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What Makes You Ill? Social Media? Nope

January 14, 2016

I read “Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.” The study reveals that people who are unhappy also get sick.

Lots of effort went into this statement:

In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network.

My hunch is that those who are believers in social media will be able to link Snapchat snaps and Reddit posts with feeling good and being healthy.

I have a different view of social media and its possible benefits: Social media posts are outstanding sources of data for those who want to predict where a Google Map thinks you will go. Other groups like social media data as well; for example, bad actors.

My thought is that heavy users of social media may find themselves making new friends. For example, when you get out of your autonomous vehicle and know no one, you can ask, “Yo, dude, where am I?” Then say, “Let’s be friends.” This is a great ice breaker in Woodlawn, for instance.

Another function is that your college roomie now supporting certain groups of interest may open some new “friendship doors.” For example, if an investigative group exploring relationships with certain tools, you will spend quite a bit of time with your new friends.

Social media, therefore, addresses loneliness. That leads to a healthier life. Obvious, no?

Stephen E Arnold, January 14, 2016

Reverend Bayes Is Inevitable

January 14, 2016

I read “R Users Will Now Inevitably Become Bayesians.” Years ago a non mathy content management maven told me that Bayes’s methods were baloney. There is not much one can do to undo misspent youth and a lack of a technical background in things that require numbers in my experience.

The write up explains that wrestle with R will find themselves turning into adherents of Bayes’s methods, and I assume these R fans will end up looking like this:


The notion of inverse probability, according the write up,

Bayesian modeling is a general machine that can model any kind of regression you can think of….With the advent of brms and rstanarm, R users can now use extremely flexible functions from within the familiar and powerful R framework. Perhaps we won’t all become Bayesians now, but we now have significantly fewer excuses for not doing so. This is very exciting!

I feel a tingle, but I don’t think the CMS oriented, non mathy types will experience much of a quiver. Too bad.

Stephen E Arnold, January 14, 2016

For Search Vendors Suddenly Transformed into Analytics Companies

January 11, 2016

I read “48 Questions to Ask to a Potential Data Scientist Hire.” The list is useful. I added two questions to make it easy to calculate one’s score. My additions are:

  1. What is Bayesian drift and how does one adjust to it?
  2. What is one use case for C* algebras when determining similarities?

Now, here’s what I propose to the vendors of keyword search systems using the phrases “analytics,” “metrics,” and for good measure “Big Data” in their marketing pitches.

Get the top three or four executives in the company together. Invite at least one of the people providing venture funding for the company.

Distribute the questions to everyone and then work through the 50 questions. Yep, you can collaborate and make phone calls to a junior in high school who is good in math.

Calculate your score. Use this scale to determine the probability of your new market positioning. Here’s the grading scale for your collective team:

90 to 100 percent: A. You are a top five percenter! You have a winner.

80 to 89 percent: B. Meh. Look for a company to buy and try another market positioning, maybe human resource management?

70 to 79 percent: C. Dull normal. Fire people, just like Yahoo and IBM.

60 to 69 percent: D. You are a disgrace to your ancestors. Start raising money. Include your relatives, the bank holding your mortgage, and a neighbor known to be losing touch with NCSI.

0 to 59 percent: F. Failure. Check out the openings at Wendy’s or KFC.

Stephen E Arnold, January 11, 2016

IBMs CFO Reveals IBMs Innovation Strategy: Why Not Ask Watson

January 11, 2016

The article on TechTarget titled IBM CFO Schroeter on the Company’s Innovation Strategy delves into the mind of Martin Schroeter regarding IBM’s strategy for chasing innovation in healthcare and big data. This year alone IBM acquired three healthcare companies with data on roughly one hundred million people as well as massive amounts of data on medical conditions. Additionally, as the article relates,

“IBM’s purchase of The Weather Co.’s data processing and analytics operations brought the company a “massive ingestion machine,” which plays straight into its IoT strategy, Schroeter said. The ingestion system pulls in 4 GB of data per second, he said, and runs a lot of analytics as users generate weather forecasts for their geographies. The Weather Co. system will be the basis for the company’s Internet of Things platform, he said.”

One of many interesting tidbits from the mouth of Schroeter was this gem about companies being willing to “disrupt [themselves]” to ensure updated and long-term strategies that align technological advancement with business development. The hurtling pace of technology has even meant IBM coming up with a predictive system to speed up the due diligence process during acquisitions. What once took weeks to analyze and often lost IBM deals has now been streamlined to a single day’s work. Kaboom.


Chelsea Kerwin, January 11, 2016

Sponsored by, publisher of the CyberOSINT monograph

The Secret Weapon of Predictive Analytics Revealed

January 8, 2016

I like it when secrets are revealed. I learned how to unlock the treasure chest containing predictive analytics secret weapon. You can too. Navigate to “Contextual Integration Is the Secret Weapon of Predictive Analytics.”

The write up reports:

Predictive analytics has been around for years, but only now have data teams begun to refine the process to develop more accurate predictions and actionable business insights. The availability of tremendous amounts of data, cheap computation, and advancements in artificial intelligence has presented a massive opportunity for businesses to go beyond their legacy methodologies when it comes to customer data.

And what is the secret?

Contextual transformation.

Here’s the explanation:

A major part of this transformation is the realization that data needs to be looked at from as many angles as possible in an effort to create a multi-dimensional profile of the customer. As a consequence, we view recommendations through the lens of ensembles in which each modeled dimension may be weighted differently based on real-time contextual information. This means that, rather than looking at just transactional information, layering in other types of information, such as behavioral data, gives context and allows organizations to make more accurate predictions.

Is this easy?

Nope. The article reminds the reader:

A sound approach follows the scientific method, starting with understanding the business domain and the underlying data that is available. Then data scientists can prepare to test a particular hypothesis, build a model, evaluate results, and refine the model to draw general conclusions.

I would point out that folks at Palantir, Recorded Future, and other outfits have been working for years to deal with integration, math, and sense making.

I wonder if the wonks at these firms have realized that contextual integration is the secret? I assume one could ask IBM Watson or just understand the difference between interpreting marketing inputs from a closed user base and dealing with slightly more slippery data has more than one secret.

Stephen E Arnold, January 8, 2016

In Scientific Study Hierarchy Is Observed and Found Problematic to Cooperation

January 8, 2016

The article titled Hierarchy is Detrimental for Human Cooperation on Nature.Com Scientific Reports discusses the findings of scientists related to social dynamics in human behavior. The abstract explains in no uncertain terms that hierarchies cause problems among human groups. Perhaps surprisingly to many millennials, hierarchies actually forestall cooperation. The article explains the circumstances of the study,

“Participants competed to earn hierarchy positions and then could cooperate with another individual in the hierarchy by investing in a common effort. Cooperation was achieved if the combined investments exceeded a threshold, and the higher ranked individual distributed the spoils unless control was contested by the partner. Compared to a condition lacking hierarchy, cooperation declined in the presence of a hierarchy due to a decrease in investment by lower ranked individuals.”

The study goes on to explain that regardless of whether power or rank was earned or arbitrary (think boss vs. boss’s son), it was “detrimental to cooperation.” It also goes into great detail on how to achieve superior cooperation through partnership and without an underlying hierarchical structure. There are lessons to take away from this study in the many fields, and the article is mainly focused on economic metaphors, but what about search vendors? Organization does, after all, have value.

Chelsea Kerwin, January 8, 2016

Sponsored by, publisher of the CyberOSINT monograph

Reverend Bayes: Still Making Headlines

January 6, 2016

Autonomy, now owned by Hewlett Packard Enterprise, was one of the first commercial search and content processing firms to embrace Bayesian methods. The approach in the 1990s was not as well known and as widely used as it is today. Part of the reason was the shroud of secrecy dropped over the method. Another factor was the skepticism some math folks had about the “judgment” factor required to set up Bayesian methods. That skepticism is still evident today even though Bayesian methods are used by many of the information processing outfits making headlines today.

A good example of the attitude appears in “Bayes’s Theorem: What’s the Big Deal?

Here’s the quote I noted:

Embedded in Bayes’ theorem is a moral message: If you aren’t scrupulous in seeking alternative explanations for your evidence, the evidence will just confirm what you already believe. Scientists often fail to heed this dictum, which helps explains why so many scientific claims turn out to be erroneous. Bayesians claim that their methods can help scientists overcome confirmation bias and produce more reliable results, but I have my doubts.

Bayesian methods are just one of the most used methods in analytics outfits. Will these folks change methods? Nah.

Stephen E Arnold, January 6, 2015

Fasten Your Seat Belts: Search Driven Analytics

January 4, 2016

Editor’s Note: ThoughtSpot has no relationship with EMC.

The buzzword meisters are salivating. A term kicked around by folks like Lucidworks (really?) and Radiology Software has been snapped up by EMC. Yep, I know. EMC is not a search vendor, and I was surprised to learn that it was in the analytics business. Hey, that’s what happens when one lives in rural Kentucky.

According to EMC, the “new” concept is the spark behind ThoughtSpot. I learned from “Introducing ThoughtSpot 3: The World’s First Product to Harness Collective Intelligence for Search Driven Analytics”:

ThoughtSpot 3 combines the ease of search with the intelligence of machine learning to deliver a powerful analytic solution that anyone can use to quickly get the right answers out of their data.

Slam dunk. Stock up on EMC shares which are trading in value territory. The company has reported flat revenues and profit margins, but search driven analytics, now in Version 3, is something that makes mid tier consulting firms quiver.


Aberdeen allegedly said:

“As the desire for data-driven decisions grows across the business world, there is a greater appetite for people capable of creating data insights,” said Aberdeen Vice President and Principal Analyst Michael Lock. “For companies looking to create insights faster and more easily, early findings from Aberdeen’s latest survey indicate that Best-in-Class organizations are adopting language-driven analytics, for example search-driven analytics and code-free discovery, at a greater rate than lesser performers.”

That’s sufficient for me. Now we just need to watch the revenues of EMC and other vendors almost certain to embrace a buzzword with some rubber left on the 15 inch recap.

Stephen E Arnold, January 4, 2015

Klout Identifies Trendy Experts

January 4, 2016

I read “Top Algorithm, Data Science, Big Data, and Machine Learning Experts.” I am not sure what to make of the write up and the information it presents. The “rankings” are derived from an analysis of Klout scores. I am not a Klout person and the notion of having one’s influence rated on a scale of one to 100. The Klout score, it seems, reflects an individual’s influence via or “in” social media.

According to the article, a publication about search engine marketing in in the top five experts in algorithms. I assume this means that many folks get their algorithmic guidance from a marketing oriented publication. A fellow named Vincent Granville, who is pretty good at the Tweeter stuff, is the top expert in Big Data, Data Visualization, Deep Learning, Machine Learning and Statistics. He’s only number 2 in predictive analytics, however.

Interesting. No wonder I have a Klout score of i.

Stephen E Arnold, December 31, 2015

SEO Tips Based on Recent Google Search Quality Guidelines

December 30, 2015

Google has recently given search-engine optimization pros a lot to consider, we learn from “Top 5 Takeaways from Google’s Search Quality Guidelines and What They Mean for SEO” at Merkle’s RKG Blog. Writer Melody Pettula presents five recommendations based on Google’s guidelines. She writes:

“A few weeks ago, Google released their newest Search Quality Evaluator Guidelines, which teach Google’s search quality raters how to determine whether or not a search result is high quality.  This is the first time Google has released the guidelines in their entirety, though versions of the guidelines have been leaked in the past and an abridged version was released by Google in 2013. Why is this necessary? ‘Quality’ is no longer simply a function of text on a page; it differs by device, location, search query, and everything we know about the user. By understanding how Google sees quality we can improve websites and organic performance. Here’s a countdown of our top 5 takeaways from Google’s newest guidelines and how they can improve your SEO strategy.”

We recommend any readers interested in SEO check out the whole article, but here are the five considerations Pettula lists, from least to most important: consider user intent; supply supplementary content; guard your reputation well; consider how location affects user searches; and, finally, “mobile is the future.” On that final point, the article notes that Google is now almost entirely focused on making things work for mobile devices. SEO pros would do well to keep that new reality in mind.

Cynthia Murrell, December 30, 2015

Sponsored by, publisher of the CyberOSINT monograph


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