Facebook is Now Restricting Access to Certain Data About Its User Base to Third Parties

Image by Gerd Altmann

Image by Gerd Altmann

It is a simple and straight-forward basic business concept in any area of commerce: Do not become too overly reliant upon a single customer or supplier. Rather, try to build a diversified portfolio of business relationships to diligently avoid this possibility and, at the same time, assist in developing potential new business.

Starting in May 2015, Facebook instituted certain limits upon access to the valuable data about its 1.5 billion user base¹ to commercial and non-commercial third parties. This has caused serious disruption and even the end of operations for some of them who had so heavily depended on the social media giant’s data flow. Let’s see what happened.

This story was reported in a very informative and instructive article in the September 22, 2015 edition of The Wall Street Journal entitled Facebook’s Restrictions on User Data Cast a Long Shadow by Deepa Seetharaman and Elizabeth Dwoskin. (Subscription required.) If you have access to the WSJ.com, I highly recommend reading in its entirety. I will summarize and annotate it, and then pose some of my own third-party questions.

This change in Facebook’s policy has resulted in “dozen of startups” closing, changing their approach or being bought out. This has also affected political data consultants and independent researchers.

This is a significant shift in Facebook’s approach to sharing “one of the world’s richest sources of information on human relationships”. Dating back to 2007, CEO Mark Zuckerberg opened to access to Facebook’s “social graph” to outsiders. This included data points, among many others, about users’ friends, interests and “likes“.

However, the company recently changed this strategy due to users’ concerns about their data being shared with third parties without any notice. A spokeswoman from the company stated this is now being done in manner that is “more privacy protective”. This change has been implemented to thus give greater control to their user base.

Other social media leaders including LinkedIn and Twitter have likewise limited access, but Facebook’s move in this direction has been more controversial. (These 10 recent Subway Fold posts cover a variety of ways that data from Twitter is being mined, analyzed and applied.)

Examples of the applications that developers have built upon this data include requests to have friends join games, vote, and highlight a mutual friend of two people on a date. The reduction or loss of this data flow from Facebook will affect these and numerous other services previously dependent on it. As well, privacy experts have expressed their concern that this change might result in “more objectionable” data-mining practices.

Others view these new limits are a result of the company’s expansion and “emergence as the world’s largest social network”.

Facebook will provide data to outsiders about certain data types like birthdays. However, information about users’ friends is mostly not available. Some developers have expressed complaints about the process for requesting user data as well as results of “unexpected outcomes”.

These new restrictions have specifically affected the following Facebook-dependent websites in various ways:

  • The dating site Tinder asked Facebook about the new data policy shortly after it was announced because they were concerned that limiting data about relationships would impact their business. A compromise was eventually obtained but limited this site only to access to “photos and names of mutual friends”.
  • College Connect, an app that provided forms of social information and assistance to first-generation students, could not longer continue its operations when it lost access to Facebook’s data. (The site still remains online.)
  • An app called Jobs With Friends that connected job searchers with similar interests met a similar fate.
  • Social psychologist Benjamin Crosier was in the process of creating an app searching for connections “between social media activity and ills like drug addiction”. He is currently trying to save this project by requesting eight data types from Facebook.
  • An app used by President Obama’s 2012 re-election campaign was “also stymied” as a result. It was used to identify potential supporters and trying to get them to vote and encourage their friends on Facebook to vote or register to vote.²

Other companies are trying an alternative strategy to build their own social networks. For example, Yesgraph Inc. employs predictive analytics³ methodology to assist clients who run social apps in finding new users by data-mining, with the user base’s permission, through lists of email addresses and phone contacts.

My questions are as follows:

  • What are the best practices and policies for social networks to use to optimally balance the interests of data-dependent third parties and users’ privacy concerns? Do they vary from network to network or are they more likely applicable to all or most of them?
  • Are most social network users fully or even partially concerned about the privacy and safety of their personal data? If so, what practical steps can they take to protect themselves from unwanted access and usage of it?
  • For any given data-driven business, what is the threshold for over-reliance on a particular data supplier? How and when should their roster of data suppliers be further diversified in order to protect themselves from disruptions to their operations if one or more of them change their access policies?

 


1.   Speaking of interesting data, on Monday, August 24, 2015, for the first time ever in the history of the web, one billion users logged onto the same site, Facebook. For the details, see One Out of Every 7 People on Earth Used Facebook on Monday, by Alexei Oreskovic, posted on BusinessInsider.com on August 27, 2015.

2See the comprehensive report entitled A More Perfect Union by Sasha Issenberg in the December 2012 issue of MIT’s Technology Review about how this campaign made highly effective use of its data and social networks apps and data analytics in their winning 2012 re-election campaign.

3.  These seven Subway Fold posts cover predictive analytics applications in range of different fields.

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.

 

Minting New Big Data Types and Analytics for Investors

Along with the exponential growth of big data in terms of its quantity, myriad of collection points, nearly limitless storage capabilities, and complex analytics, investors are keenly interested in discovering unique advantages from this phenomenon to be applied in the securities markets.* While financial institutions of all types have used sophisticated metrics and predictions to gain tactical advantages in their trading operations for many decades, burgeoning big data methodologies have recently created new opportunities for entrepreneurs to provide the financial services industry with ever more original and arcane forms of predictive analytics.

Investors now have data services available to them offering insights never previously feasible or even imaginable. Yesterday’s (November 21, 2014) edition of The Wall Street Journal carried a fascinating report highlighting three of these operations entitled Startups Tip Investors to Hidden Data Pearls by Bradley Hope. (A subscription to the WSJ Online is required for full access to this report on WSJ.com, but this piece was available here in slightly different version on CBS’s Marketwatch.com.) This additional extract page from the article is also available online and contains explanatory graphics of their formats and analyses.

How are these new data points being mined, examined and spun into forecasts? To briefly sum up the work of these startups covered in this article:

  • Orbital Insight analyzes satellite photos of building sites in 30 cities in China, cornfields, and parking lots in order to assess how their capacities might influence the markets in various ways. They are seeking to intuit “early indicators” of trends and influences. Their clients include hedge funds.
  • Dataminr sifts through a half a billion daily tweets in order to spot potential market moving trends ahead of the news services.** The link above to the graphics from the WSJ article contains a very effective infographic on this process.*** The company’s proprietary systems categorize and analyze all tweets in real time, discerns potentially useful patterns, and then distributes the results to their clients.
  • Premise Inc. uses a global system, now in 18 countries, that provides cell phone credits as payments to individuals who monitor the prices of various goods. From this input, the company tracks early inflation rates and other economic data. They believe that their data can differ from official government sources.

I recommend reading this story in full for all of its compelling details.

My follow up questions include:

  • Who watches these watchmen? Will market forces determine which of them are producing valid and actionable collection and analytics or should they somehow be subject to regulatory oversight?
  • Because these data types and analytics are so new, how are these companies and others like them addressing the distinctions between correlation and causation in their reports to their clients? Would it be beneficial for them to form a trade association to address this and other issues that might arise in the future for this nascent industry?
  • Are there entrepreneurial opportunities here for another type of new startups to vet the practices and products of such companies? That is, analysts who produce no new data types themselves, but rather, apply existing and, perhaps develop new, analytical tools for such assessments?
  • What other fields, markets and professions might benefit from this trend to discover and assess new data types in addition to finance?

_____________________________

*    Please see this April 9, 2014 Subway Fold post entitled Roundup of Some Recent Books on Big Data, Analytics and Intelligent Systems.

**   Please see this July 31, 2014 Subway Fold post entitled New Analytical Twitter Traffic Report on US TV Shows During the 2013 – 2014 Season.

***  Please see this January 30, 2015 Subway Fold post entitled Timely Resources for Studying and Producing Infographics.