Watson, is That You? Yes, and I’ve Just Demo-ed My Analytics Skills at IBM’s New York Office

IMAG0082

My photo of the entrance to IBM’s office at 590 Madison Avenue in New York, taken on July 29, 2015.

I don’t know if my heart can take this much excitement. Yesterday morning, on July 29, 2015, I attended a very compelling presentation and demo of IBM’s Watson technology. (This AI-driven platform has been previously covered in these five Subway Fold posts.) Just the night before, I saw I saw a demo of some ultra-cool new augmented reality systems.

These experiences combined to make me think of the evocative line from Supernaut by Black Sabbath with Ozzie belting out “I’ve seen the future and I’ve left it behind”. (Incidentally, this prehistoric metal classic also has, IMHO, one of the most infectious guitar riffs with near warp speed shredding ever recorded.)

Yesterday’s demo of Watson Analytics, one key component among several on the platform, was held at IBM’s office in the heart of midtown Manhattan at 590 Madison Avenue and 57th Street. The company very graciously put this on for free. All three IBM employees who spoke were outstanding in their mastery of the technology, enthusiasm for its capabilities, and informative Q&A interactions with the audience. Massive kudos to everyone involved at the company in making this happen. Thanks, too, for all of attendees who asked such excellent questions.

Here is my summary of the event:

Part 1: What is Watson Analytics?

The first two speakers began with a fundamental truth about all organizations today: They have significant quantities of data that are driving all operations. However, a bottleneck often occurs when business users understand this but do not have the technical skills to fully leverage it while, correspondingly, IT workers do not always understand the business context of the data. As a result, business users have avenues they can explore but not the best or most timely means to do so.

This is where Watson can be introduced because it can make these business users self-sufficient with an accessible, extensible and easier to use analytics platform. It is, as one the speakers said “self-service analytics in the cloud”. Thus, Watson’s constituents can be seen as follows:

  • “What” is how to discover and define business problems.
  • “Why” is to understand the existence and nature of these problems.
  • “How” is to share this process in order to affect change.

However, Watson is specifically not intended to be a replacement for IT in any way.

Also, one of Watson’s key capabilities is enabling users to pursue their questions by using a natural language dialog. This involves querying Watson with questions posed in ordinary spoken terms.

Part 2: A Real World Demo Using Airline Customer Data

Taken directly from the world of commerce, the IBM speakers presented a demo of Watson Analytics’ capabilities by using a hypothetical situation in the airline industry. This involved a business analyst in the marketing department for an airline who was given a compilation of market data prepared by a third-party vendor. The business analyst was then assigned by his manager with researching and planning how to reduce customer churn.

Next, by enlisting Watson Analytics for this project, the two central issues became how the data could be:

  • Better understand, leveraged and applied to increase customers’ positive opinions while simultaneously decreasing the defections to the airline’s competitors.
  • Comprehensively modeled in order to understand the elements of the customer base’s satisfaction, or lack thereof, with the airline’s services.

The speakers then put Watson Analytics through its paces up on large screens for the audience to observe and ask questions. The goal of this was to demonstrate how the business analyst could query Watson Analytics and, in turn, the system would provide alternative paths to explore the data in search of viable solutions.

Included among the variables that were dexterously tested and spun into enlightening interactive visualizations were:

  • Satisfaction levels by other peer airlines and the hypothetical Watson customer airline
  • Why customers are, and are not, satisfied with their travel experience
  • Airline “status” segments such as “platinum” level flyers who pay a premium for additional select services
  • Types of travel including for business and vacation
  • Other customer demographic points

This results of this exercise as they appeared onscreen showed how Watson could, with its unique architecture and tool set:

  • Generate “guided suggestions” using natural language dialogs
  • Identify and test all manner of connections among the population of data
  • Use predictive analytics to make business forecasts¹
  • Calculate a “data quality score” to assess the quality of the data upon which business decisions are based
  • Map out a wide variety of data dashboards and reports to view and continually test the data in an effort to “tell a story”
  • Integrate an extensible set of analytical and graphics tools to sift through large data sets from relevant Twitter streams²

Part 3: The Development Roadmap

The third and final IBM speaker outlined the following paths for Watson Analytics that are currently in beta stage development:

  • User engagement developers are working on an updated visual engine, increased connectivity and capabilities for mobile devices, and social media commentary.
  • Collaboration developers are working on accommodating work groups and administrators, and dashboards that can be filtered and distributed.
  • Data connector developers are working on new data linkages, improving the quality and shape of connections, and increasing the degrees of confidence in predictions. For example, a connection to weather data is underway that would be very helpful to the airline (among other industries), in the above hypothetical.
  • New analytics developers are working on new functionality for business forecasting, time series analyses, optimization, and social media analytics.

Everyone in the audience, judging by the numerous informal conversations that quickly formed in the follow-up networking session, left with much to consider about the potential applications of this technology.


1.  Please see these six Subway Fold posts covering predictive analytics in other markets.

2.  Please see these ten Subway Fold posts for a variety of other applications of Twitter analytics.

 

Google is Giving Away Certain Patents to 50 Startups In Their Latest Effort to Thwart Patent Trolls

"She Runs Neon Fraction of a Second", Image by Wonderlane

“She Runs Neon Fraction of a Second”, Image by Wonderlane

In the highly competitive world of creating, monetizing, defending and challenging tech-based intellectual property, “free” is neither a word often heard nor an offer frequently made.

However, Google has just begun a new program, for a limited time, to give away a certain types of patents they own to an initial group of 50 startups.  This is principally being done in an effort to resist time and resources devouring litigation with “patent trolls“, companies that purchase patents for no other purpose than to litigate infringement claims in their attempts to win monetary judgments. (We first visited this issue in an April 21, 2015 Subway Fold post entitled New Analytics Process Uses Patent Data to Predict Advancements in Specific Technologies.)

The details of this initiative were carried in a most interesting new article on TechCrunch.com posted on July 23, 2015 entitled Google Offers To Give Away Patents To Startups In Its Push Against Patent Trolls by Ingrid Lunden. I will summarize, annotate, and pose some free questions of my own.

In April 2015, Google successfully started a temporary program for companies to offer to sell them (Google) their patents. Then on July 23, 2015, they launched a reciprocal program to give away, at no cost, “non-organic” patents (that is, those purchased by Google from third parties), to startups.

The recipients of these giveaways are required to abide by two primary conditions:

  • They must join the LOT Network for two years.  This is a tech industry association of patent owners dedicated to reducing the volume of patent troll-driven litigation.
  • The patents can only be used defensively to “protect a company against another patent suit”. Thus, the patents cannot be used to bring a case “against another company” or else its ownership “reverts back to Google”.

Kurt Brasch, one of Google’s senior patent licensing managers who was interviewed for the TechCrunch story, expects other members of the LOT Network to start their own similar programs.

For any of the 50 startups to be eligible for Google’s program, another key requirement is that their 2014 revenues must fall between $500,000 and $20 million. Next, if eligibility is determined, within 30 days they will receive “a list of three to five families of patents”, from which they can make their selection. Still, Google “will retain a broad, nonexclusive license to all divested assets”, as these patents might still be important to the company.

For those startups that apply and are determined to be ineligible, Google will nonetheless provide them with access “to its own database of patents”. These are presumed to alas be categorized as “non-organic”. The unselected startups will be able to ask Google to consider “the potential purchase of any such assets”.

Back in April, when Google began their acquisitions of patents, they were approached by many operating companies and patent brokers. Both types of entities told Mr. Brasch about a “problem in the secondary market“. These businesses were looking for an alternative means to sell their patents to Google and Mr. Brasch was seeking a means to assist interested buyers and sellers.

Google eventually purchased 28% of the patents they were offered that the company felt could potentially be used in their own operations. As these patents were added to Google’s patent portfolio, a portion of them were categorized as “non-organic” and, as such, the company is now seeking to give them away.

Both sides of Google’s latest patent initiative demonstrate two important strategic points as the company is now:

  • Taking more action in enabling other tech firms to provide assistance against litigation brought by troll-driven lawsuits.
  • Presenting the company as a comprehensive “broker and portal” for patents matters.

Last week, as another part of this process, Google substantially upgraded the features on its Google Patents Search. This included the addition of search results from both Google Scholar and Google Prior Art Finder.  (For the full details and insights on the changes to these services see Google’s New, Simplified Patent Search Now Integrates Prior Art And Google Scholar, also by Ingrid Lunden, posted on TechCrunch.com on July 16, 2015.)

While both the purchasing and selling operations of Google’s effort to test new approaches to the dynamics of the patent marketplace appear to be limited, they might become more permanent later on depending on the  results achieved. Mr. Brasch also anticipates continuing development of this patent market going forward either from his company or a larger “group of organizations”.  Just as Google has moved into other commercial sector including, among others, “shopping, travel and media”, so too does he expect the appearance of more new and comparable marketplaces.

My own questions are as follows:

  • In addition to opposing patent troll litigation, what other policy, public relations, technical and economic benefits does Google get from their new testbed of marketplace services?
  • What other industries would benefit from Google’s new marketplace? What about pharmaceuticals and medical devices, materials science (see these four recent Subway Fold posts in this category),  and/or automotive and aerospace?
  • Should Google limit this project only to startups? Would they consider a more expansive multi-tiered approach to include different ranges of yearly revenue? If so, how might the selection of patents to be offered and other eligibility requirements be decided?
  • Might there be some instances where Google and perhaps other companies would consider giving away non-organic patents to the public domain and allowing further implementation and development of them to operate on an open source basis? (These 10 Subway Fold posts have variously touched upon open source projects and methods.)

Prints Charming: A New App Combines Music With 3D Printing

"Totem", Image by Brooke Novak

“Totem”, Image by Brooke Novak

What does a song actually look like in 3D? Everyone knows that music has always been evocative of all kinds of people, memories, emotions and sensations. In a Subway Fold post back on November 30, 2014, we first looked at Music Visualizations and Visualizations About Music. But can a representation of a tune now be taken further and transformed into a tangible object?

Yes, and it looks pretty darn cool. A fascinating article was posted on Wired.com on July 15, 2015, entitled What Songs Look Like as 3-D Printed Sculptures by Liz Stinson, about a new Kickstarter campaign to raise funding for the NYC startup called Reify working on this. I will sum up, annotate and try to sculpt a few questions of my own.

Reify’s technology uses sound waves in conjunction with 3D printing¹ to shape a physical “totem” or object of it. (The Wired article and the Reify website contain pictures of samples.) Then an augmented reality² app in a mobile device will provide an on-screen visual experience accompanying the song when the camera is pointed towards it. This page on their website contains a video of a demo of their system.

The firm is led by Allison Wood and Kei Gowda. Ms. Wood founded it in order to study “digital synesthesia”. (Synthesia is a rare condition where people can use multiple senses in unusual combinations to, for example, “hear” colors, and was previously covered in the Subway Fold post about music visualization linked to above.) She began to explore how to “translate music’s ephemeral nature” into a genuine object and came up with the concept of using a totem.

Designing each totem is an individualized process. It starts with analyzing a song’s “structure, rhythm, amplitude, and more” by playing it through the Echo Nest API.³ In turn, the results generated correspond to measurements including “height, weight and mass”. The tempo and genre of a song also have a direct influence on the shaping of the totem. As well, the musical artists themselves have significant input into the final form.

The mobile app comes into play when it is used to “read” the totem and interpret its form “like a stylus on a record player or a laser on a CD”. The result is, while the music playing, the augmented reality component of the app captures and then generates an animated visualization incorporating the totem on-screen.  The process is vividly shown in the demo video linked above.

Reify’s work can also be likened to a form of information design in the form of data visualization4. According to Ms. Wood, the process involves “translating data from one form into another”.

My questions are as follows:

  • Is Reify working with, or considering working with, Microsoft on its pending HoloLens augmented reality system and/or companies such as Oculus, Samsung and Google on their virtual reality platforms as covered in the posts linked to in Footnote 2 below?
  • How might Reify’s system be integrated into the marketing strategies of musicians? For example, perhaps printing up a number of totems for a band and then distributing them at concerts.
  • Would long-established musicians and performers possibly use Reify to create totems of some their classics? For instance, what might a totem and augmented reality visualization for Springsteen’s anthem, Born to Run, look like?

1.  See these two Subway Fold posts mentioning 3D printing.

2.  See these eight Subway Fold posts covering some of the latest developments in virtual and augmented reality.

3API’s in a medical and scientific context were covered in a July 2, 2015 Subway Fold Post entitled The Need for Specialized Application Programming Interfaces for Human Genomics R&D Initiatives.

4.  This topic is covered extensively in dozens of Subway Fold posts in the Big Data and Analytics and Visualization categories.

Twitter and Facebook are Rapidly Rising Across All Major US Demographic Groups as Primary News Platforms

"Media in Central Park New York City", Image by Ernst Moeksis

“Media in Central Park New York City”, Image by Ernst Moeksis

Cutting across five fundamental demographic segments, Twitter and Facebook are now the primary sources for news among the US population. This was the central finding of a new report issued on July 14, 2015 by the Pew Research Center for Journalism and Media entitled News Use on Facebook and Twitter Is on the Rise by Michael Barthel, Elisa Shearer, Jeffrey Gottfried and Amy Mitchell. The full text and supporting graphics appear in an 18-page PDF file on the Pew website is entitled The Evolving Role of News on Twitter and Facebook. I highly recommended clicking through to read the full report.

A number of concise summaries of it quickly appeared online. I found the one written by Joseph Lichtman on NeimanLab.com (a site about Internet journalism at Harvard University), entitled New Pew Data: More Americans are Getting News on Facebook and Twitter, also on July 14th to be an informative briefing on it. I will, well, try to sum up this summary, add some annotations and pose some questions.

First, for some initial perspective, on January 21, 2015, a Subway Fold Post entitled  The Transformation of News Distribution by Social Media Platforms in 2015, examined how the nature of news media was being dramatically impacted by social media. This new Pew Research Institute report focuses on the changing demographics of Facebook and Twitter users for news consumption.

This new study found that 63% of both Twitter and Facebook users are now getting their news from these leading social media platforms. As compared to a similar Pew survey in 2013, this is a 52% increase for Twitter and a 47% increase for Facebook. Of those following a live news event as it occurs, the split is more pronounced as 59% of Twitter users and 31% of Facebook users are engaged in viewing such coverage.

According to Amy Mitchell, one of the report’s authors and Pew’s Director of Journalism Research, each social media site “adapt to their role” and provide “unique features”. As well, they ways in which US users connect in different ways “have implications” for how they “learn about their world” and partake in their democracy.

In order enhance their growing commitment to live coverage, both sites have recently rolled out innovative new services. Twitter has a full-featured multimedia app called Project Lightening to facilitate following news in real-time. Facebook is likewise expanding its news operations with their recent announced of the launch of Instant Articles, a rapid news co-publishing app in cooperation with nine of the world’s leading news organizations.

Further parsing the survey’s demographic data for US adults generated the following findings:

  • Sources of News: 10% get their news on Twitter while 41% get their news on Facebook, with an overlap of 8% using both. This is also due to the fact that Facebook has a much larger user base than Twitter. Furthermore, while the total US user bases of both platforms currently remains steady, the percentages of those users therein seeking news on both is itself increasing.
  • Comparative Trends in Five Key Demographics: The very enlightening chart at the bottom of Page 2 of the report breaks down Twitter’s and Facebook’s percentages and percentage increases between 2013 and 2015 for gender, race, age, education level, and incomes.
  • Relative Importance of Platforms: These results are further qualified in that those surveyed reported that Americans still see both of these platforms overall as “secondary news sources” and “not a very important way” to stay current.
  • Age Groups: When age levels were added, this changes to nearly 50% of those between 18 and 35 years finding Twitter and Facebook to be “the most important” sources of news. Moving on to those over 35 years, the numbers declined to 34% of Facebook users and 31% of Twitter users responding that these platforms were among the “most important” news sources.
  • Content Types Sought and Engaged: Facebook users were more likely to click on political content than Twitter users to the extent of 32% to 25%, respectively. The revealing charts in the middle of Page 3 demonstrate that Twitter users see and pursue a wider variety of 11 key news topics. As well, the percentage tallies of gender differences by topic and by platform are also presented.

My own questions are as follows:

  • Might Twitter and Facebook benefit from additional cooperative ventures to further expand their comprehensiveness, target demographics, and enhanced data analytics for news categories by exploring additional projects with other organizations. For instance, and among many other possibilities, there are Dataminr who track and parse the entirety of the Twitterverse in real-time (as previously covered in these three Subway Fold posts); Quid who is tracking massive amount of online news (as previously covered in this Subway Fold post); and GDELT which is translating online news in real-time in 65 languages (as previously covered in this Subway Fold post).
  • What additional demographic categories would be helpful in future studies by Pew and other researchers as this market and its supporting technologies, particularly in an increasingly social and mobile web world, continue to evolve so quickly? For example, how might different online access speeds affect the distribution and audience segmentation of news distributed on social platforms?
  • Are these news consumption demographics limited only to Twitter and Facebook? For example, LinkedIn has gone to great lengths in the past few years to upgrade its content offerings. How might the results have differed if the Pew questionnaire had included LinkedIn and possibly others like Instagram?
  • How can this Pew study be used to improve the effectiveness of marketing and business development for news organizations for their sponsors, content strategist for their clients, and internal and external SEO professionals for their organizations?

On Location Filming of a New Movie About the Kitty Genovese Case

See the end of this post below for two updates on the Kitty Genovese Case on April 5, 2016 and May 31, 2016.


In May 1964 there was an absolutely horrific murder of a young woman in Queens, New York, named Kitty Genovese. Late at night as she was returning home from work, an attacker viciously stabbed her twice and then returned about ten minutes later to brutalize her again and murder her.

Winston Moseley was later arrested, tried and convicted for this crime. He remains in jail to this day.

At first, the crime did not receive that much attention in the press. Several weeks later, a metro writer for The New York Times reported that as many as 37 witnesses in the surrounding apartment had seen the crime and heard the victim’s cries for help but did nothing to assist or protect her. The Kitty Genovese Case as it came to be known (the link is to a concise summary on Wikipedia of the facts, history and lasting impact), turned into a decades long shameful story and commentary about the indifference of the neighbors who were alleged to have not acted when someone’s life was at stake. Over the many years since this crime, it was also often cited as a symbol for the callousness of New Yorkers.

In 2007, a new study carefully re-examined the records and evidence, concluding much of the case’s legacy was wrong. Last year in 2014, on the 50th anniversary of the crime, two books were published and critically well-received about how the reporting was incorrect and how the public’s outrage over her neighbors’ behavior had tragically taken root in US culture. The two books are Kitty Genovese: A True Account of a Public Murder and Its Private Consequences by Catherine Pelonero, published by Skyhorse Publishing, and Kitty Genovese: The Murder, the Bystanders, the Crime that Changed America, by Kevin Cook published by W.W. Norton & Company. On March 14, 2014, Mr. Cook was interviewed on New York radio station WNYC about the case in a podcast entitled What Really Happened on the Night Kitty Genovese Was Murdered?

A movie is being currently made about this terrible crime and its aftermath in an attempt to revise the misconceptions surrounding it. During a walk yesterday morning, I happened upon movie company filming this famous story on location and took a series of pictures wile they were filming. The working title of the film listed on the local notices about the filming  posted on the street signs is “37”. I am not certain whether this will be for a theatrical, television or web release.

Nonetheless, below are five of the pictures I took as I walked down the street. They are sequenced in the order I took them.

Moving further down the block, the filming is going on in front the large white screen in the background. Notice all of the cars are from the 50's and 60's.

The actual filming is going on in front the large white screen in the background. Notice all of the cars are from the 50’s and 60’s.

This was taken directly across the street from the filming of a scene in front of a private house.

This was taken directly across the street from the filming of a scene in front of a private house.

Zooming in closer here. The actors where placed in front of the row of trees to the right.

Zooming in a bit closer, the actors and the director where working in front of the row of trees to the right.

IMAG0075

Another view of the filming in progress taken a bit further to the right.

A better view of that beautiful old purple Plymouth.

A better view of that beautiful old purple Plymouth.

 I am looking forward to seeing this film when it is finally completed and released.


April 5, 2016 Update:

Today’s edition of The New York Times carries an article entitled Winston Moseley, Who Killed Kitty Genovese, Dies in Prison at 81, by Robert D. McFadden. This report concisely covers the original crime, trial, Winston’s 52 years in jail, and the very inaccurate reporting in 1964 and its decades-long consequences afterwards. A postscript well worth reading if you have an opportunity to this terribly tragic story.

May 31, 2016 Update:

For a very different and poignant perspective on the Kitty Genovese case, today’s (May 31, 2016) edition of The Wall Street Journal carries a feature about the lifelong work and personal sacrifices made by Ms. Genovese’s brother, Bill, in getting to the truth of what really happened to his sister and making a new documentary film about it entitled The Witness. I very highly recommend reading this article entitled Kitty Genovese’s Brother Re-Examines Her 1964 Murder in Documentary Film, by Steve Dollar.

Eight Proven Factors to Help Make Your Web Content Go Viral

"M31. The Andromeda Galaxy", Image by Adam Evans

“M31. The Andromeda Galaxy”, Image by Adam Evans

On a daily basis, we see news, commentary, videos, photos, tweets, blog posts, podcasts, articles, rumors and memes go viral where they spread rapidly across the web like a propulsive digital wave. From YouTube postings of dogs and cats doing goofy things to in-the-moment hashtags and tweets about late-breaking current events, attention grabbing content now spreads at nearly the speed of light.

All content creators, strategists and distributors want to know how to infuse their offerings with this elusive clickable contagion. Providing eight very useful and scientifically proven elements to, at the very least, increase the probability of new content going viral, is a new article entitled The Science Behind What Content Goes Viral, by Sarah Snow, posted on SocialMediaToday.com on July 6, 2015. I will sum up, annotate, and pose some not entirely scientific questions of my own.

For further reading I also highly recommend clicking through and reading The Secret to Online Success: What Makes Content Go Viral, by Liz Rees-Jones, Katherine L. Milkman and Jonah Berger (the second and third of whom are professors at the University of Pennsylvania – – the “U of P”), posted on ScientificAmerican.com (“SciAm”) on April 14, 2015. Two fully detailed and fascinating reports by Milkman and Berger that underlie their SciAm article are available here and here. Ms. Snow’s article cites many of the findings in the SciAm piece. As well, I suggest checking out a May 22, 2015 blog post by Peter Gasca entitled The 4 Essentials of the Most Read Content posted on Entrepreneur.com for some additionally effective content strategies, not to mention a hilarious picture of a dog wearing glasses.

Ms. Snow organized her article into a series of eight individual hypotheses about online virality that she then proceeds to provide references to support them. I will put each of these in bold and quotes below as she stated them in her text. (My own highlights in orange are explained afterwards.)

  • Long, in-depth posts tend to go viral more than short ones.”: Drawing from the findings of Milkman’s and Berger’s studies that, among other things, examined the data from the feature on the home page of the NYTimes.com called Most Emailed, longer articles had a higher tendency to be shared. As also stated by Carson Ward of the search engine optimization (SEO) consulting firm called Moz, of all possible variables, word count most closely correlate with the breadth of online sharing. Further, he believes this is a directly causal relationship. (The distinctions between correlation and causation have been previously raised in other various contexts in these six Subway Fold posts.) See also, Mr. Ward’s practical and informative January 14, 2013 posting on Moz’s site entitled Why Content Goes Viral: the Theory and Proof.
  • Inspire anger, awe, or anxiety and your post will go viral.”: Evidence shows that “high energy emotions” such as awe and anger, as opposed to “law energy emotions”, are more likely to spur virality.  Among them, anger is the most effective, but it must be, well, tempered without insulting the audience. It is best for content authors to write about something that angers them, which, in return, require “some tolerance” by their readers. In terms of usage data, blog content which engages controversial topics generates twice as many comments in response. Alternatively, awe is a better emotion for those who wish to avoid controversy and instead focuses on the positive effects of brands and heroic acts.
  • Showing a little vulnerability or emotion helps content go viral.”: This is indeed true again according to the U of P studies. Readers respond to emotional content because they “want to feel things when they read”. The author Walter Kirn is quoted recommending that writers should begin with what they feel “most shameful about”. This is where conflict resides and writing about it makes you vulnerable to your readers. For other content creators, rather than shame, writers can start with some other genuine “human emotion”.
  • “Viral content is practically useful, surprising, and interesting.”: Clearly, engaging and practical content beats boring and dull any day of the week. Content that is useful generates the highest levels of online sharing. For example, posting pragmatic suggestions and solutions to “how-to” questions is going to draw many more clicks.
  • “Content written by known authors is more likely to go viral.”: Milkman’s and Berger’s reports further showed that being a known writer had a significant impact on the sharing of a news article. Name recognition translates into credibility and trust.
  • “Content written by women is more likely to go viral.”: The U of P professors also reported that on NYTimes.com, the gender of a writer had an effect insofar as the data showed that articles by female authors had a tendency to be shared more that stories by male authors. 
  • “Posts that spend a lot of time on the home page are more likely to go viral.”: Yes, insofar as the NYTimes.com goes. (The article does not mention whether other sites have been tested or are planning to be tested for this variable.)
  • “Content that is truly and broadly viral is almost always funny“: This quote about humor from Ward’s post (linked above in the first factor about blog post length), is helpful for content authors as it gives all of them an opportunity to be funny. This is particularly so in efforts to make online ads go viral.

I propose the following mnemonic to assist in remembering all of these variables tracking with the key words highlighted above in orange:

Writer + Emotion – – give- – Useful – – content – – Funny + Long + Inspiration + Gender + Homepage Time

That is, WE give U content FLIGHT!

My own questions are as follows:

  • Which of these factors will more likely endure, expand or disappear, especially now that a majority of users access the web on mobile devices? What new factors that have not yet emerged might soon affect the rate(s) of content virality?
  • Is going viral purely an objective and quantifiable matter of the numbers of clicks and visitors, or are there some more qualitative factors involved? For instance, might marketing specialists and content strategists be more interested in reaching a significant percentage of traffic among a particular demographic group or market segment and just attaining X clicks and Y visitors regardless of whether or not they involve identifiable cohorts?
  • Do the above eight factors lend themselves to be transposed into an algorithm? Assuming this is possible, how would it be applied to optimize viral content and, in turn, overall SEO strategic planning?
  • Beside the length of content discussed as the first factor above, how do the other seven factors lend themselves to being evaluated for degrees of correlation and causation of viral results?

The Successful Collaboration of Intuit’s In-House Legal Team and Data Scientists

"Data Represented in an Interactive 3D Form", Image by Idaho National Laboratory

“Data Represented in an Interactive 3D Form”, Image by Idaho National Laboratory

Intuit’s in-house legal team has recently undertaken a significant and successful collaborative effort with the company’s data scientists. While this initiative got off to an uneasy start, this joining (and perhaps somewhat of a joinder, too), of two seemingly disparate departments has gone on to produce some very positive results.

Bill Loconzolo, the Intuit’s VP of Data Engineering and Analytics, and Laura Fennel, the Chief Counsel and Head of the Legal, Data, Compliance and Policy, tell this instructive story and provide four highly valuable object lessons in an article entitled Data Scientists and Lawyers: A Marriage Made in Silicon Valley, posted on July 2, 2015 on VentureBeat.com. I will sum up, annotate, and pose a few questions of my own requiring neither a law degree nor advanced R programming skills to be considered.

Mr. Loconzolo and Ms. Fennel initially recognized there might be differences between their company’s data scientists and the in-house Legal Department because the former are dedicated to innovation with “sensitive customer data”, while the latter are largely risk averse. Nonetheless, when these fundamentally different mindsets were placed into a situation where they were “forced to collaborate”, this enabled the potential for both groups to grow.¹

Under the best of circumstances, they sought to assemble “dynamic teams that drive results” that they could not have achieved on their own. They proceeded to do this in the expectation that the results would generate “a much smarter use of big data”. This turned out to be remarkably true for the company.

Currently, the Data Engineering and Analytics group reports to the Legal Department. At first, the data group wanted to move quickly in order to leverage the company’s data from a base of 50 million customers. At the same time, the Legal Department was concerned because of this data’s high sensitivity and potential for damage through possible “mistake or misuse”. ² Both groups wanted to reconcile this situation where the data could be put to its most productive uses while simultaneously ensuring that it would be adequately protected.

Despite outside skepticism, this new arrangement eventually succeeded and the two teams “grew together to become one”. The four key lessons that Mr. Loconzolo and Ms. Fennel learned and share in their article for teaming up corporate “odd couples” include:

  • “Shared Outcome”:  A shared vision of success held both groups together. As well, a series of Data Stewardship Principles were written for both groups to abide. Chief among them was that the data belonged to the customers.
  • “Shared Accountability”:  The entire integrated team, Legal plus Data, were jointly and equally responsible for their outcomes, including successes and failures, of their work. This resulted in “barriers” being removed and “conflict” being transformed into “teamwork”.
  • “Healthy Tension Builds Trust”: While both groups did not always agree, trust between them was established so that all perspectives “could be heard” and goals were common to everyone.
  • “A Learning Curve”: Both groups have learned much from each other that has improved their work. The legal team is now using the data team’s “rapid experimentation innovation techniques” while the data team has accepted “a more rigorous partnership mindset” regarding continually learning from others.

The authors believe that bringing together such different groups can be made to work and, once established, “the possibilities are endless”.

I say bravo to both of them for succeeding in their efforts, and generously and eloquently sharing their wisdom and insights online.

My own questions are as follows:

  • What are the differences in lawyers’ concerns and the data scientists’ concerns about the distinctions between correlation and causation in their conclusions and actions? (Similar issues have been previously raised in these six Subway Fold posts.)
  • Is the Legal Department collecting and analyzing its own operation big data? If so, for what overall purposes? Are the data scientists correspondingly seeing new points of view, analytical methods and insights that are possibly helpful to their own projects?
  • What metrics and benchmarks are used by each department jointly and separately to evaluate the successes and failures of their collaboration with each other? Similarly, what, if any, considerations of their collaboration are used in the annual employee review process?

1.  Big data in law practice has been covered from a variety of perspectives in many of the 20 previous Subway Fold posts in the Law Practice and Legal Education category.)

2.  For the very latest comprehensive report on data collection and consumers, see Americans’ Views About Data Collection and Security, by Mary Madden and Lee Rainie, published May 20, 2015, by the Pew Research Center for Internet Science and Tech.