Better Letters

Image from Pixabay

Image from Pixabay

Traditionally, the USPS has never really distinguished itself as a leader in innovation. Rather, it has a long reputation for its inefficient operations. This could possibly be significantly changed by this series of a series of blockchain proposals. Because this technology is decentralized, widely accessible, and secured by encryption, it is highly resistant to tampering.

On May 23, 2016, the USPS Office of the Inspector General and a consulting firm called Swiss Economics, published a new report entitled Blockchain Technology: Possibilities for the U.S. Postal Service. It analyzed the following four possible future implementations:

1.  Financial Services:  US post offices currently offers a limited number of financial services such as international money transfers. The IOG report speculated that the USPS “could benefit from developing its own bitcoin-like digital currency”.  Perhaps it could be called “Postcoin”. This would permit the expansion into other financial services such as a “global payment service” for people without traditional bank accounts.

2.  Identity:  An individual’s identity could be verified for the USPS using a blockchain. Essentially, they already do this when they deliver your mail to you each day. By using a blockchain for this, the USPS could provide you with assistance to help you manage both your online and offline identities “by storing it on an immutable ledger”.

3.  Logistics Support:  Applying the blochchain to support the Internet of Things (IoT) could enhance the USPS logistics management operations. The IGO report imagine a system where “vehicles and sorting equipment could manage their own tracking, monitoring, and maintenance”. This could include items such as autonomously, efficiently and economically monitoring brake pad performance including:

  • Assessing when one will need to be replaced
  • Determining whether its warranty is still in effect
  • Creating a smart contract with a vendor to replace it
  • Paying for the part and its installation

4.  Mail Tracking:  On a daily basis, the USPS delivers 509 million pieces of mail. As stated in the OIG report, the blockchain can be deployed to uniquely identify each piece of it. This could be done with “a small sensor” on each piece in order to use the blockchain to “manage the chain of custody between different USPS partners, like UPS and Fedex”. As well, the blockchain could be put to the additional uses of:

  • Expediting customs clearance
  • Integrating payments
  • Shipping upon one unified platform

[All of these components form the very convenient anagram FILM, thus making it easier to, well, picture.]

For now, the USPS intends to keep studying blockchain technology. The OIG report states that the agency “could benefit from experimenting” with it on new financial products and then eventually progress on toward “more complex uses”.

"Stamped Mail to be Posted", Image by Steven Depolo

“Stamped Mail to be Posted”, Image by Steven Depolo

My Questions

  • Would these blochchain apps have a negative impact on USPS revenues as this massive government agency has been running at a budget deficit for many years? If so, would this have unintended negative consequences for consumers and/or the USPS?
  • Conversely, can the USPS use blockchain innovations to create new sources of revenue and employment? What new sorts of job descriptions and titles might emerge?
  • Would the blockchain do away with the traditional services of certified, registered, priority and insured mail? If so, what forms of proof of delivery or non-delivery could be provided to consumers?
  • Would any of these proposed new apps possibly create new privacy issues for consumers and policy concerns for the US government?
  • What type of opportunities might arise for entrepreneurs to create new mail apps built on the blockchain?

Ethical Issues and Considerations Arising in Big Data Research

Image from Pixabay

Image from Pixabay

In 48 of 50 states in the US, new attorneys are required to pass a 60 multiple-choice question exam on legal ethics in addition to passing their state’s bar exam. This is known as the Multistate Professional Responsibility Examination (MPRE). I well recall taking this test myself.

The subject matter of this test is the professional ethical roles and responsibilities a lawyer must abide by as an advocate and counselor to clients, courts and the legal profession. It is founded upon a series of ethical considerations and disciplinary rules that are strictly enforced by the bars of each state. Violations can potentially lead to a series of professional sanctions and, in severe cases depending upon the facts, disbarment from practice for a term of years or even permanently.

In other professions including, among others, medicine and accounting, similar codes of ethics exist and are expected to be scrupulously followed. They are defined efforts to ensure honesty, quality, transparency and integrity in their industries’ dealings with the public, and to address certain defined breaches. Many professional trade organizations also have formal codes of ethics but often do not have much, if any, sanction authority.

Should some comparable forms of guidelines and boards likewise be put into place to oversee the work of big data researchers? This was the subject of a very compelling article posted on Wired.com on May 20, 2016, entitled Scientists Are Just as Confused About the Ethics of Big-Data Research as You by Sharon Zhang. I highly recommend reading it in its entirety. I will summarize, annotate and add some further context to this, as well as pose a few questions of my own.

Two Recent Data Research Incidents

Last month. an independent researcher released, without permission, the profiles with very personal information of 70,000 users of the online dating site OKCupid. These users were quite angered by this. OKCupid is pursuing a legal claim to remove this data.

Earlier in 2014, researchers at Facebook manipulated items in users’ News Feeds for a study on “mood contagion“.¹ Many users were likewise upset when they found out. The journal that published this study released an “expression of concern”.

Users’ reactions over such incidents can have an effect upon subsequent “ethical boundaries”.

Nonetheless, the researchers involved in both of these cases had “never anticipated” the significant negative responses to their work. The OKCupid study was not scrutinized by any “ethical review process”, while a review board at Cornell had concluded that the Facebook study did not require a full review because the Cornell researchers only had a limited role in it.

Both of these incidents illustrate how “untested the ethics” are of these big data research. Only now are the review boards that oversee the work of these researchers starting to pay attention to emerging ethical concerns. This is in high contrast to the controls and guidelines upon medical research in clinical trials.

The Applicability of The Common Rule and Institutional Research Boards

In the US, under the The Common Rule, which governs ethics for federally funded biomedical and behavioral research where humans are involved, studies are required to undergo an ethical review.  However, such review does not apply a “unified system”, but rather, each university maintains its own institutional review board (IRB). These are composed of other (mostly medical) researchers at each university. Only a few of them “are professional ethicists“.

To a lesser extent, do they have experience in computer technology. This deficit may be affecting the protection of subjects who participate in data science research projects. In the US, there are hundreds of IRBs but they are each dealing with “research efforts in the digital age” in their own ways.

Both the Common Rule and the IRB system came into being following the revelation in the 1970s that the U.S. Public Health Service had, between 1932 and 1972, engaged in a terrible and shameful secret program that came to be known as the Tuskegee Syphilis Experiment. This involved leaving African Americans living in rural Alabama with untreated syphilis in order to study the disease. As a result of this outrage, the US Department of Health and Human Services created new regulations concerning any research on human subjects they conducted. All other federal agencies likewise adopted such regulations. Currently, “any institution that gets federal funding has to set up an IRB to oversee research involving humans”.

However, many social scientists today believe these regulations are not accurate or appropriate for their types of research involving areas where the risks involved “are usually more subtle than life or death”. For example, if you are seeking volunteers to take a survey on test-taking behaviors, the IRB language requirements on physical risks does not fit the needs of the participants in such a study.

Social scientist organizations have expressed their concern about this situation. As a result, the American Association of University Professors (AAUP) has recommended:

  • Adding more social scientists to IRBs, or
  • Creating new and separate review boards to assess social science research

In 2013, AAUP issued a report entitled Regulation of Research on Human Subjects: Academic Freedom and the Institutional Review Board, recommending that the researchers themselves should decide if “their minimal risk work needs IRB approval or not”. In turn, this would make more time available to IRBs for “biomedical research with life-or-death stakes”.

This does not, however, imply that all social science research, including big data studies, are entirely risk-free.

Ethical Issues and Risk Analyses When Data Sources Are Comingled

Dr. Elizabeth A. Buchanan who works as an ethicist at the University of Wisconsin-Stout, believes that the Internet is now entering its “third phase” where researchers can, for example, purchase several years’ worth of Twitter data and then integrate it “with other publicly available data”.² This mixture results in issues involving “ethics and privacy”.

Recently, while serving on an IRB, she took part in evaluated a project proposal involving merging mentions of a drug by its street name appearing on social media with public crime data. As a result, people involved in crimes could potentially become identified. The IRB still gave its approval. According to Dr. Buchanan, the social value of this undertaking must be weighed against its risk. As well, the risk should be minimized by removing any possible “idenifiers” in any public release of this information.

As technology continues to advance, such risk evaluation can become more challenging. For instance, in 2013, MIT researchers found out that they were able to match up “publicly available DNA sequences” by using data about the participants that the “original researchers” had uploaded online.³ Consequently, in such cases, Dr. Buchanan believes it is crucial for IRBs “to have either a data scientist, computer scientist or IT security individual” involved.

Likewise, other types of research organizations such as, among others, open science repositories, could perhaps “pick up the slack” and handle more of these ethical questions. According to Michelle Meyer, a bioethicist at Mount Sinai, oversight must be assumed by someone but the best means is not likely to be an IRB because they do not have the necessary “expertise in de-identification and re-identification techniques”.

Different Perspectives on Big Data Research

A technology researcher at the University of Maryland 4 named Dr. Katie Shilton recently conducted interviews of “20 online data researchers”. She discovered “significant disagreement” among them on matters such as the “ethics of ignoring Terms of Service and obtaining informed consent“. The group also reported that the ethical review boards they dealt with never questioned the ethics of the researchers, while peer reviewers and their professional colleagues had done so.

Professional groups such as the Association of Internet Researchers (AOIR) and the Center for Applied Internet Data Analysis (CAIDA) have created and posted their own guidelines:

However, IRBs who “actually have power” are only now “catching up”.

Beyond universities, tech companies such as Microsoft have begun to establish in-house “ethical review processes”. As well, in December 2015, the Future of Privacy Forum held a gathering called Beyond IRBs to evaluate “processes for ethical review outside of federally funded research”.

In conclusion., companies continually “experiment on us” with data studies. Just to name to name two, among numerous others, they focus on A/B testing 5 of news headings and supermarket checkout lines. As they hire increasing numbers of data scientists from universities’ Ph.D. programs, these schools are sensing an opportunity to close the gap in terms of using “data to contribute to public knowledge”.

My Questions

  • Would the companies, universities and professional organizations who issue and administer ethical guidelines for big data studies be taken more seriously if they had the power to assess and issue public notices for violations? How could this be made binding and what sort of appeals processes might be necessary?
  • At what point should the legal system become involved? When do these matters begin to involve civil and/or criminal investigations and allegations? How would big data research experts be certified for hearings and trials?
  • Should teaching ethics become a mandatory part of curriculum in data science programs at universities? If so, should the instructors only be selected from the technology industry or would it be helpful to invite them from other industries?
  • How should researchers and their employers ideally handle unintended security and privacy breaches as a result of their work? Should they make timely disclosures and treat all inquiries with a high level of transparency?
  • Should researchers experiment with open source methods online to conduct certain IRB functions for more immediate feedback?

 


1.  For a detailed report on this story, see Facebook Tinkers With Users’ Emotions in News Feed Experiment, Stirring Outcry, by Vindu Goel, in the June 29, 2014 edition of The New York Times.

2These ten Subway Fold posts cover a variety of applications in analyzing Twitter usage data.

3.  For coverage on this story see an article published in The New York Times on January 17, 2013, entitled Web Hunt for DNA Sequences Leaves Privacy Compromised, by Gina Kolata.

4.  For another highly interesting but unrelated research initiative at the University of Maryland, see the December 27, 2015 Subway Fold post entitled Virtual Reality Universe-ity: The Immersive “Augmentarium” Lab at the U. of Maryland.

5.  For a detailed report on this methodology, see the September 30, 2015 Subway Fold post entitled Google’s A/B Testing Method is Being Applied to Improve Government Operations.