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 ArnoldIT.com, 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 ArnoldIT.com, 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 ArnoldIT.com, publisher of the CyberOSINT monograph
Marketing Analytics Holds Many Surprises
December 29, 2015
What I find interesting is how data analysts, software developers, and other big data pushers are always saying things like “hidden insights await in data” or “your business will turn around with analytics.” These people make it seem like it is big thing, when it is really the only logical outcome that could entail from employing new data analytics. Marketing Land continues with this idea in the article, “Intentional Serendipity: How Marketing Analytics Trigger Curiosity Algorithms And Surprise Discoveries.”
Serendipitous actions take place at random and cannot be predicted, but the article proclaims with the greater amount of data available to marketers that serendipitous outcomes can be optimized. Data shows interesting trends, including surprises that make sense but were never considered before the data brought them to our attention.
“Finding these kinds of data surprises requires a lot of sophisticated natural language processing and complex data science. And that data science becomes most useful when the patterns and possibilities they reveal incorporate the thinking of human beings, who contribute the two most important algorithms in the entire marketing analytics framework — the curiosity algorithm and the intuition algorithm.”
The curiosity algorithm is the simple process of triggering a person’s curious reflex, then the person can discern what patterns can lead to a meaningful discovery. The intuition algorithm is basically trusting your gut and having the data to back up your faith. Together these make explanatory analytics help people change outcomes based on data.
It follows up with a step-by-step plan about how to organize your approach to explanatory analytics, which is a basic business plan but it is helpful to get the process rolling. In short, read your data and see if something new pops up.
Whitney Grace, December 29, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Analytics Predictions for 2016
December 28, 2015
Well, there is one omission: Predictive analytics. The truth is revealed in “Top 5 Analytics Predictions for 2016.” I like the idea of focusing on five prognostications.
Here is what’s ahead in the analytics sector. Close the flap on your SAS disks:
- Machine learning is “established” in the enterprise.
- The Internet of Things “hits reality.”
- Big Data enriches modeling.
- Cybersecurity is improved via analytics.
- Analytics drives increased industry academic interaction.
A few observations.
Machine learning is a synonym for artificial intelligence and smart software. My experience is that software has included smarter functions for years. Remember Clippy? Don’t you love those disappearing options in Adobe “creative” products?
The Internet of Things remains a bit of a baffler to me. I am not sure about a smart refrigerator, but I am okay with machine tools reporting their “health” to a person who wants to keep downtime to a minimum. Unfortunately that type of IoT application is moving, just not with the pace of an intrepid millennial on the Stairmaster.
The notion of enriching modeling is interesting. The push is to make intelligence systems deliver outputs in a semi or automated fashion. Focusing on the intermediary—that is, the modeler—reminds me of the non user friendly tasks an analyst must perform before outputs are available.
On the cybersecurity front, analytics have been a major thrust for years. I assume that when one predicts the future, information about the past and what’s currently in use are not pre-requisites.
The academic industry thing is an interesting way to make clear that folks with knowledge of math, statistics, and related expertise are in short supply. Universities are in the financial services business. I am not sure their core competency is producing more math types quickly. Well, maybe the Kahn Academy can pick up the slack. C* algebras are really trivial and can be learned in a four minute video.
Quite a list.
Stephen E Arnold, December 30, 2015
RankBrain, the Latest AI from Google, Improves Search Through Understanding and Learning
December 23, 2015
The article on Entrepreneur titled Meet RankBrain, the New AI Behind Google’s Search Results introduces the AI that Google believes will aid the search engine in better understanding the queries it receives. RankBrain is capable of connecting related words to the search terms based on context and relevance. The article explains,
“The real intention of this AI wasn’t to change visitors’ search engine results pages (SERPs) — rather, it was to predict them. As a machine-learning system, RankBrain actually teaches itself how to do something instead of needing a human to program it…According to Jack Clark, writing for Bloomberg on the topic: “[Rankbrain] uses artificial intelligence to embed vast amounts of written language into mathematical entities — called vectors — that the computer can understand.”
Google scientist Greg Corrado spoke of RankBrain actually exceeding his expectations. In one experiment, RankBrain beat a team of search engineers in predicting which pages would rank highest. (The engineers were right 70% of the time, RankBrain 80%.) The article also addresses concerns that many vulnerable brands relying on SEOs may have. The article ventures to guess that it will be mainly newer brands and services that will see a ranking shift. But of course, with impending updates, that may change.
Chelsea Kerwin, December 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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