Candidates as the Top Business Intelligence Providers

November 30, 2015

I read “Top 10 Business Intelligence Software Services.” I must admit that I had heard of only two of the outfits on this list. I noted that there was zero information about the methodology used, who the “experts” were generating the list, and the particular angle each of the companies takes to “business intelligence.”

Here are the 10 outfits identified in the write up:

  • Cyfe
  • GoodData
  • HasOffers
  • Kissmetrics
  • Looker
  • Moz
  • Phocus
  • Salesforce Analytics Cloud
  • Similar Web
  • Tableau

I would suggest there are some other business intelligence vendors you may wish to review. I profile a dozen vendors in CyberOSINT: Next Generation Information Access. None of these outfits in the article made the cut for my study. I do provide information about why certain vendors were selected. I profile another batch of vendors in my forthcoming monograph The Dark Web Dilemma. Again, none of the vendors in the article’s list of 10 “leaders” made it past my researchers’ analyses.

Who cares about Google and In-Q-Tel backed outfits or companies pushing into high value real world applications of intelligence? The answer, gentle reader, is that I do.

Stephen E Arnold, November 30, 2015

Big Data: Some Obvious Issues

November 30, 2015

Imagine this. Information Week writes an article which does not mix up marketing jargon, cheerleading, and wild generalizations. No. It’s true. Es verdad.

Navigate to “Big Data & The Law Of Diminishing Returns.” The write up is a recycling of comments from and Ivory Tower type at Harvard University. Not enough real world experience? Never fear, the poobah is Cathy O’Neil, who worked in the private sector.

Here are her observations as presented by the estimable Information Week real journalists. Note: my observation appears in italics after the words of wisdom.

“The [Big Data] technology is encouraging people to use algorithms they don’t understand.” My question: How many information professionals got an A in math?

“Know what you don’t know. It’s hard.” My question: How does not know oneself if the self is trying to hit one’s numbers and work with concepts about which their information is skewed by Google boosted assumptions about one’s intelligence?

The write up includes this bummer of a statement to the point and click analytics wizards:

“I’d rather have five orthogonal modest data sets than one ginormous data set along a single axis…That is where the law of diminishing returns kicks in.” This is attributed to Caribou Hoenig, a venture capital firm. My question: What is ginormous?

The write up also reveals, without much in the way of questioning the analytic method, that IDC has calculated that the size of the Big Data market will be “$58.6 billion by the end of the year, and it would grow to $101.9 billion by 2019.”

Perhaps clear thinking about data begins with some thinking about where numbers come from, the validity of the data set, and the methods used to figure out the future.

Oh, right. That’s the point of the article. Too bad the write up ignores its own advice. I like that ginormous number in 2019. Yep, clear thinking about data abounds.

Stephen E Arnold, November 30, 2015

Alphabet Google Goes to College

November 30, 2015

I read “Google’s Insidious Shadow Lobbying: How the Internet Giant Is Bankrolling Friendly Academics—and Skirting Federal Investigations.” The write up seems to take a somewhat negative view of alleged Google activities. The clue? The word “insidious.” Put that in your sentiment analysis system, gentle reader.

I learned:

Google’s actions between 2011 and 2013 show how they dodge legal bullets: by molding elite opinion, using the support of experts and academics as a firewall against criticism. The donations to George Mason and professors at other universities reveal that Google purchases that privilege. It’s just one way Google uses its war chest to influence policymakers: they spent $5.47 million on official lobbying in the first quarter of 2015 alone, and spends more money on lobbying than any public company. But the academic funding machine may be even more insidious, a stealth form of lobbying wrapped in the guise of “independent” research. Google has not responded to multiple requests for comment at the time of publication.

Interesting. How does this differ from the funding by commercial enterprises of other activities. You know. There are programs at Ivy League schools with supporters. There are buildings at some universities with the names of folks who represent some interesting outfits.

Give the Alphabet Google thing a break. Salaries at most universities for the hard working researchers do not cover the payments on a Toyota Prius.

From my point of view, I believe almost everything academics write and the contents of commercial online databases charging money for articles for which the hard working researchers have paid to get “published.” I am confident that research results are indeed reproducible. Academics do good work.

Want more data? Just run queries on the GOOG. Precision, recall, and objectivity in abundance.

Stephen E Arnold, November 30, 2015

Kmart Australia Faces Security Breach

November 30, 2015

Oracle’s Endeca and IBM’s Coremetrics were both caught up in a customer-data hack at Kmart Australia, we learn from “Customer Data Stolen in Kmart Australia Hack” at iTnews. Fortunately, it appears credit card numbers and other payment information were not compromised; just names, contact information, and purchase histories were snagged. It seems Kmart Australia’s choice to use a third party to process payments was a wise decision. The article states:

“The retailer uses ANZ Bank’s CyberSource payments gateway for credit card processing, and does not store the details internally. iTnews understands Kmart’s online ecommerce platform is built on IBM’s WebSphere Commerce software. The ecommerce solution also includes the Oracle Endeca enterprise data discovery platform and Coremetrics (also owned by IBM) digital marketing platform, iTnews understands.

The article goes on to report that Kmart Australia has created a new executive position, “head of online trading and customer experience.” Perhaps that choice will help the company avoid such problems in the future. It also notes that the retailer reported the breach voluntarily. Though such reporting is not yet mandatory in Australia, legislation to make it so is expected to be introduced before the end of the year.

Cynthia Murrell, November 30, 2015

Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph

Yandex Takes on Google with Anticompetitive Business Practices

November 30, 2015

Google is the dominate search engine in North America, South America, and Europe.  When it comes to Asia, however, Google faces stiff competition with Yahoo in Japan and Yandex in Russia.  Yandex has been able to hold a firm market share and remains stuff competition for Google.  Reuters says that “Russia’s Yandex Says Complained To EU Over Google’s Android” pointing to how Yandex might be able to one up its competition.

According to the article, Russia has petitioned the European Commission to investigate Google’s practices related to the Android mobile OS.  Yandex has been trying for a long time to dislodge Google’s attempts to gain a stronger market share in Europe and Asia.

“The new complaint could strengthen the case against Google, possibly giving enough ammunition to EU antitrust regulators to eventually charge the company with anti-competitive business practices, on top of accusations related to its Google Shopping service. The formal request was filed in April 2015 and largely mirrors the Russian company’s claims against the U.S. company in a Russian anti-monopoly case that Yandex won.”

The Russian competition watchdog discovered that Google is trying to gain an unfair advantage in the European and Asian search markets.  Yandex is one of the few companies who voices its dislike of Google along with Disconnect, Aptoide, and the FairSearch lobbying group.  Yandex wants the European Commission to restore balance to the market, so that fair competition can return.  Yandex is especially in favor of having mobile device users be able to select their search engine of choice, rather than having one preprogrammed into the OS.

It is interesting to view how competitive business practices take place over seas.  Usually in the United States whoever has the deepest pockets achieves market dominance, but the European Union is proving to uphold a fairer race for search dominance.  Even more interesting is that Google is complaining Yandex is trying to maintain its domiance with these complaints.

 
Whitney Grace, November 30, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph

 

Enterprise Search: A Confused Stew

November 29, 2015

Every culture has a stew. A stew is a mélange of ingredients; for example, tripe, brains, chicken fat, water, rutabaga, etc. Your parental beacon probably used an off-the-shelf product and added grocery store goodies. Heat and serve. Yummy.

I read two articles which make vivid the sad state of enterprise search. I think the experts who cooked up these write ups are doing the best with the ingredients in the fridge. I am not sure about the palatability of the meals the items presage.

Get the Right Search Tool

First, I read “Choosing an Enterprise Search Tool.” Okay, tool, not utility. The word tool does not deliver the calories I need. Let’s move forward. I learned that search has these important ingredients:

  • Text analytics functions, including “entity co reference resolution”. Got it.
  • Pipeline architecture, which means no reprogramming. Love that if it is indeed Grade A chuck.

That’s it. The write up wants me to be sure the solution is scalable. Okay. No problem in cloud land unless there are some pesky contractual and security requirements to keep the system close at hand and in conformance with rules, regulations, and laws.

The notion of security is good. I am all for a secure enterprise search tool. The problem is that security is a slippery fish. Toss it into this mix with the analytics and the pipeline. It will be just fine, a mantra crock pot manufacturers use in their advertisements.

Go for the open standards. None of that Kobe beef technology.

Then the write up enjoins me to do what I think is a pretty tough task in the overheated kitchen under the gaze of the chief financial officer:

you need to make sure you first know what type of information your users will need to find.

I don’t know what information I need until I encounter a problem which I cannot resolve with what I know, have in my archive, or a colleague with hopefully a clue. Where did I put that venison bone? Right, I don’t have a venison bone, and I don’t know anyone who has one. Is there a difference between a cow bone and a deer bone? Help, I need a food centric search system without Yelp and TripAdvisor filters.

I am not sure about this recipe.

Do the Enterprise Search Engine Optimization Thing

The second article I read was “Kickstart Your Enterprise Search Program.” The approach I am urged to follow involves getting on the road to better search. Happy users are important.

To reach this goal:

You need to undertake your own Enterprise SEO, or ESEO. I first wrote about ESEO in 2009, and it’s as relevant today as it was then — and suffers the same lack of tools even now. However, there are methodologies you can use.

How do I do that? It’s as easy as microwaving a burrito. Just look at the search terms the users employ. In my experience, those search terms often mislead when users are hunting for their own documents or tackling a topic about which the user lacks information and vocabulary.

One I know the terms, the rest of the enterprise search task is even easier. I don’t have to puncture the cellophane wrap after microwaving the El Monterey wonder.

Observations

Stew and microwaved SEOs. Enterprise search requires more substantive fare than these two write ups deliver. Little wonder that enterprise search vendors are struggling to find purchase in a business environment looking for Red Bull solutions.

My thought: Do not rely on either of these chefs’ suggestions. Analytics and not precision and recall. SEO and not substantive nutrition? Not for me.

Stephen E Arnold, November 29, 2015

The New Real Journalism: Bezos a WaPo to the Gray Lady

November 29, 2015

I read “Jeff Bezos Says The Washington Post’s Goal Is to Become the New Paper of Record.” As Jack Benny used to say when someone mentioned $1 million, “Yipes.”

My hunch is that the sports at the New York Times probably had other exclamations to share among themselves.

We know that Mr. Bezos seems to have made the overhead reducing cloud computing thing a money maker. We know Mr. Bezos has pulled off a 1950s style rocket ship landing which suggests the visionary inventor of the Tesla has some catching up to do in the space craft landing field. We know Mr. Bezos has lots of money.

I noted this quote, which suggests, he knows his achievement factoids as well:

Well, you know, what we’re doing with the Post is we’re working on becoming the new paper of record, Charlie. We’ve always been a local paper, and just this month The Washington Post passed The New York Times in terms of number of viewers online. This is a gigantic accomplishment for the Post team. We’re just gonna keep after that. The reason that that’s working is because we have such a talented team at the Post. It’s all about quality journalism. And even here in the Internet age, in the 21st century, people really care about quality journalism.

What will the New York Times do? Gee, I don’t know. In the third quarter of 2015, the Gray Lady generated $9 million in profit. What do you think building rockets for fun costs? Probably a lot more than real journalism Bezos style.

Stephen E Arnold, November 29, 2015

Quote to Note: Wolfram on Artificial Intelligence

November 28, 2015

There’s a long interview with Stephen Wolfram in “Interview with Stephen Wolfram on AI and the Future,” which I found when pruning my archives. Here’s one of the quotes I noted:

Recently, computers, and GPUs, and all that kind of thing became fast enough that, really—there are a bunch of engineering tricks that have been invented, and they’re very clever, and very nice, and very impressive, but fundamentally, the approach is 50 years old, of being able to just take one of these neural network–like systems, and just show it a whole bunch of examples and have it gradually learn distinctions between examples, and get to the point where it can, for example, recognize different kinds of objects and images.

Hmm. Half a century. Progress comes from faster chips and clever implementations of well known methods. Interesting.

Enterprise search is also old. Improvements have been slow and seem to be lagging behind other fields. Is it the vendors or is the nature of the problem? The self appointed experts, failed webmasters, and former middle school teachers now working as taxonomy experts are pitching governance, semantics, and assorted packets of artificial butter.

Stephen E Arnold, November 28, 2015

Visualization Tool Round Up

November 28, 2015

Want to make a snappy visualization to impress your manager or a one star general? Navigate to “Top 5 Visualisation Tools” and explore the five recommendations. These systems output some Hollywood-style chart. Just remember to know where a particular data point came from and how the number was produced. Well, if you are briefing a CEO or a four star general, you might not have to stick to close to the facts. Just make each chart shout, “Good news.”

Here are the five systems the write up explains and illustrates:

  • Gephi. Yep, free to use with a couple of caveats
  • Tom Sawyer Perspectives. Not for the Huck Finns eager to kick back on a raft
  • Keylines. You too can do geospatial integration
  • Linkurious. Sharpen your query language skills
  • GraphX. Open source and Spark what could be more wonderful?

PowerPoint away. Just remember to make sure you can answer the question, “Where did that come from?”

Stephen E Arnold, November 28, 2015

Money Laundering: Digital Currency or Old Fashioned Methods?

November 27, 2015

Online is zeros and ones. I worked for a number of years for a fellow with lots of money who explained, “Money is information.” He was mostly correct. However, in the world of big time money laundering, online does not yet have the NFL lineman muscles to do the entire job of keeping financial transactions secret.

The challenge with digital currencies boils down to a search and retrieval problem. Actionable information is embedded in transaction data. Bad actors may not be Bitcoin fans for certain types of unregulated cash transfer tasks.

Navigate to “‘White Gloves,’ ‘VIP Boxes:’ How It’s Done at China’s Underground Banks,” which does a good job of explaining how more traditional money laundering is handled. Bitcoin is okay for moving assets if one has the time, the operational security, and expertise to make the system work.

For folks with JP Morgan-style funds, something more robust and reliable may be needed. Oh, the ability to keep the activity hard to find, hidden from regulators and tax authorities, and reliable is important.

The article states:

In one case Xinhua highlighted this week, state investigators accused a longtime general manager, surnamed Dai, in a state-owned engineering company, Beijing-based China Harbour Engineering, of helping to move $3 million of corruption-tainted gains via a Chinese underground bank onshore. The underground bank used a technique that regulators called an “audit hedge,” essentially depositing 18 million yuan in Mr. Dai’s onshore account in exchange for an equivalent amount of foreign exchange placed in the underground bank’s offshore account. No money crosses the border physically or electronically, making the transaction almost perfectly undetectable — hence “a hedge against audits.”

Another method is an old fave: Shell accounts. The article stated:

In Ningxia, a small northwestern region home to China’s Hui ethnic minority, criminal gangs in the provincial capital Yinchuan set up 12 trading shells that did nothing but generate false export data as a means to move money in or out of the country under the guise of legitimate corporate payments, according to Xinhua. Companies are allowed to move foreign exchange exceeding China’s $50,000 annual limit for legitimate purposes. Police found that the gangs marked the funds that moved through their shell accounts as “national export incentive awards” obtained from the Yinchuan City Bureau of Finance. Investigators alleged that the gangs used the scheme to defraud the Ningxia government since 2013 of export incentives worth 38.6 million yuan ($6 million). Export scams like these usually facilitate illegally moving funds onshore, rather than offshore. China controls foreign exchange coming onshore just as it does money trying to move offshore. The Ningxia case stemmed from 2013, when China was experiencing a high level of net capital inflows.

When will digital currencies facilitate money laundering on this supersized scale? Not surprisingly, verifiable data about the volume of money laundering via digital currencies is tough to obtain.

I would point out that old fashioned methods still have their use. Investigators, therefore, have to rely on useful software like Maltego and add ins and have the resources to dig out information the old fashioned way. This is not just feet on the street; it is humans pulling information threads.

Stephen E Arnold, November 27, 2015

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