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Restlet Promotes Paul Doscher to the Cloud

October 8, 2015

What has Paul Doscher been up to?  We used to follow him when he was a senior executive over at LucidWorks, but he has changed companies and is now riding on clouds.  PRWeb published the press release “Restlet Appoints Paul Doscher As New CEO To Accelerate Deployment Of Most Comprehensive Cloud-Based API Platform.”  Doscher is the brand new president, CEO, and board member at Restlet, leading creators of deployed APIs framework.  Along with LucidWorks, Doscher held executive roles at VMware, Oracle, Exalead, and BusinessObjects.

Restlet hot its start as an open source project by Jerome Louvel.  Doscher will be replacing Louvel as the CEO and is quite pleased about handing over the reins to his successor:

“ ‘I’m extremely pleased that we have someone with Paul’s experience to grow Restlet’s leadership position in API platforms,’ said Louvel. ‘Restlet has the most complete API cloud platform in the industry and our ease of use makes it the best choice for businesses of any size to publish and consume data and services as APIs. Paul will help Restlet to scale so we can help more businesses use APIs to handle the exploding number of devices, applications and use cases that need to be supported in today’s digital economy.’ ”

Doscher wants to break down the barriers for cloud adoption and take it to the next level.  His first task as the new CEO will be implementing the API testing tools vendor DHC and using it to enhance Restlet’s API Platform.

Restlet is ecstatic to have Doscher on board and Louvel is probably heading off to a happy retirement.

Whitney Grace, October 8, 2015
Sponsored by, publisher of the CyberOSINT monograph

Agriculturized Content Marketing

October 7, 2015

When you think of paid content, eggs are probably not the first product you envision. However, the Guardian reveals, “US-Appointed Egg Lobby Paid Food Blogs and Targeted Chef to Crush Vegan Startup.” Apparently, the American Egg Board’s (AEB’s) efforts began when Silicon  Valley startup Hampton Creek began gaining traction with their egg alternative. Fearing encroachment on its territory, the AEB is reported to have paid food bloggers up to $2500 to insert their talking points into recipes and other content; to have slammed publications that wrote positive articles about Hampton Creek; to have attempted to recruit celebrities to push real eggs; and, my favorite, to have purchased Google ads that returned AEB-sponsored content when users searched for Hampton Creek or company founder Josh Tetrick.

There is a slight problem: these tactics appear to violate U.S. Department of Agriculture rules. Reporter Sam Thielman tells us:

“The scale of the campaign – dubbed ‘Beyond Eggs’ after Hampton Creek’s original company name – shows the lengths to which a federally-appointed, industry-funded marketing group will go to squash a relatively small Silicon Valley startup, from enlisting a high-powered public relations firm to buying off unwitting bloggers. One leading public health attorney, asked to review the internal communications, said the egg marketing group was in breach of a US department of agriculture (USDA) regulation that specifically prohibited ‘any advertising (including press releases) deemed disparaging to another commodity’. Tetrick called for the USDA to clamp down on the food lobby, as thousands of petitioners called on the White House to investigate the USDA itself for ‘deceptive endorsements’. ‘This is a product that has been around for a very long time,’ the Hampton Creek founder said. ‘They are not used to competition and they don’t know how to deal with it’.”

That’s one way to look at it. It seems that Tetrick’s company, however, is not beyond reproach. The U.S. Food and Drug Administration recently told them to rename their main product, “Just Mayo,” because mayonnaise, by definition, contains eggs. There also seem to be some issues with their methods and work environment, according to former employees. See the article for more details on this culinary rivalry.

Cynthia Murrell, October 7, 2015

Sponsored by, publisher of the CyberOSINT monograph

Facebook on Top of App Sales

October 7, 2015

While Facebook is a common social media tool and it does not make headlines as much as it used to, except when it added the new GIF function and angers users by rearranging its options, it now has something even more exciting to shout about.  Business Insider reported that, “Facebook’s WhatsApp Hits Another Major Milestone” with a messaging app that it bought back in 2014.

Facebook bought WhatsApp for $19 billion and since its purchase its growth has exploded.  There are now nine hundred million active users and it could jump to one billion by the end of the year.  Compared to its competitors Viber and WeChat, however, is not bringing in much profit.  Zuckerberg has plans for WhatsApp and has asked his investors to be patience.  He wants WhatsApp to be a “natural place for people to communicate with businesses.”

” ‘The long-term bet is that by enabling people to have good organic interactions with businesses, that will end up being a massive multiplier on the value of the monetization down the road, when we really work on that, and really focus on that in a bigger way,’ Zuckerberg said.”

Zuckerberg knows what he is doing.  He is setting up a messenger platform that people trust, enjoy, and is popular.  When you have access to nine hundred million active users and want to grow it to one billion, there are definitely plans to monetize it.  We just have to wait.

Whitney Grace, October 7, 2015
Sponsored by, publisher of the CyberOSINT monograph

Business Intelligence and Data Science: There Is a Difference

October 6, 2015

An article at the SmartDataCollective, “The Difference Between Business Intelligence and Real Data Science,” aims to help companies avoid a common pitfall. Writer Brigg Patton explains:

“To gain a competitive business advantage, companies have started combining and transforming data, which forms part of the real data science. At the same time, they are also carrying out Business Intelligence (BI) activities, such as creating charts, reports or graphs and using the data. Although there are great differences between the two sets of activities, they are equally important and complement each other well.

“For executing the BI functions and data science activities, most companies have professionally dedicated BI analysts as well as data scientists. However, it is here that companies often confuse the two without realizing that these two roles require different expertise. It is unfair to expect a BI analyst to be able to make accurate forecasts for the business. It could even spell disaster for any business. By studying the major differences between BI and real data science, you can choose the right candidate for the right tasks in your enterprise.”

So fund both, gentle reader. Patton distinguishes each position’s area of focus, the different ways they use and look at data, and  their sources, migration needs, and job processes. If need to hire someone to perform these jobs, check out this handy clarification before you write up those job descriptions.

Cynthia Murrell, October 6, 2015

Sponsored by, publisher of the CyberOSINT monograph

Visual Analytics Makes Anyone a Data Expert

October 5, 2015

Humans are sight-based creatures.  When faced with a chunk of text or a series of sequential pictures, they will more likely scan the pictures for information than read.  With the big data revolution, one of the hardest problems analytics platforms have dealt with is how to best present data for users to implement.  Visual analytics is the key, but one visual analytics is not the same as another.   DCInno explains that one data visual company stands out from the rest in the article, “How The Reston Startup Makes Everyone A Big Data Expert.”

Zoomdata likes to think of itself as the one visual data companies that gives its clients a one up over others and it goes about it in layman’s terms.

“Zoomdata has been offering businesses and organizations a way to see data in ways more useful than a spreadsheet since it was founded in 2012. Its software offers real-time and historical explorations of data streams, integrating multiple sources into a cohesive whole. This makes the analytics far more accessible than they are in raw form, and allows a layperson to better understand what the numbers are saying without needing a degree in mathematics or statistics.”

Zoomdata offers a very interactive platform and is described to be the only kind on the market.  Their clients range from government agencies, such as the Library of Congress, and private companies.  Zoomdata does not want to be pigeonholed as a government analytics startup.  Their visual data platform can be used in any industry and by anyone with the goal of visual data analytics for the masses.  Zoomdata has grown tremendously, tripled its staff, and raised $22.2 million in fundraising.

Now let us sit back and see how their software is implemented in various industries.  I wonder if they could make a visual analytics graphic novel?
Whitney Grace, October 5, 2015
Sponsored by, publisher of the CyberOSINT monograph

Edging Closer to a Predictive Analytics Appliance

October 4, 2015

We live in a hybrid world. Some see virtualization as the future. Other want hardware with smart software able to live in the cloud or down the hall in an on premises facility. The smart software has to figure out what the information flowing to the system and stored within the system means. Humans used to do this work, but volume and other constraints force a rethink. If the information is “FalconStor Adds Cumulus Logic Predictive Analytics to FreeStor” is accurate, a predictive analytics appliance may be at hand. If so, this is an important step. I don’t like the lack of “e”, however.

Stephen E Arnold, October 4, 2015

Google Flu Trends: Smart Software in Action

October 2, 2015

i read “What We Can Learn from the Epic Failure of Google Flu Trends.” I like history. In grade school in the 1950s, there was not much talk about predicting the flu.

Flash forward to 2008. According to the write up:

In 2008, researchers from Google explored this [prediction based on users’ queries] potential, claiming that they could “nowcast” the flu based on people’s searches. The essential idea, published in a paper in Nature, was that when people are sick with the flu, many search for flu-related information on Google, providing almost instant signals of overall flu prevalence.

Then what? Failure. The write up reminded me:

GFT failed—and failed spectacularly—missing at the peak of the 2013 flu season by 140 percent. When Google quietly euthanized the program, called Google Flu Trends (GFT), it turned the poster child of big data into the poster child of the foibles of big data.

The point is that Big Data are going to be darned useful. I agree. For now I will temper my enthusiasm for Google’s beating death and IBM Watson curing cancer. I want to be conservative and get a flu shot.

Stephen E Arnold, October 2, 2015

Massive Analytic: The First Precognitive Analytics Platform

October 1, 2015

Let me reflect a moment. IBM is doing cognitive computing. I am assuming that the on going PR and marketing activities are accurate representation of money making technologies.

Massive goes IBM one better. The Massive Analytic outfit claims on its Web site that it delivers “effortless data driven decisions.” The product or service is Oscar AP, which allows you to “analyze all your data with artificial precognition.”

Interesting. About five weeks ago, I read “SAP, Oracle and HP Don’t Get Big Data, Claims Massive Analytic Chairman.” In the article, I learned:

Large IT vendors such as SAP, Oracle and HP don’t understand how to properly help their customers to make the most of big data, being more concerned about locking them into their ecosystems than providing them with true analytical insight. That’s according to George Frangou, executive chairman and founder of “precognitive data analytics” platform Oscar AP, which Frangou described as “an AI that allows people to foresee the future and the outcome of their decisions” which “makes Minority Report real”.

That reminded me of Recorded Future, an outfit partially funded by the Alphabet Google thing and In-Q-Tel, the US government intelligence community’s investment fund. Recorded Future rolled out in 2008 after a year or so of gestation. Massive Analytic took its first breath in 2010. I assume the wiggle room created by the term “precognitive” allows Massive Analytic to claim the adjective “first.”

The write up about Massive Analytic contained a statement which I found interesting. I circled this in red, gentle reader:

according to Frangou, larger competitors such as SAP, Oracle and HP “don’t get it” when it comes to making the most of big data and analytics. “They don’t get it because the driver for them is to sell kit. … You’re into millions of dollars before you start,” he said, attacking the aggressive sales tactics of the big vendors, which he said are designed solely to sell the product and not to provide support. “And by the way, the actual algorithms don’t scale either, so you’re into lots of people and manual intervention,” he added. Because of this, Frangou said Massive Analytic is “quite unashamedly following a displacement strategy to displace the incumbents because they’re not getting it”.  He added that SAP HANA, Oracle Exalytics and HP Haven are essentially the same product because they’re built on the same base code.

It is true that most analytics vendors recycle what the engineers and mathematicians with MBAs learned in their university courses. I am not sure about the “algorithms don’t scale.” There are issues with algorithms, but as the work by SRCH2 shows, there is a great deal of innovation opportunity in optimization.

But the point which I find slightly jarring is the reference to SAP, Oracle, and HP “built on the same base code.”

Well, maybe. SAP uses home brew code (anyone remember TREX), acquired stuff from Business Objects (Inxight), and open source snippets. Oracle uses the wild and crazy home brew code, acquired code from “analytics” outfits like Endeca, and confections from some of the Oracle partner ecosystem. HP—an example for MBA cases studies for the next couple of decades—uses home brew, acquired technology from outfits like Autonomy, and probably scripts written by the Board of Directors and Meg Whitman in their spare time.

What the three companies share is, therefore:

  1. Code written by employees and contractors
  2. Code from open source and licensed libraries
  3. Code from companies acquired in moments of great wisdom.

The wrappers each of these companies exposes to its customers and partners make it easy to use the popular programming conventions, recycle structured query language, and exploit reasonably stable Web conventions.

I would suggest that once one looks under the hood of one of these companies’ projects, there will be a world of differences. There is a simple reason or two.

First, some familiar bits and lots of unfamiliar or downright extraterrestrial methods translate to job security and on going consulting work. Who wants to lose a night Oracle DBA job? Not anyone I know.

Second, enterprise software is about customization. I know the yap about enterprise apps, but these apps are little more than customized scripts to allow a hapless marketer with a degree in home economics to pull down a standard report.

I will leave it to you to unravel the mysteries of precognitive analytics and the assertion that HP, Oracle, and SAP are peas in a pod.

Stephen E Arnold, October 1, 2015

Reddit’s Extended Family

October 1, 2015

I have a problem.  I have a Reddit addiction.  My addiction is so bad that I once meant to spend five minutes on the news site, when I ended up spending five hours.  To control my compulsions, I only allow myself to read the first hundred posts and if I have finished my work, the first two hundred.   I am currently in the process to kick the Reddit habit, so I will be a more productive person.  But then I came across this article on Chi-Nese: “20 Great Reddit Alternatives You Should Know.”

Just as I thought I did not have enough Web sites on my RSS feed, now I have these lovely alternatives. Here is the scoop:

“Reddit is the most popular social bookmarking site celebrating 10-year anniversary of existence nowadays. Reddit has accumulated over 16 billion up-votes, over 1 billion comments and over 190 million posts, which are – compared to other Reddit alternatives – enormous numbers.  Despite the fact that Reddit is a website with a massive number of users and posts, below is a list of international Reddit alternatives that have great potential, and are definitely worth a try!”

Most of these Reddit alternatives are in a foreign language (not English), but some of ones to make the list are Hubski, PushedUp, Qetzl,, and 3tags.

I am surprised that Fark did not make the list.  Fark is the “original” Reddit, but it focuses on aggregating outlandish news content.  There goes my productivity!


Whitney Grace, October 1, 2015
Sponsored by, publisher of the CyberOSINT monograph


Data Science: Converging to Confusion

September 30, 2015

I read “Two Great Visualizations about Data Science.” There is not too much reading involved. The article provides images of two graphics.  The more interesting is “another nice picture about the history of big data and data science.”


Note that in the 2010 column, the separate lines of “technology” have converged into what looks to me like a fur ball.

The diagram captures several important ideas.

First, note that Bayes and Bayesian methods have some continuity. Other numerical approaches are important, but that Bayes has created the equivalent of Gorilla Glue.

Second, progress, particularly after 1990, seems to point to visualization. This is, for me, similar to judges awarding a cake with nice looking icing a blue ribbon without tasting the baker’s confection. Appearances are more important than substance.

Third, the end point of the diagram is a circular image which looks like a 1950s atomic diagram from the old Atomic Energy Forum. I think the image looks like a darned confusing diagram.

I think data science and Big Data are more confusing than they were in 2010. The eccentric orbits are becoming more distorted.

Stephen E Arnold, September 30, 2015

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