September 29, 2014
In 2012 and 2013, IDC sold my content with my name and Dave Schubmehl’s. These were nifty IDC “official” reports. The only hitch in the git along is that IDC did not trouble itself to issue a contract, get my permission, or tell me what they were doing with research my team prepared. The deal was witnessed by a law librarian, and I have a stack of emails about my research into such open source companies as Attivio, ElasticSearch (one of the disruptors of the enterprise search market), IBM (the subject of the IDC twit storm), Lucid Imagination (now Lucid Works which I write when I feel playful as Lucid works, really?), and eight other companies.
Hit by a twit storm. Rough seas ahead. Image from www.qsl.net.
In 2012, I had the open source research. IDC wanted the open source content to use in a monograph. So in front of a law librarian, IDC’s search “expert” thought the exchange of my information for open source intelligence, money, and stuff to sell was a great idea. (I have a file of email from IDC to me about what IDC wanted, but I never got a contract. But IDC had my research. Ah, those administrative delays.) IDC, however, was organized enough to additions to my company research like an open source industry overview.
In an odd approach to copyright, IDC did not produce a contract but it produced reports about four open source companies. Mr. Schubmehl and IDC just went about producing what were recycled company reports and trying to sell them at $3,500 a whack. Is that value or an example of the culture of narcissism? It may come as a surprise to you, gentle reader, but I sell research for money. I have a business model and it has worked for about 40 years. When an outfit uses the research without issuing a contract, I have to start thinking about such issues as fairness, integrity, copyright, and name surfing. Call me idiosyncratic, but when my name is used without my permission, I wonder how a big and allegedly respected organization can operate like a BearStearns-type senior executive.
Then, the straw that broke the proverbial camel’s back, a librarian told me that IDC was selling a report with my name and Mr. Schubmehl’s on Amazon. Wow, Amazon, the Wal-Mart for the digital age. The reports, now removed from Amazon’s blue light special shelf cost $3,500. Not bad for eight pages of information based on my year long research investment into the wild and volatile world of open source search and content processing. Surf’s up for Mr. Schubmehl.
Well, IDC after some prodding by my very gentle legal gerbil stopped selling my work. We received a proposal that offered me a pittance for a guarantee that I would not talk or write about this name surfing, unauthorized resale of my information on Amazon, and the flubs of Mr. Schubmehl.
My legal gerbil rejected IDC’s lawyer crafted “deal,” and I am now converting my IDC misadventure into a metaphor for some of the deeper issues associated with “experts” and certain professional services firms. My legal gerbil suggested a significantly higher fee, but, like many of that ilk, the gerbil broke my heart.
Hence, IDC and Mr. Schubmehl’s tweets and twit storm are on my fragile ship’s radar. Let’s review the IBM IDC Schubmehl twit storm on just one day in September 2014. Trigger warning: Do not emulate the IDC Schubmehl method for your content marketing program. One day of tweets only generates a lot of twit.
Now to the Twit Storm Unleashed on September 16, 2014
Using my Overflight system, I monitor IDC tweets. Quite an interesting series of tweets appears on September 16, 2014. Mr. Schubmehl posted 25 tweets about IBM Watson.
Here are three examples of the Watson content content to which his name was attached::
- September 16, 2014.
#WatsonAnalytics uses Watson cognitive technologies to ingest structured data and find relationships – Robin Grosset & Dan Wolfson
- September 16, 2014 Combo of cognitive with cloud analytics improves process, analysis and decision making – cognitive will change all mkts
- September 16, 2014
#WatsonAnalytics will be using a freemium model….first time for IBM…
Obviously there is nothing wrong with a tweet about an IBM product. What’s one more twit emission in a flow of several hundred thousand 144 character text outputs.
There is nothing illegal with two dozen tweets about IBM. What two dozen tweets do is make me laugh and see this content marketing effort as fodder for corporate weirdness.
Also, this IBM twit storm is not on the Miley Cyrus or Lady Gaga scale, but it is notable because it is a one day twit storm quite unlike the Jeopardy journey. Quite a marketing innovation: getting an alleged “expert” to craft 16 “original” tweets in one day and issue seven retweets of tweets from others who are fans of Big Blue. A few Schubmehl tweets on the 16th illustrated diversity; for example, “The FBI’s Facial Recognition System Is Here.” Hmm. The FBI and facial recognition. I wonder why one is interested in this development.
The terms mentioned in these IBM centric tweets on September 16, 2014, reveal the marketing jargon that IBM is using to generate revenue from the game show winning technology. My list of buzzwords from the tweets read like a who’s who of blogosphere and venture oriented yak:
- Automated data cleansing
- Analytics (cloud based)
- Big Data
- Cognitive (system and capabilities)
- Data explorer
- Natural Language Computing
- Natural Language Query.
From this list of buzzwords my favorites are “cognitive,” “Big Data,” and the number one silly word “Freemium.” Imagine. Freemium from IBM. Imagine.
My Interpretation of the Twit Storm
Let me capture several preliminary observations:
First, the Schubmehl Twitter activity on September 16, 2014 focuses mostly on IBM’s challenged Watson business development effort. The cluster of tweets on the 16th suggest a somewhat ungainly and down-market content marketing play.
Did Mr. Schubmehl wake up on the 16th of September and decide to crank out Watson centric tweets? Did IBM pay IDC and Mr. Schubmehl to do some content marketing like thousands of PR firms do each day? We even have these outfits in Harrod’s Creek, Kentucky to flog auto sales, bourbon, and cheesy festivals in Middletown, Kentucky.
Here’s a question: “How many tweets does a McKinsey or Bain type of consulting firm issue on a single day for a single product that seems to be struggling for revenue?” If you know, please, use the comments section of this blog to provide some factoids.
Second, the tweets provide the reader with a list of what seem to be IBM Watson aficionados or employees who have the job of making the shotgun marriage of open source code, legacy Almaden technology, and proprietary scripts into a billion dollar revenue producer soon, very soon, gentle reader. The individuals mentioned in the September 16, 2014, tweets include:
- Steve Gold, Baylor University
- Robin Grosset, Distinguished engineer Watson Analytics.
- Dan Wolfson, IBM Distinguished Engineer
- Bob Picciano, Senior vice president, IBM information and analytics group.
Perhaps Mr. Gold is objective? I ask, “Do the other three IBM wizards looking at the world through IBM tinted spectacles when reading their business objectives for the current fiscal year?” I asked myself, “Should I trust these individuals who presumably are also “experts” in all things related to Watson?” My preliminary answer is, “Not for an objective view of the game show winning Watson.”
Third, what’s the payoff of this twit storm for IBM? Did IBM expect me to focus on the Schubmehl twit storm and convert the information into my idea of a 10 minute stand up comedy routine to deliver at the upcoming intelligence and law enforcement conference in nine days? Is it possible that “doing social media” looks good on a weekly report when an executive does not have juicy revenue numbers to present? The value of the effort strikes me as modest. In fact, viewed as a group, the tweets could be interpreted as a indicator of IBM’s slide into desperation marketing?
What about consulting firms and their ability to pump out high margin revenue?
Outfits like Gerson Lehrman Group have put the squeeze on mid tier consulting firms. The bottom feeders with its middle school teacher and poet contingent are not likely to sell to the IBMs of the world. GLG types companies are also nipping at the low end business of the blue chip outfits like Bain, Boston Consulting, and even McKinsey.
Put GLG can deliver to a client retired professionals from blue chip firms and on point experts. As a result, GLG has made life very, very tough for the mid tier outfits. Why pay $50,000 for an unproven “expert” when you can buy a person with a pedigree for an hour and pay a few hundred bucks when you need a factoid or an opinion? I consider IDC’s move to content marketing indicative of a fundamental shift in the character of a consulting firm’s business. The shift to low level PR work seems out of character for a professionals services with a commitment to intellectual rigor.
Every few days I learn that something called TopSEOs.com generates a list of content marketing leaders. Will IDC appear on this list?
For those who depend on lower- or mid tier consulting firms for professional counsel, how would you answer these questions:
- What is the intellectual substance behind pronouncements? Is there original research underpinning pronouncements and projections, or are the data culled from secondary sources and discussions with paying customers?
- What is the actual relationship between a mid tier consulting firm and the companies discussed in “authoritative” reports? Are these reports and projects inclusions (a fancy word for ads) or are they objective discussions of companies?
- Are the experts presented as “experts” actually experts or are they individuals who want to hit revenue goals while keeping costs as low as possible?
I don’t have definitive answers to these questions. Perhaps one day I can use a natural language query to tap into Big Data and rely on cognitive methods to provide answers.
For now, a one day twit storm is a wonderful example of how not to close deals, build reputations, and stimulate demand for advanced technology offered via a “Freemium” model. What the heck does that mean anyway?
Stephen E Arnold, September 29, 2014
September 24, 2014
Check out the presentation “The Surprising Path to a Faster NYTimes.com.”
I was surprised at some of the information in the slide deck. First, I thought the New York Times was first online in the 1970s via LexisNexis.
This is not money. See http://bit.ly/1rus9y8
I thought that was an exclusive deal and reasonably profitable for both LexisNexis and the New York Times. When the newspaper broke off that exclusive to do its own thing, the revenue hit on the New York Times was immediate. In addition, the decision had significant cost implications for the newspaper.
The New York Times needed to hire people who allegedly create an online system. The newspaper had to license software, write code, hire consultants, maintain computers not designed to set type and organize circulation. The New York Times had to learn on the fly about converting content for online content processing. Learning that one does not know anything after thinking one knew everything is a very, very inefficient way to get into the online business. In short, the blow off of the LexisNexis deal added significant initial and then ever increasing on-going costs to the New York Times Co. I don’t think anyone at the New York Times has ever sat down to figure out the cost of that decision to become the Natty Bumpo of the newspaper publishing world.
I had heard that the newspaper raked in the 1970s seven figures a year while LexisNexis did the heavy lifting. Yep, that included figuring out how to put the newspaper content on tape into a suitable form for LexisNexis’ mainframe system. Figuring this out inside the New York Times in the early 1990s made this sound: Crackle, crackle, whoosh. That is the sound of a big company burning money not for a few months but for DECADES, folks. DECADES.
Photo from US Fish and Wildlife.
When the newspaper decided that it could do an online service itself and presumably make more money, the newspaper embarked on the technical path discussed in the slide deck. Few recall that the fellow who set up the journal Online worked on the online version of the newspaper. I recall speaking to that person shortly after he and the newspaper parted ways. He did not seem happy with budgets, technology, or vision. But, hey, that was decades ago.
How some information companies solve common problems with new tools. Image thanks to Enlgishrussia.com at http://bit.ly/1ps0MPF.
In the slide deck, we get an insider’s view of trying to deal with the problem of technical decisions made decades ago. What’s interesting is that the cost of the little adventure by the newspaper does not reflect the lost revenue from the LexisNexis exclusive. The presentation does illustrate quite effectively how effort cannot redress technical decisions made in the past.
This is an infrastructure investment problem. Unlike a physical manufacturing facility, an information centric business is difficult to re-engineer. There is the money problem. It costs a lot to rip and replace or put up a new information facility and then cut it over when it is revved and ready. But information centric businesses have another problem. Most succeed by virtue of luck. The foundation technology is woven into the success of the business, but in ways that are often non replicable.
The New York Times killed off the LexisNexis money flow. Then it had to figure out how to replicate that LexisNexis money flow and generate a bigger profit. What happened? The New York Times spent more money creating the various iterations of the Times Online, lost the LexisNexis money, and became snared in the black hole of trying to figure out how to make online information generate lots of dough. I am suggesting that the New York Times may be kidding itself with the new iteration of the Times Online service.
September 1, 2014
Last week I had a conversation with a publisher who has a keen interest in software that “knows” what content means. Armed with that knowledge, a system can then answer questions.
The conversation was interesting. I mentioned my presentations for law enforcement and intelligence professionals about the limitations of modern and computationally expensive systems.
Several points crystallized in my mind. One of these is addressed, in part, in a diagram created by a person interested in machine learning methods. Here’s the diagram created by SciKit:
The diagram is designed to help a developer select from different methods of performing estimation operations. The author states:
Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
First, notice that there is a selection process for choosing a particular numerical recipe. Now who determines which recipe is the right one? The answer is the coding chef. A human exercises judgment about a particular sequence of operation that will be used to fuel machine learning. Is that sequence of actions the best one, the expedient one, or the one that seems to work for the test data? The answer to these questions determines a key threshold for the resulting “learning system.” Stated another way, “Does the person licensing the system know if the numerical recipe is the most appropriate for the licensee’s data?” Nah. Does a mid tier consulting firm like Gartner, IDC, or Forrester dig into this plumbing? Nah. Does it matter? Oh, yeah. As I point out in my lectures, the “accuracy” of a system’s output depends on this type of plumbing decision. Unlike a backed up drain, flaws in smart systems may never be discerned. For certain operational decisions, financial shortfalls or the loss of an operation team in a war theater can be attributed to one of many variables. As decision makers chase the Silver Bullet of smart, thinking software, who really questions the output in a slick graphic? In my experience, darned few people. That includes cheerleaders for smart software, azure chip consultants, and former middle school teachers looking for a job as a search consultant.
Second, notice the reference to a “rough guide.” The real guide is understanding of how specific numerical recipes work on a set of data that allegedly represents what the system will process when operational. Furthermore, there are plenty of mathematical methods available. The problem is that some of the more interesting procedures lead to increased computational cost. In a worst case, the more interesting procedures cannot be computed on available resources. Some developers know about N=NP and Big O. Others know to use the same nine or ten mathematical procedures taught in computer science classes. After all, why worry about math based on mereology if the machine resources cannot handle the computations within time and budget parameters? This means that most modern systems are based on a set of procedures that are computationally affordable, familiar, and convenient. Does this similar of procedures matter? Yep. The generally squirrely outputs from many very popular systems are perceived as completely reliable. Unfortunately, the systems are performing within a narrow range of statistical confidence. Stated in a more harsh way, the outputs are just not particularly helpful.
In my conversation with the publisher, I asked several questions:
- Is there a smart system like Watson that you would rely upon to treat your teenaged daughter’s cancer? Or, would you prefer the human specialist at the Mayo Clinic or comparable institution?
- Is there a smart system that you want directing your only son in an operational mission in a conflict in a city under ISIS control? Or, would you prefer the human-guided decision near the theater about the mission?
- Is there a smart system you want managing your retirement funds in today’s uncertain economy? Or, would you prefer the recommendations of a certified financial planner relying on a variety of inputs, including analyses from specialists in whom your analyst has confidence?
When I asked these questions, the publisher looked uncomfortable. The reason is that the massive hyperbole and marketing craziness about fancy new systems creates what I call the Star Trek phenomenon. People watch Captain Kirk talking to devices, transporting himself from danger, and traveling between far flung galaxies. Because a mobile phone performs some of the functions of the fictional communicator, it sure seems as if many other flashy sci-fi services should be available.
Well, this Star Trek phenomenon does help direct some research. But in terms of products that can be used in high risk environments, the sci-fi remains a fiction.
Believing and expecting are different from working with products that are limited by computational resources, expertise, and informed understanding of key factors.
Humans, particularly those who need money to pay the mortgage, ignore reality. The objective is to close a deal. When it comes to information retrieval and content processing, today’s systems are marginally better than those available five or ten years ago. In some cases, today’s systems are less useful.
August 5, 2014
I have mentioned recent “expert analyses” of the enterprise search and content marketing sector. In my view, these reports are little more than gussied up search engine optimization (SEO), content marketing plays. See, for example, this description of the IDC report about “knowledge quotient”. Sounds good, right. So does most content marketing and PR generated by enterprise search vendors trying to create sustainable revenue and sufficient profits to keep the investors on their boats, in their helicopters, and on the golf course. Disappointing revenues are not acceptable to those with money who worry about risk and return, not their mortgage payment.
Some content processing vendors are in need of sales leads. Others are just desperate for revenue. The companies with venture money in their bank account have to deliver a return. Annoyed funding sources may replace company presidents. This type of financial blitzkrieg has struck BA Insight and LucidWorks. Other search vendors are in legal hot water; for example, one Fast Search & Transfer executive and two high profile Autonomy Corp. professionals. Other companies tap dance from buzzword to catchphrase in the hopes of avoiding the fate of Convera, Delphes, or Entopia. The marketing beat goes on, but the revenues for search solutions remains a challenge. How will IBM hit $10 billion in Watson revenues in five or six years? Good question, but I know the answer. Perhaps accounting procedures might deliver what looks like a home run for Watson. Perhaps the Jeopardy winner will have to undergo Beverly Hills-style plastic surgery? Will the new Watson look like today’s Watson? I would suggest that some artificiality could be discerned.
Last week, one of my two or three readers wrote to inform me that the phrase “knowledge quotient” is a registered trademark. One of my researchers told me that when one uses the phrase “knowledge quotient,” one should include the appropriate symbol. Omission can mean many bad things, mostly involving attorneys:
Another one of the goslings picked up the vaporous “knowledge quotient” and poked around for other uses of the word. Remember. I encountered this nearly meaningless quasi academic jargon in the title of an IDC report about content processing, authored by the intrepid expert Dave Schubmehl.
According to one of my semi reliable goslings, the phrase turned up in a Portland State University thesis. The authors were David Clitheroe and Garrett Long.
The trademark was registered in 2004 by Penn State University. Yep, that’s the university which I associate with an unfortunate management “issue.” According to Justia, the person registering the phrase “knowledge quotient” was a Penn State employee named Gene V J Maciol.
So we are considering a chunk of academic jargon cooked up to fulfill a requirement to get an advanced degree in sociology in 1972. That was about 40 years ago. I am not familiar with sociology or the concept knowledge quotient.
I printed out the 111 page document and read it. I do have some observations about the concept and its relationship to search and content processing. Spoiler alert: Zero, none, zip, nada, zilch.
The topic of the sociology paper is helping kids in trouble. I bristled at the assumptions implicit in the write up. Some cities had sufficient resources to help children. Certain types of faculties are just super. I assume neither of the study’s authors were in a reformatory, orphanage, or insane asylum.
Anyway the phrase “knowledge quotient” is toothless. It means, according to page 31:
the group’s awareness and knowledge of the [troubled youth or orphan] home.
And the “quotient” part? Here it is in all its glory:
A knowledge quotient reflects the group’s awareness and knowledge of the home.
August 2, 2014
Editor’s note: These three companies are involved in search and content processing. The opinion piece considers the question, “Is management unable to ensure standard business processes working in some businesses today?” Links have been inserted to open source information that puts some of the author’s comments in context. Comments about this essay may be posted using the Comments function for this blog.
Forgetting to Put Postage on Lots of Letters
I read “HP to Pay $32.5 Million to Settle Claims of Overbilling USPS.” (Keep in mind you may have to pony up some cash to access this article. Mr. Murdoch needs cash to buy more media properties. Do your part!)
The main point of the story, told by “real” journalists, is that the company failed “to comply with pricing terms.” The “real” news story asserts:
The DOJ also alleged H-P made misrepresentations during the negotiation of the contract with the USPS regarding its pricing and its plans to ensure it would provide the required most favored customer pricing.
I suppose any company can overlook putting postage on an envelope. When that happened to me in my day of snail mail activity, my local postmistress Claudette would give me a call and I would go to the Harrod’s Creek post office and buy a stamp.
I am no big time manager, but I understood that snail mail required a stamp. If you are a member of the House or Senate, the rules are different, but even the savvy Congressperson makes sure the proper markings appear on the absolutely essential missives.
My mind, which I admit is not as agile as it was when I worked at Halliburton Nuclear Utility Services, drew a dotted line between this seemingly trivial matter of goofing on an administrative procedure and the fantastic events still swirling around Hewlett Packard’s purchase of Autonomy, a vendor of search and content processing software.
A number of questions flapped slowly across my mind:
- Is HP management becoming careless with trivial matters like paying $11 billion for a company generating about $800 million in revenue and forgetting to pay the US post office?
- Is the thread weaving together such HP events as the mobile operating system affair, the HP tablet, the fumbling of the Alta Vista opportunity, and the apparent administrative goofs like the Autonomy purchase and this alleged postage stamp licking flawed administrative processes?
- What does the stamp sticking, Autonomy litigating, and alleged eavesdropping say about the company’s “git ‘er done” approach?
The attitude may apply to confident senior managers with incentives to produce revenue. Image source: http://profileengine.com/groups/profile/420722222/larry-the-cable-guy-for-president
I don’t think too much about Hewlett Packard. I do wonder if HP is an isolated actor or if companies with search interests are focusing on priorities that seem to be orthogonal to what I understand to be appropriate corporate behavior. One isolated event is highly suggestive.
But what do similar events suggest? In this short essai, I want to summarize two events. Both of these are interesting. For me, I see a common theme connecting the HP stamp licking and the two macro events. The glue fixing these in my mind is what seems to be a failure of management to pay attention to details.
But first, let’s go back in time for a modest effort penned by Edmund Spenser.
July 31, 2014
At lunch yesterday, several search aware people discussed a July 2014 Gartner study. One of the folks had a crumpled image of the July 2014 “magic quadrant.” This is, I believe, report number G00260831. Like other mid tier consulting firms, Gartner works hard to find something that will hook customers’ and prospects’ attention. The Gartner approach is focused on companies that purport to have enterprise search systems. From my vantage point, the Gartner approach is miles ahead of the wild and illogical IDC report about knowledge, a “quotient,” and “unlocking” hidden value. See http://bit.ly/1rpQymz. Now I have not fallen in love with Gartner. The situation is more like my finding my content and my name for sale on Amazon. You can see what my attorney complained about via this link, http://bit.ly/1k7HT8k. I think I was “schubmehled,” not outwitted.
I am the really good looking person. Image source: http://bit.ly/1rPWjN3
What the IDC report lacks in comprehensiveness with regard to vendors, Gartner mentions quite a few companies allegedly offering enterprise search solutions. You must chase down your local Garnter sales person for more details. I want to summarize the points that surfaced in our lunch time pizza fest.
First, the Gartner “study” includes 18 or 19 vendors. Recommind is on the Gartner list even though a supremely confident public relations “professional” named Laurent Ionta insisted that Recommind was not in the July 2014 Gartner report. I called her attention to report number G00260831 and urged her to use her “bulldog” motivation to contact her client and Gartner’s experts to get the information from the horse’s mouth as it were. (Her firm is www.lewispr.com and its is supported to be the Digital Agency of the Year and on the Inc 5000 list of the fastest growing companies in America.) I am impressed with the accolades she included in her emails to me. The fact that this person who may work on the Recommind account was unaware that Gartner pegged Recommind as a niche player seemed like a flub of the first rank. When it comes to search, not even those in the search sector may know who’s on first or among the chosen 19.
To continue with my first take away from lunch, there were several companies that those at lunch thought should be included in the Gartner “analysis.” As I recall, the companies to which my motley lunch group wanted Gartner to apply their considerable objective and subjective talents were:
- ElasticSearch. This in my view is the Big Dog in enterprise search at the moment. The sole reason is that ElasticSearch has received an injection of another $70 million to complement the $30 odd million it had previously gather. Oh, ElasticSearch is a developer magnet. Other search vendors should be so popular with the community crowd.
- Oracle. This company owns and seems to offer Endeca solutions along with RightNow/InQuira natural language processing for enterprise customer support, the fading Secure Enterprise Search system, and still popping and snapping Oracle Text. I did not mention to the lunch crowd that Oracle also owns Artificial Linguistics and Triple Hop technology. This information was, in my view, irrelevant to my lunch mates.
- SphinxSearch. This system is still getting love from the MySQL contingent. Imagine no complex structured query language syntax to find information tucked in a cell.
There are some other information retrieval outfits that I thought of mentioning, but again, my free lunch group does not know what it does not know. Like many folks who discuss search with me, learning details about search systems is not even on the menu. Even when the information is free, few want to confuse fantasy with reality.
The second take away is that rational for putting most vendors in the niche category puzzled me. If a company really has an enterprise search solution, how is that solution a niche? The companies identified as those who can see where search is going are, as I heard, labeled “visionaries.” The problem is that I am not sure what a search visionary is; for example, how does a French aerospace and engineering firm qualify as a visionary? Was HP a visionary when it bought Autonomy, wrote off $8 billion, and initiated litigation against former colleagues? How does this Google supplied definition apply to enterprise search:
able to see visions in a dream or trance, or as a supernatural apparition?
The final takeaway for me was the failure to include any search system from China, Germany, or Russia. Interesting. Even my down on their heels lunch group was aware of Yandex and its effort in enterprise search via a Yandex appliance. Well, internationalization only goes so far I suppose.
I recall hearing one of my luncheon guests say that IBM was, according the “experts” at Gartner, a niche player.Gentle reader, I can describe IBM many ways, but I am not sure it is a niche player like Exorbyte (eCommerce mostly) and MarkLogic (XML data management). Nope, IBM’s search embraces winning Jeopardy, creating recipes with tamarind, and curing assorted diseases. And IBM offers plain old search as part of DB2 and its content management products plus some products obtained via acquisition. Cybertap search, anyone? When someone installs, what used to be OmniFind, I thought IBM was providing an enterprise class information retrieval solution. Guess I am wrong again.
Net net: Gartner has prepared the ground for a raft of follow on analyses. I would suggest that you purchase a copy of the July 2014 Gartner search report. You may be able to get your bearings so you can answer these questions:
- What are the functional differences among the enterprise search systems?
- How does the HP Autonomy “solution” compare to the pre-HP Autonomy solution?
- What is the cost of a Google Search Appliance compared to a competing product from Maxxcat or Thunderstone? (Yep, two more vendors not in the Gartner sample.)
- What causes a company to move from being a challenger in search to a niche player?
- What makes both a printer company and a Microsoft-centric solution qualified to match up with Google and HP Autonomy in enterprise search?
- What are the licensing costs, customizing costs, optimizing costs, and scaling costs of each company’s enterprise search solution? (You can find the going rate for the Google Search Appliance at www.gsaadvantage.gov. The other 18? Good luck.)
I will leave you to your enterprise search missions. Remember. Gartner, unlike some other mid-tier consulting firms, makes an effort to try to talk about what its consultants perceive as concrete aspects of information retrieval. Other outfits not so much. That’s why I remain confused about the IDC KQ (knowledge quotient) thing, the meaning of hidden value, and unlocking. Is information like a bike padlock?
Stephen E Arnold, July 31, 2014
July 28, 2014
Shortly after writing the first draft of Google: The Digital Gutenberg, “Enterprise Findability without the Complexity” became available on the Google Web site. You can find this eight page polemic at http://bit.ly/1rKwyhd or you can search for the title on—what else?—Google.com.
Six years after the document became available, Google’s anonymous marketer/writer raised several interesting points about enterprise search. The document appeared just as the enterprise search sector was undergoing another major transformation. Fast Search & Transfer struggled to deliver robust revenues and a few months before the Google document became available, Microsoft paid $1.2 billion for what was another enterprise search flame out. As you may recall, in 2008, Convera was essentially non operational as an enterprise search vendor. In 2005, Autonomy bought the once high flying Verity and was exerting its considerable management talent to become the first enterprise search vendor to top $500 million in revenues. Endeca was flush with Intel and SAP cash, passing on other types of financial instruments due to the economic downturn. Endeca lagged behind Autonomy in revenues and there was little hope that Endeca could close the gap between it and Autonomy.
Secondary enterprise search companies were struggling to generate robust top line revenues. Enterprise search was not a popular term. Companies from Coveo to Sphinx sought to describe their information retrieval systems in terms of functions like customer support or database access to content stored in MySQL. Vivisimo donned a variety of descriptions, culminating in its “reinvention” as a Big Data tool, not a metasearch system with a nifty on the fly clustering algorithm. IBM was becoming more infatuated with open source search as a way to shift development an bug fixes to a “community” working for the benefit of other like minded developers.
Google’s depiction of the complexity of traditional enterprise search solutions. The GSA is, of course, less complex—at least on the surface exposed to an administrator.
Google’s Findability document identified a number of important problems associated with traditional enterprise search solutions. To Google’s credit, the company did not point out that the majority of enterprise search vendors (regardless of the verbal plumage used to describe information retrieval) were either losing money or engaged in a somewhat frantic quest for financing and sales).
Here are the issues Google highlighted:
- User of search systems are frustrated
- Enterprise search is complex. Google used the word “daunting”, which was and still is accurate
- Few systems handle file shares, Intranets, databases, content management systems, and real time business applications with aplomb. Of course, the Google enterprise search solution does deliver on these points, asserted Google.
Furthermore, Google provides integrated search results. The idea is that structured and unstructured information from different sources are presented in a form that Google called “integrated search results.”
Google also emphasized a personalized experience. Due to the marketing nature of the Findability document, Google did not point out that personalization was a feature of information retrieval systems lashed to an alert and work flow component. Fulcrum Technologies offered a clumsy option for personalization. iPhrase improved on the approach. Even Endeca supported roles, important for the company’s work at Fidelity Investments in the UK. But for Google, most enterprise search systems were not personalizing with Google aplomb.
Google then trotted out the old chestnuts gleaned from a lunch discussion with other Googlers and sifting competitors’ assertions, consultants’ pronouncements, and beliefs about search that seemed to be self-evident truths; for example:
- Improved customer service
- Speeding innovation
- Reducing information technology costs
- Accelerating adoption of search by employees who don’t get with the program.
Google concluded the Findability document with what has become a touchstone for the value of the Google Search Appliance. Kimberly Clark, “a global health and hygiene company,” reduced administrative costs for indexing 22 million documents. The costs of the Google Search Appliance, the consultant fees, and the extras like GSA fail over provisions were not mentioned. Hard numbers, even for Google, are not part of the important stuff about enterprise search.
One interesting semantic feature caught my attention. Google does not use the word knowledge in this 2008 document.
- Was Google unaware of the fusion of information retrieval and knowledge?
- Does the Google Search Appliance deliver a laundry list of results, not knowledge? (A GSA user has to scan the results, click on links, and figure out what’s important to the matter at hand, so the word “knowledge” is inappropriate.)
- Why did Google sidestep providing concrete information about costs, productivity, and the value of indexing more content that is allegedly germane to a “personalized” search experience? Are there data to support the implicit assertion “more is better.” Returning more results may mean that the poor user has to do more digging to find useful information. What about a few, on point results? Well, that’s not what today’s technology delivers. It is a fiction about which vendors and customers seem to suspend disbelief.
With a few minor edits—for example, a genuflection to “knowledge—this 2008 Findability essay is as fresh today as it was when Google output its PDF version.
First, the freshness of the Findability paper underscores the staleness and stasis of enterprise search in the past six years. If you scan the free search vendor profiles at www.xenky.com/vendor-profiles, explanations of the benefits and functions of search from the 1980s are also applicable today. Search, the enterprise variety, seems to be like a Grecian urn which “time cannot wither.”
Second, the assertions about the strengths and weaknesses of search were and still are presented without supporting facts. Everyone in the enterprise search business recycles the same cant. The approach reminds me of my experience questioning a member of a sect. The answer “It just is…” is simply not good enough.
Third, the Google Search Appliance has become a solution that costs as much, if not more, than other big dollar systems. Just run a query for the Google Search Appliance on www.gsaadvantage.gov and check out the options and pricing. Little wonder than low cost solutions—whether they are better or worse than expensive systems—are in vogue. Elasticsearch and Searchdaimon can be downloaded without charge. A hosted version is available from Qbox.com and is relatively free of headaches and seven figure charges.
Net net: Enterprise search is going to have to come up with some compelling arguments to gain momentum in a world of Big Data, open source, and once burned twice shy buyers. I wonder why venture / investment firms continue to pump money into what is same old search packaged with decades old lingo.
I suppose the idea that a venture funded operation like Attivio, BA Insight, Coveo, or any other company pitching information access will become the next Google is powerful. The problem is that Google does not seem capable of making its own enterprise search solution into another Google.
This is indeed interesting.
Stephen E Arnold, July 28, 2014
July 24, 2014
“Myths and Misreporting About Malaysia Airlines Flight 17” is an interesting article. I found the examples of misinformation, disinformation, and reformation thought provoking. The write up spotlights a few examples of fake or distorted information about an airline’s doomed flight.
As i considered the article and its appearance in a number of news alerting services, I shifted from the cleverness of the content to a larger and more interesting issue. From the revelations about software that can alter inputs to an online survey (see this link) to fake out “real” news, determining what’s sort of accurate from what’s totally bogus is becoming more and more difficult. I have professional researchers, librarians, and paralegals at my disposal. Most people do not. No longer surprising to me is the email from one of the editors working to fact check my for fee columns. The questions range from “Did IBM Watson invent a recipe with tamarind in its sauce?” to “Do you have a source for the purchase price of Vivisimo?” Now I include online links for the facts and let the editors look up my source without the intermediating email. Even then, there is a sense of wonderment when an editor expresses surmise that what he or she believed is, in fact, either partially true, bogus, or unexpected. Example: “Why do French search vendors feel compelled to throw themselves at the US market despite the historically low success rates?” The answer is anchored in [a] French tax regulations, [b] French culture, particularly when a scruffy entrepreneur from the wrong side of the educational tracks tries to connect with a French money source from the right side of the educational tracks, [c] the lousy financial environment for certain high technology endeavors, and [d] selling to the big US markets looks like a slam dunk, at least for a while.
The reason for the disconnect between factoids and information manipulation boils down to a handful of factors. Let me highlight several:
First, the need for traffic to Web sites (desktop, mobile, app instances, etc.) is climbing up the hierarchy of business / personal needs. You want traffic today? The choices are limited. Pay Google $25,000 or more a month. Pay an SEO (search engine optimization “expert” whatever you can negotiate. Create content, do traditional marketing, and trust that the traffic follows the “if you build it they will come” pipedream. Most folks just whack at getting traffic and use increasingly SEOized headlines as a low cost way of attracting attention. Think headlines from the National Enquirer in the 1980s.
Second, Google has to pump lots of money into plumbing, infrastructure, moon shots, operational costs (three months at the Stanford Psych unit, anyone?) At the same time, mobile is getting hot. Two problems plague the sunny world of the GOOG. [a] Revenue from mobile ads is less than from traditional ads. Therefore, Google has to find a way to keep that 2006 style revenue flowing. Because there is a systemic shift, the GOOG needs money. One way to get it is to think about Adwords as a machine that needs tweaking. How does one sell Adwords to those who do not buy enough today? You ponder the question, but it involves traffic to a Web site. [b] Google gets bigger so the “think cheap” days of yore are easier to talk about than deliver. A 15 year old company is getting more and more expensive to run. The upcoming battles with Amazon and Samsung will not be cheap. The housing developments, the Loon balloons, and the jet fleet, smart people, and other oddments of the company—money pits. If the British government can fiddle traffic, is it possible that others have this capability too?
Third, marketing, an easy whipping boy or girl as the case may be. After spending lots and lots on Web sites and apps, some outfits’ CFOs are asking, “What do we get for this spending?” In order to “prove” their worth and stop the whipping, marketers have kicked into overdrive. Baloney, specious, half baked, crazy, and recycled content is generated by the terabyte drive. The old fashioned ideas about verification, accuracy, and provenance are kicked to the side of the road.
Net net: running a query on a search engine, accepting the veracity of a long form article, or just finding out what happened at an event is very difficult. The fixes are not palatable to some people. Others are content to believe that their Internet or Internet search engine dispenses wisdom like the oracle at Delphi. Who knew the “oracles” relied on confusing entrances, various substances, and stage tricks to get their story across.
We now consult digital Delphis. How is that working out when you search for information to address a business problem, find a person who can use finger manipulation to relax a horse’s muscle, or determine if a company is what its Web site says it is?
Stephen E Arnold, July 24, 2014
July 10, 2014
Editor’s Note: This is information that did not make Stephen E Arnold’s bylined article in Information Today. That forthcoming Information Today story about French search and content processing companies entering the US market. Spoiler alert: The revenue opportunities and taxes appear to be better in the US than in France. Maybe a French company will be the Next Big Thing in search and content processing. Few French companies have gained significant search and retrieval traction in the US in the last few years. Arguably, the most successful firm is the image recognition outfit called A2iA. It seems that French information retrieval companies and the US market have been lengthy, expensive, and difficult. One French company is trying a different approach, and that’s the core of the Information Today story.)
In 1999, I learned about a Swiss enterprise search system. The working name was, according to my Overflight archive, was AMI Albert.The “AMI” did not mean friend. AMI shorthand for Automatic Message Interpreter.
Flash forward to 2014. Note that a Google query for “AMI” may return hits for AMI International a defense oriented company as well as hits to American Megatrends, Advanced Metering Infrastructure, ambient intelligence, the Association Montessori International, and dozens of other organizations sharing the acronym. In an age of Google, finding a specific company can be a challenge and may inhibit some potential customers ability to locate a specific vendor. (This is a problem shared by Thunderstone, for example. The game company makes it tough to locate information about the search appliance vendor.)
Basic search interface as of 2011.
Every time I update my files, I struggle to get specific information. Invariably I get an email from an AMI Software sales person telling me, “Yes, we are growing. We are very much a dynamic force in market intelligence.”
The UK Web site for the firm is www.amisw.co.uk. The French language Web site for the company is http://www.amisw.com/fr/. And the English language version of the French Web site is at http://www.amisw.com/fr/. The company’s blog is at http://www.amisw.com/fr/blog/, but the content is stale. The most recent update as of July 7, 2014, is from December 2013. The company seems to have shifted its dissemination of news to LinkedIn, where more than 30 AMI employees have a LinkedIn presence. The blog is in French. The LinkedIn postings are in English. Most of the AMI videos are in French as well.
Advanced Search Interface as of 2011.
The Managing Director, according to www.amisw.com/fr, is Alain Beauvieux. The person in charge of products is Eric Fourboul. The UK sales manager is Mike Alderton.
Mr. Beauvieux is a former IBMer and worked at LexiQuest, which originally formerly Erli, S.A. LexiQuest (Clementine) was acquired by SPSS. SPSS was, in turn, acquired by IBM, joining other long-in-the-tooth technologies marketed today by IBM. Eric
Fourboul is a former Dassault professional, and he has some Microsoft DNA in his background.
June 30, 2014
I returned from a brief visit to Europe to an email asking about Rocket Software’s breakthrough technology AeroText. I poked around in my archive and found a handful of nuggets about the General Electric Laboratories’ technology that migrated to Martin Marietta, then to Lockheed Martin, and finally in 2008 to the low profile Rocket Software, an IBM partner.
When did the text extraction software emerge? Is Rocket Software AeroText a “new kid on the block”? The short answer is that AeroText is pushing 30, maybe 35 years young.
Digging into My Archive of Search Info
As far as my archive goes, it looks as though the roots of AeroText are anchored in the 1980s, Yep, that works out to an innovation about the same age as the long in the tooth ISYS Search system, now owned by Lexmark. Over the years, the AeroText “product” has evolved, often in response to US government funding opportunities. The precursor to AeroText was an academic exercise at General Electric. Keep in mind that GE makes jet engines, so GE at one time had a keen interest in anything its aerospace customers in the US government thought was a hot tamale.
The AeroText interface circa mid 2000. On the left is the extraction window. On the right is the document window. From “Information Extraction Tools: Deciphering Human Language, IT Pro, November December 2004, page 28.
The GE project, according to my notes, appeared as NLToolset, although my files contained references to different descriptions such as Shogun. GE’s team of academics and “real” employees developed a bundle of tools for its aerospace activities and in response to Tipster. (As a side note, in 2001, there were a number of Tipster related documents in the www.firstgov.gov system. But the new www.usa.gov index does not include that information. You will have to do your own searching to unearth these text processing jump start documents.)
The aerospace connection is important because the Department of Defense in the 1980s was trying to standardize on markup for documents. Part of this effort was processing content like technical manuals and various types of unstructured content to figure out who was named, what part was what, and what people, places, events, and things were mentioned in digital content. The utility of NLToolset type software was for cost reduction associated with documents and the intelligence value of processed information.
The need for a markup system that worked without 100 percent human indexing was important. GE got with the program and appears to have assigned some then-young folks to the project. The government speak for this type of content processing involves terms like “message understanding” or MU, “entity extraction,” and “relationship mapping. The outputs of an NLToolset system were intended for use in other software subsystems that could count, process, and perform other operations on the tagged content. Today, this class of software would be packaged under a broad term like “text mining.” GE exited the business, which ended up in the hands of Martin Marietta. When the technology landed at Martin Marietta, the suite of tools was used in what was called in the late 1980s and early 1990s, the Louella Parsing System. When Lockheed and Martin merged to form the giant Lockheed Martin, Louella was renamed AeroText.
Over the years, the AeroText system competed with LingPipe, SRA’s NetOwl and Inxight’s tools. In the hay day of natural language processing, there were dozens and dozens of universities and start ups competing for Federal funding. I have mentioned in other articles the importance of the US government in jump starting the craziness in search and content processing.
In 2005, I recall that Lockheed Martin released AeroText 5.1 for Linux, but I have lost track of the open source versions of the system. The point is that AeroText is not particularly new, and as far as I know, the last major upgrade took place in 2007 before Lockheed Martin sold the property to AeroText. At the time of the sale, AeroText incorporated a number of subsystems, including a useful time plotting feature. A user could see tagged events on a timeline, a function long associated with the original version of i2’s the Analyst Notebook. A US government buyer can obtain AeroText via the GSA because Lockheed Martin seems to be a reseller of the technology. Before the sale to Rocket, Lockheed Martin followed SAIC’s push into Australia. Lockheed signed up NetMap Analytics to handle Australia’s appetite for US government accepted systems.
What does AeroText purport to do that caused the person who contacted me to see a 1980s technology as the next best thing to sliced bread?
AeroText is an extraction tool; that is, it has capabilities to identify and tag entities at somewhere between 50 percent and 80 percent accuracy. (See NIST 2007 Automatic Content Extraction Evaluation Official Results for more detail.)
The AeroText approach uses knowledgebases, rules, and patterns to identify and tag pre-specified types of information. AeroText references patterns and templates, both of which assume the licensee knows beforehand what is needed and what will happen to processed content.
In my view, the licensee has to know what he or she is looking for in order to find it. This is a problem captured in the famous snippet, “You don’t know what you don’t know” and the “unknown unknowns” variation popularized by Donald Rumsfeld. Obviously without prior knowledge the utility of an AeroText-type of system has to be matched to mission requirements. AeroText pounded the drum for the semantic Web revolution. One of AeroText’s key functions was its ability to perform the type of markup the Department of Defense required of its XML. The US DoD used a variant called DAML or Darpa Agent Markup Language. natural language processing, Louella, and AeroText collected the dust of SPARQL, unifying logic, RDF, OWL, ontologies, and other semantic baggage as the system evolved through time.
Also, staff (headcount) and on-going services are required to keep a Louella/AeroText-type system generating relevant and usable outputs. AeroText can find entities, figure out relationships like person to person and person to organization, and tag events like a merger or an arrest “event.” In one briefing about AeroText I attended, I recall that the presenter emphasized that AeroText did not require training. (The subtext for those in the know was that Autonomy required training to deliver actionable outputs.) The presenter did not dwell on the need for manual fiddling with AeroText’s knowledgebases and I did not raise this issue.)