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.

“Technographics” – A New Approach for B2B Marketers to Profile Their Customers’ Tech Systems

"Gold Rings - Sphere 1" Image by Linda K

“Gold Rings – Sphere 1” Image by Linda K

Today’s marketing and business development professionals use a wide array of big data collection and analytical tools to create and refine sophisticated profiles of market segments and their customer bases. These are deployed in order to systematically and scientifically target and sell their goods and services in steadily changing marketplaces.

These processes can include, among a multitude of other vast data sets and methodologies, demographics, web user metrics and econometrics. Businesses are always looking for a data-driven edge in highly competitive sectors and such profiling, when done correctly, can be very helpful in detecting and interpreting market trends, and consistently keeping ahead of their rivals. (The Subway Fold category of Big Data and Analytics now contains 50 posts about a variety of trends and applications in this field.)

I will briefly to this add my own long-term yet totally unscientific study of office-mess-ographics. Here I have been looking for any correlation between the relative states of organization – – or entropy – – in people’s offices and their work’s quality and output.  The results still remain inconclusive after years of study.

One of the most brilliant and accomplished people I have ever known had an office that resembled a cave deep in the earth with piles of paper resembling stalagmites all over it. Even more remarkably, he could reach into any one of those piles and pull out exactly the documents he wanted. His work space was so chaotic that there was a long-standing joke that Jimmy Hoffa’s and Judge Crater’s long-lost remains would be found whenever ever he retired and his office was cleaned out.

Speaking of office-focused analytics, an article posted on VentureBeat.com on March 5, 2016, entitled CMOs: ‘Technographics’ is the New Demographics, by Sean Zinsmeister, brought news of a most interesting new trend. I highly recommend reading this in its entirety. I will summarize and add some context to it, and then pose a few question-ographics of my own.

New Analytical Tool for B2B Marketers

Marketers are now using a new methodology call technography to analyze their customers’ “tech stack“, a term of art for the composition of their supporting systems and platforms. The objective of this approach is to deeply understand what this says about them as a company and, moreover, how can this be used in business-to-business (B2B) marketing campaigns. Thus applied, technography can identify “pain points” in products and alleviate them for current and prospective customers.

Using established consumer marketing methods, there is much to be learned and leveraged on how technology is being used by very granular segments of users bases.  For example:

By virtue of this type of technographic data, retailers can target their ads in anticipation of “which customers are most likely to shop in store, online, or via mobile”.

Next, by transposing this form of well-established marketing approach next upon B2B commerce, the objective is to carefully examine the tech stacks of current and future customers in order to gain a marketing advantage. That is, to “inform” a business’s strategy and identify potential new roles and needs to be met. These corporate tech stacks can include systems for:

  • Office productivity
  • Project management
  • Customer relationship management (CRM)
  • Marketing

Gathering and Interpreting Technographic Signals and Nuances

Technographics can provide unique and valuable insights into assessing, for example, whether a customer values scalability or ease-of-use more, and then act upon this.

As well, some of these technographic signals can be indicative of other factors not, per se, directly related to technology. This was the case at Eloqua, a financial technology concern. They noticed their marketing systems have predictive value in determining the company’s best prospects. Furthermore, they determined that companies running their software were inclined “to have a certain level of technological sophistication”, and were often large enough to have the capacity to purchase higher-end systems.

As business systems continually grow in their numbers and complexity, interpreting technographic nuances has also become more of a challenge. Hence, the application of artificial intelligence (AI) can be helpful in detecting additional useful patterns and trends. In a July 2011 TED Talk by Ted Slavin, directly on point here, entitled How Algorithms Shape Our World, he discussed how algorithms and machine learning are needed today to help make sense out of the massive and constantly growing amounts of data. (The Subway Fold category of Smart Systems contains 15 posts covering recent development and applications involving AI and machine learning.)

Technographic Resources and Use Cases

Currently, technographic signals are readily available from various data providers including:

They parse data using such factors as “web hosting, analytics, e-commerce, advertising, or content management platforms”. Another firm called Ghostery has a Chrome browser extension illuminating the technologies upon which any company’s website is built.

The next key considerations are to “define technographic profiles and determine next-best actions” for specific potential customers. For instance, an analytics company called Looker creates “highly targeted campaigns” aimed at businesses who use Amazon Web Services (AWS). The greater the number of marketers who undertake similar pursuits, the more they raise the value of their marketing programs.

Technographics can likewise be applied for competitive leverage in the following use cases:

  • Sales reps prospecting for new leads can be supported with more focused messages for potential new customers. These are shaped by understanding their particular motivations and business challenges.
  • Locating opportunities in new markets can be achieved by assessing the tech stacks of prospective customers. Such analytics can further be used for expanding business development and product development. An example is the online training platform by Mindflash. They detected a potential “demand for a Salesforce training program”. Once it became available, they employed technographic signals to pinpoint customers to whom they could present it.
  • Enterprise wide decision-making benefits can be achieved by adding “value in areas like cultural alignment”. Familiarity with such data for current employees and job seekers can aid businesses with understanding the “technology disposition” of their workers. Thereafter, its alignment with the “customers or partners” can be pursued.  Furthermore, identifying areas where additional training might be needed can help to alleviate productivity issues resulting from “technology disconnects between employees”.

Many businesses are not yet using technographic signals to their full advantage. By increasing such initiatives, businesses can acquire a much deeper understanding of their inherent values. In turn, the resulting insights can have a significant effect on the experiences of their customers and, in turn, elevate their resulting levels of loyalty, retention and revenue, as well as the magnitude of deals done.

My Questions

  • Would professional service industries such as law, medicine and accounting, and the vendors selling within these industries, benefit from integrating technographics into their own business development and marketing efforts?
  • Could there be, now or in the future, an emerging role for dedicated technographics specialists, trainers and consultants? Alternatively, should these new analytics just be treated as another new tool to be learned and implemented by marketers in their existing roles?
  • If a company identifies some of their own employees who might benefit from additional training, how can they be incentivized to participate in it? Could gamification techniques also be applied in creating these training programs?
  • What, if any, privacy concerns might surface in using technographics on potential customer leads and/or a company’s own internal staff?

Movie Review of “The Human Face of Big Data”

"Blue and Pink Fractal", Image by dev Moore

“Blue and Pink Fractal”, Image by dev Moore

What does big data look like, anyway?

To try to find out, I was very fortunate to have obtained a pass to see a screening of a most enlightening new documentary called The Human Face of Big Data. The event was held on October 20, 2015 at Civic Hall in the Flatiron District in New York.

The film’s executive producer, Rick Smolan, (@ricksmolan), first made some brief introductory remarks about his professional work and the film we were about to see. Among his many accomplishments as a photographer and writer, he was the originator and driving force behind the A Day in the Life series of books where teams of photographers were dispatched to take pictures of different countries for each volume in such places as, among others, the United States, Japan and Spain.

He also added a whole new meaning to a having a hand in casting in his field by explaining to the audience that he had recently fallen from a try on his son’s scooter and hence his right hand was in a cast.

As the lights were dimmed and the film began, someone sitting right in front of me did something that was also, quite literally, enlightening but clearly in the wrong place and at the wrong time by opening up a laptop with a large and very bright screen. This was very distracting so I quickly switched seats. In retrospect, doing so also had the unintentional effect of providing me with a metaphor for the film: From my new perspective in the auditorium, I was seeing a movie that was likewise providing me with a whole new perspective on this important subject.

This film proceeded to provide an engrossing and informative examination of what exactly is “big data”, how it is gathered and analyzed, and its relative virtues and drawbacks.¹ It accomplished all of this by addressing these angles with segments of detailed expositions intercut with interviews of leading experts. In his comments afterwards, Mr. Smolan described big data as becoming a form of “nervous system” currently threading out across our entire planet.

Other documentarians could learn much from his team’s efforts as they smartly surveyed the Big Dataverse while economically compressing their production into a very compact and efficient package. Rather than a paint by, well, numbers production with overly long technical excursions, they deftly brought their subject to life with some excellent composition and editing of a wealth of multimedia content.

All of the film’s topics and transitions between them were appreciable evenhanded. Some segments specifically delved into how big data systems vacuum up this quantum of information and how it positively and negatively affects consumers and other demographic populations. Other passages raised troubling concerns about the loss of personal privacy in recent revelations concerning the electronic operations conducted by the government and the private sector.

I found the most compelling part of the film to be an interview with Dr. Eric Topol, (@EricTopol), a leading proponent of digital medicine, using smart phones as a medical information platform, and empowering patients to take control of their own medical data.² He spoke about the significance of the massive quantities and online availability of medical data and what this transformation  mean to everyone. His optimism and insights about big data having a genuine impact upon the quality of life for people across the globe was representative of this movie’s measured balance between optimism and caution.

This movie’s overall impression analogously reminded me of the promotional sponges that my local grocery used to hand out.  When you returned home and later added a few drops of water to these very small, flat and dried out novelties, they quickly and voluminously expanded. So too, here in just a 52-minute film, Mr. Smolan and his team have assembled a far-reaching and compelling view of the rapidly expanding parsecs of big data. All the audience needed to access, comprehend and soak up all of this rich subject matter was an open mind to new ideas.

Mr. Smolan returned to the stage after the movie ended to graciously and enthusiastically answer questions from the audience. It was clear from the comments and questions that nearly everyone there, whether they were familiar or unfamiliar with big data, had greatly enjoyed this cinematic tour of this subject and its implications. The audience’s well-informed inquiries concerned the following topics:

  • the ethics and security of big data collection
  • the degrees to which science fiction is now become science fact
  • the emergence and implications of virtual reality and augment reality with respect to entertainment and the role of big data in these productions³
  • the effects and influences of big data in medicine, law and other professions
  • the applications of big data towards extending human lifespans

Mr. Smolan also mentioned that his film will be shown on PBS in 2016. When it becomes scheduled, I very highly recommend setting some time aside to view it in its entirety.

Big data’s many conduits, trends, policies and impacts relentlessly continue to extend their global grasp. The Human Face of Big Data delivers a fully realized and expertly produced means for comprehending and evaluating this crucial and unavoidable phenomenon. This documentary is a lot to absorb yet an apt (and indeed fully app-ed), place to start.

 


One of the premiere online resources for anything and everything about movies is IMDB.com. It has just reached its 25th anniversary which was celebrated in a post in VentureBeat.com on October 30, 2015, entitled 25 Years of IMDb, the World’s Biggest Online Movie Database by Paul Sawers.


1These 44 Subway Fold Posts covered many of the latest developments in different fields, marketplaces and professions in the category of Big Data and Analytics.

2.  See also this March 3, 2015 Subway Fold post reviewing Dr. Topol’s latest book, entitled Book Review of “The Patient Will See You Now”.

3These 11 Subway Fold Posts cover many of the latest developments in the arts, sciences, and media industries in the category of Virtual and Augmented Reality. For two of the latest examples, see an article from the October 20, 2015 edition of The New York Times entitled The Times Partners With Google on Virtual Reality Project by Ravi Somaiya, and an article on Fortune.com on September 27, 2015 entitled Oculus Teams Up with 20th Century Fox to Bring Virtual Reality to Movies by Michael Addady. (I’m just speculating here, but perhaps The Human Face of Big Data would be well-suited for VR formatting and audience immersion.)

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.

Human Resources Management Meets Big Data in Devising Systems to Identify Star Employees

"2009 Leonid Meteor", Image by Ed Sweeney

“2009 Leonid Meteor”, Image by Ed Sweeney

Have you ever seen a clearly talented colleague at your workplace who was not fully recognized for his or her potential?

Today there is a raft of sophisticated data-driven software products being marketed to Human Resources departments (HR) to assist companies in finding possible star employees. However, some of these systems are not living up to their own, well, potential. Employers are still struggling to identify people on their staffs who have might be likely to excel in their future career paths.

This modern workplace quandary was the subject of a very interesting and informative feature in the June 17, 2015 edition of The Wall Street Journal entitled Are Companies Any Good at Picking Stars?, by Rachel Feintzeig.  I will sum up some of the main points, annotate, and ask some additional questions.

Businesses today have a wealth of data about their employees’ performances and productivity. Nonetheless, identifying who among them have the greatest potential to assume leadership roles in the future is still “more art than science”.  Assessments by humans as well as software algorithms are both still lacking in some respects.

As a result, companies including Nokia, American Express and SAP are turning to new means to measure employee potential. These include new forms of metrics and classifications, as well as games to identify leadership characteristics.

No firm has yet constructed a truly breakthrough HR system to accomplish this. Furthermore, a survey entitled Potential: Who’s Doing What to Identify Their Best? to conducted by Talent Strategy Group LLC indicates, among its other findings, that much of the approximately $70B to $75B US spent on corporate training has been “misspent”.

Tom Rauzi, Dell’s Director of Global Talent, will soon be launching a research project to assess employee data including “education, trajectories and performance” in an effort to identify candidates who might be best qualified to move up in the company.

Generally, when managers have workers with high potential, they have a tendency to choose people “who are like them”.  In another survey, this one by US-based management and advisory company CEB Inc., 25% of 9,500 manager surveyed reported that they “reply on gut instinct” when choosing potential future leaders. This might suggest why some businesses are so challenged in locating “fresh thinkers and diverse hires”.

Christopher Collins, “an associate professor at Cornell University’s School of Industrial and Labor Relations and director of its Center for Advanced Human Resource Studies“, reports that workers who sensed their work is being tracked and evaluated for advancement, often stay with their companies longer and work harder.

Conversely, those workers who are not tracked for future leadership may become resentful. As a result of this, SAP North America ended its high potential categorization.

Carie Davis, who until March 2015 was Coca-Cola’s Director of Innovation and Entrepreneurship, sensed that the company’s high potential program was made up mostly of “Type A employees” with common backgrounds. During some meetings, she found that the discussion ended up being more about “jostling for power” than the intended purpose of innovation.

At a management consulting company called Development Dimensions International Inc., a vice president named Matt Paese reported that companies are now using executive level assessment tools to test thousands of employees throughout their companies. His firm is set to soon start offering a “cheaper, lighter version” of their existing executive-level products for this purpose.

Some HR software vendors are devising their own new tools to illuminate potential. Their algorithms draw from a series of metrics including, among others, an employee’s 401(k) contributions, promotions and network connections within their firms.

For example, a system called UltiPro High Performance Predictor from Ultimate Software Group Inc., measures workers on the probability of their performing well, as distinguished from their potential, into future months. Currently, they are extending their research on “predictors of potential”.

Another suppressant of potential leadership in the workplace, rude and disrespectful behavior by management, was covered in a very insightful opinion piece in the June 25, 2015 edition of The New York Times entitled No Time to Be Nice at Work, by Christine Porath. I highly recommend reading this for its many piercing analytical insights as well as an adjunct to this terrific WSJ article by Ms. Feintzeig. I found that these articles overlapped on some points and can be seen as two sides of the same coin in their effects upon today’s workplaces.

My own questions are as follows:

  • In addition to all of the testing, training, metrics collection and analysis that goes on by HR departments, what if any role does the opinion of an employee’s peers have in spotting potential? While there are many businesses that engage in peer evaluations, I wonder whether on a more informal basis, are co-workers also asked to identify which of their colleagues could be future stars?
  • What are the results of follow-up validation studies in those who were promoted along a path to leadership? While the WSJ article explores the faults in these systems, what about the successes? If John and Mary have been vetted for a leadership track, do they more often than not meet such expectations? Are they more or less inclined to change jobs or departments along the way?
  • As companies, consultants and academics continue to experiment with and fine tune their algorithms, what is the relationship between and among data establishing a correlation as opposed to actual causation in identifying leaders? (This issue has also previously been visited in these five Subway Fold posts.)

Finally, for a hilarious take on a completely unqualified and unmotivated fictional employee failing his way up the corporate ladder, I very highly recommend checking out Season 2 of Silicon Valley on HBO. Here is an interview on Tumblr with the actor Josh Brenner, discussing his role as this character named “Big Head”.

Companies Are Forming Digital Advisory Panels To Help Keep Pace With Trending Technologies

"Empty Boardroom", Image by reynermedia

“Empty Boardroom”, Image by reynermedia

As a result of the lightening-fast rates of change in social media, big data and analytics, and online commerce¹, some large corporations have recently created digital advisory panels (also called  “boards”, “councils” and “groups” in place of “panels”), to assist executives in keeping pace with implementing some of the latest technologies. These panels are being patterned as less formal and scaled-down counterparts of traditional boards of directors.

This story was covered in a fascinating and very instructive article in the June 10, 2015 edition of The Wall Street Journal entitled “Companies Set Up Advisory Boards to Improve Digital Savvy” (subscription required, however, the article is fully available here on nasdaq.com). I will sum up, annotate and add a few questions of my own.

These digital advisory panels are often composed of “six outside experts under 50 years old”. In regularly scheduled meetings, their objective is to assist corporate managers in reaching diverse demographics and using new tools such as virtual reality² for marketing purposes. The executives whom the panels serve are appreciative of their “honest feedback”, access to entrepreneurs, and perspectives on these digital matters.

George L. Davis at the executive recruiting firm Egon Zehnder reports that approximately 50 companies in the Fortune 500 have already set up digital advisory panels. These include, among others, Target Corp. (details below) and American Express. However, not all such panels have not continued to stay in operation.

Here are the experiences of three major corporations with their digital advisory panels:

1. General Electric

GE’s digital advisory panel has met every quarter since its inception in 2011. Its members are drawn from a diversity of fields such as gaming and data visualization³. The youngest member of their 2014 panel was Christina Xu. She is a co-founder of a consulting company called PL Data. She found her experience with GE to be “an interesting window” into a corporate environment.

Ms. Xu played a key role in creating something new that has already drawn eight million downloads. It’s called the GE Sound Pack, a collection of factory sounds recorded at their own industrial facilities, intended for use by musicians4.  In effect, with projects like this the company is using the web in new ways to enhance its online presence and reputation.

GE’s panel also participated in the company’s remembrance of the 45th anniversary of the first moon landing. Back then, the company made the silicon rubber for the Apollo 11 astronauts’ boots. To commemorate in 2014, the panel convinced GE to create and market a limited edition line of “Moon Boot” sneakers online. They sold out in seven minutes. (For more details but, unfortunately, no more chances to get a pair of these way cool sneakers, see an article with photos of them entitled GE Modernizes Moon Boots and Sells Them as Sneakers, by Belinda Lanks, posted on Bloomberg.com on July 16, 2014 .)

2.  Target Corporation

On Target’s digital advisory council,  Ajay Agarwal, who is the Managing Director of Bain Capital Ventures in Palo Alto, California, is one of its four members. He was told by the company that “there were ‘no sacred cows’ “. Among the council’s recommendations was to increase Target’s staff of data scientists faster than originally planned, and to deploy new forms of in-store and online product displays.

Another council member, Sam Yagin, the CEO of Match.com,  viewed a “showcase” Target store and was concerned that it looked just like other locations. He had instead expected advanced and personalized features such as “smart” shopping carts linked to shoppers’ mobile phones that would serve to make shopping more individualized. Casey Carl, the chief strategy and innovation officer at Target, agreed with his assessment.

3.  Medtronic PLC

This medical device manufacturer’s product includes insulin pumps for people with diabetes.5 They have been working with their digital advisory board, founded in 2011, to establish a “rapport” on social media with this community. One of the board’s members, Kay Madati, who was previously an executive at Facebook, recommended a more streamlined approach using a Facebook page. The goal was to build patient loyalty. Today, this FB page (clickable here), has more than 230,000 followers. Another initiative was launched to expand Medtronics’ public perception beyond being a medical device manufacturer.

This digital advisory board was suspended following the company’s acquisition and re-incorporation in Ireland. Nonetheless, an executive expects the advisory board to be revived within six months.

My questions are as follows:

  • Would it be advisable for a member of a digital advisory panel to also sit on another company’s panel, given that it would not be a competitor? Would both the individual and both corporations benefit by the possible cross-pollination of ideas from different markets?
  • What guidelines should be established for choosing members of such panels in terms of their qualifications and then vetting them for any possible business or legal conflicts?
  • What forms of ethical rules and guidelines should be imposed panel members? If so, who should draft,  approve, and then implement them?
  • What other industries, marketplaces, government agencies, schools and public movements might likewise benefit from their own digital advisory panels? Would established tech companies and/or startups likewise find benefits from them?
  • Might finding and recruiting members for a digital advisory panel be a new market segment for executive search firms?
  • What new entrepreneurial opportunities might emerge when and if digital advisory panels continue to grow in acceptance and popularity?

 


1.   All of which are covered in dozens of Subway Fold posts in their respective categories here, here and here.

2.  There are six recent Subway Fold posts in the category of Virtual and Augmented Reality.

3.  There are 21 recent Subway Fold posts in the category of Visualization.

4.   When I first read this, it made me think of Factory by Bruce Springsteen on his brilliant Darkness on the Edge of Town album.

5.   X-ref to the October 3, 2014 Subway Fold post entitled New Startups, Hacks and Conferences Focused Upon Health Data and Analytics concerning Project Night Scout involving a group of engineers working independently to provide additional mobile technology integration and support for people using insulin pumps.

Tech Day New York 2015’s Great Success Was Clearly App-arent

IMAG0059Even though the weather was cold and windy in New York yesterday, the environment inside Tech Day New York 2015 (and on @TechDayHQ and #NYTD) was sunny and warm. Thousands of guests attended and were able to survey the exhibits and speak with the representatives of more than 400 startups from the NYC area. (Thanks and kudos, btw, to the designers and coders responsible this event’s website because it’s a very snappy and original piece of work.)

There is a thriving entrepreneurial community across this great city and its pride and spirit were well represented here. I found the hours that I spent wandering around the exhibits to be exhilarating because of the energy, creativity and determination displayed by all of these budding companies. Indeed, I found a massive group of people doing a lot of way cool things today. I took the photos above and below to try to capture some sense of the scale of TDNY.

Of course, such vivid concentrations of tech entrepreneurship exist elsewhere in a multitude of locations across the globe. But, forgive me, this is my hometown.

The startups at the event displayed a deep and wide range of online goods and services. Among many others, these included programming and app development tools, big data and analytics offerings, medical information collection and analytical platforms, cloud management and security systems, employment and benefits sites, social networking and organization apps, food preparation and delivery services, fashion industry services, music and media apps and services, education support offerings, and 3-D printing systems. There was even someone dressed up like a slice of pizza putting on some pretty cool dance moves in the middle of it all.

I stopped and talked with the reps at a number of the startups. I was very impressed with everyone’s sincerity, desire to succeed and wide-ranging knowledge of their businesses and markets. Despite the vast number of people attending, they all appeared to be making their best efforts to speak with everyone who was interested in speaking with them. I found that all of own my questions were answered in full and any of my inquiries for further clarifications were gladly provided. I also saw none of them doing hard sales pitches. Rather, they seemed more determined to make sure that the attendees to understand each venture’s goals, methods and services.

I believe that the attendees and these entrepreneurs both got much value out of participating in this tremendously exciting event. While not all of these startups will survive, they all deserve a grade of A+ for their visions, hard work and willingness to take big risks. Some will have the insight and fortitude to pivot and adapt their businesses plans to changes in the marketplace.

My very best wishes for all of them to succeed and continue to thrive.

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