Search Vendors Under Pressure: Welcome to 2016
December 21, 2015
I read ”Silicon Valley’s Cash Party Is Coming to an End.” What took so long? I suppose reality is less fun than fantasy. Why watch a science documentary when one can get lost in Netflix binging.
The write up reports:
Based on interviews with about two dozen venture capitalists and tech investors, 2016 is shaping up to be a year of reckoning for scores of technology start-ups that have yet to prove out their business models and equally challenging for those that raised money at unjustifiably high prices.
Forget the unicorns. There are some enterprise search outfits which have ingested millions of dollars, have convinced investors that big revenue or an HP-Autonomy scale buy out is just around the corner, and proprietary technology or consulting plus open source will produce gushers of organic revenue. Other vendors have tapped their moms, their nest eggs, and angels who believe in fairies.
I am not there is a General Leia Organa to fight Star Wars: The Revenue Battle for most vendors of search and content processing. Bummer. Despite the lack of media coverage for search and content processing vendors, the number of companies pitching information access is hefty. I track about 200 outfits, but many of these are unknown either because they don’t want to be visible or lack any substantive “newsy” magnetism.
My hunch is that this article suggests that 2016 may be different from the free money era the articles suggests is ending. In 2016, my view is that many vendors will find themselves in a modest tussle with their stakeholders. I worked through some of the search and content processing companies taking cash from folks with deep pockets often filled with other people’s money. (Note that investments totals come from Crunchbase). Here’s a list of search and content processing vendors who may face stakeholder and investor pressure. The more more ingested, the greater the interest investors may have in getting a return:
- Antidot, $3 million
- Attensity, $90 million
- Attivio, $71 million
- BA Insight, $14 million
- Connotate, $12 million
- Coveo, $69 million
- Digital Reasoning, $28 million
- Elastic (formerly Elasticsearch), $104 million
- Lucidworks, $53 million
- MarkLogic, $175 million
- Perfect Search, $4 million
- Palantir, $1.7 billion
- Recommind, $22 million
- Sinequa, $5 million
- Sophia Ambiance, $5 million
- X1, $12 million.
Then there are the acquired search systems which been acquired. One assumes these deals will have to produce sustainable revenues in some form:
- Hewlett Packard with Autonomy
- IBM with Vivisimo
- Dassault Systèmes with Exalead
- Lexmark with Brainware and ISYS Search
- Microsoft with Fast Search
- OpenText with BASIS, BRS, Fulcrum, and Nstein
- Oracle with Endeca, InQuira, and Rightnow
- Thomson Reuters with Solcara
Are there sufficient prospects to generate deals large enough to keep these outfits afloat?
There are search and content processing vendors competing for sales with free and open source options and the vendors with proprietary software:
- Ami Albert
- Content Analyst
- Concept Searching
- dtSearch
- EasyAsk
- Exorbyte
- Fabasoft Mindbreeze
- Funnelback
- IHS Goldfire
- SLI Systems
- Smartlogic
- Sprylogics
- SurfRay
- Thunderstone
- WCC Elise
- Zaizi
These search vendors plus many smaller outfits like Intrafind and Srch2 have to find a way to close deals to avoid the fate of Arikus, Convera, Delphes, Dieselpoint, Entopia, Hakia, Kartoo, NuTech Search, and Siderean Software, among others.
Despite the lack of coverage from mid tier consultants and the “real” journalists, the information access sector is moving along. In fact, when one looks at the software options, search and content processing vendors are easily found.
The problem for 2016 will be making sales, generating sustainable revenues, and paying back stakeholders. For many of these companies, the new year will be one which sees a number of outfits going dark. A few will thrive.
Darned exciting times in findability.
Stephen E Arnold, December 21, 2015
Internet Sovereignty, Apathy, and the Cloud
December 21, 2015
The OS News post titled Dark Clouds Over the Internet presents an argument that boils down to a choice between international accord and data sharing agreement, or the risk of the Internet being broken up into national networks. Some very worked up commenters engaged in an interesting discussion that spanned government overreaching, democracy, data security, privacy, and for some reason, climate change. One person summarized their opinion thusly:
“Best policy: don’t store data with someone else. There is no cloud. It’s just someone else’s computer.”
In response, a user named Alfman replied that companies are to blame for the current lack of data security, or more precisely, people are generally to blame for allowing this state of affairs to exist,
The privacy issues we’re now seeing are a direct consequence of corporate business models pushing our data into their central silos. None of this is surprising except perhaps how willing users have been to forgo their own privacy. Collectively, it seems that we are very willing to give up our rights for very little in exchange… makes it difficult to achieve critical mass around technologies promoting data independence.”
It is hard to argue with the apathy factor, with data breaches occurring regularly and so little being done by individuals to protect themselves. Good thing these commenters have figured it all out. Next up, solving climate change.
Chelsea Kerwin, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
IBM Watson Competes for the Artificial Intelligence Crown
December 21, 2015
The article titled IBM Watson Vs. Amazon: Machine Learning Systems Presage the Future on Datamation dukes it out between IBM’s famous supercomputer and the Amazon Web Services platform. Both are at the forefront of the industry, but which is best? Unsurprisingly, the article offers no definitive answer beyond: it depends what you are using them for. The article states,
“Amazon offers a simplified platform for developers who want to start working with machine learning without a lot of stress or specialized tools or investment… What IBM is trying to establish with the Watson analytics engine is not just storing and acquiring data, but taking all that information and doing something meaningful with it as an AI service or Intelligence as a Service.”
Jack Gold, Principal Analyst for J.Gold Associates, emphasizes that the larger point is that the AI technologies these two companies are competing to lead will shortly be much more far-spread due to the ever increasing amounts of data. The article also discusses some of the more exciting uses of Watson and Amazon. The former, through a company called Fluid, is being put to use in the retail industry relying on Watson’s ability to “read” customer personalities (with his handy personality matrix). Amazon Machine Learning, in the meanwhile, has recently been used for predictive modeling of job-cost estimates for insurance companies and builders.
Chelsea Kerwin, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Topology Is Finally on Top
December 21, 2015
Topology’s time has finally come, according to “The Unreasonable Usefulness of Imagining You Live in a Rubbery World,” shared by 3 Quarks Daily. The engaging article reminds us that the field of topology emphasizes connections over geometric factors like distance and direction. Think of a subway map as compared to a street map; or, as writer Jonathan Kujawa describes:
“Topologists ask a question which at first sounds ridiculous: ‘What can you say about the shape of an object if you have no concern for lengths, angles, areas, or volumes?’ They imagine a world where everything is made of silly putty. You can bend, stretch, and distort objects as much as you like. What is forbidden is cutting and gluing. Otherwise pretty much anything goes.”
Since the beginning, this perspective has been dismissed by many as purely academic. However, today’s era of networks and big data has boosted the field’s usefulness. The article observes:
“A remarkable new application of topology has emerged in the last few years. Gunnar Carlsson is a mathematician at Stanford who uses topology to extract meaningful information from large data sets. He and others invented a new field of mathematics called Topological data analysis. They use the tools of topology to wrangle huge data sets. In addition to the networks mentioned above, Big Data has given us Brobdinagian sized data sets in which, for example, we would like to be able to identify clusters. We might be able to visually identify clusters if the data points depend on only one or two variables so that they can be drawn in two or three dimensions.”
Kujawa goes on to note that one century-old tool of topology, homology, is being used to analyze real-world data, like the ways diabetes patients have responded to a specific medication. See the well-illustrated article for further discussion.
Cynthia Murrell, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Score One for Yandex
December 21, 2015
Russian search powerhouse Yandex has successfully sued Google, we learn from re/code’s article, “Meet the Russian Company that Got Its Antitrust Watchdog to Bite Google.” Reporter Mark Bergen interviewed Yandex’s Roman Krupenin, who has led this legal campaign. In his intro, Bergen relates:
“In October, Russia’s antitrust authority ruled that Google’s practice of bundling its services on Android handsets violated national law. The case’s lead complainant was Yandex, an 18-year old Web search and advertising company. It’s not a global name, but is big in Russia. Last quarter, Yandex raked in $233.1 million in revenue. (For context, Google averaged about $179 million in sales a day over the same period.) Most Russians use Yandex for Internet searches — an estimated 57 percent in the last quarter, though that share has slipped in recent years. The culprit? According to Yandex, it’s the favored position of Google’s apps, including its search one and its browser, on Android smartphones, which outnumber iPhones in Russia considerably. To fight it off, Yandex has pushed to cut handset agreements of its own: It finalized one with Lenovo last year, and paired with Microsoft last month to make Yandex’s homepage and search results the Russian default for Windows 10.”
Furthermore, we’re reminded, Yandex is also taking part in the EU’s latest antitrust investigation. Naturally, Google is appealing the decision. See the article for text of the interview, where Krupenin discusses the focus on Android over Search, the unique factors that made for victory over the notoriously slippery company, and the call for an end to Google’s service-bundling practices.
Cynthia Murrell, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Watson Is Laying Startup Eggs
December 21, 2015
Incubators are warming stations for eggs. Without having to rely on an organism’s DNA donor, an incubator provides a warm, safe environment for the organism to develop, hatch, and eventually be ready to face the world. Watson has decided it is time for itself to propagate, but instead of knitting tiny computer cases Watson will invest its digital DNA in startups. The Chicago Tribune discusses Watson’s reproduction efforts and progeny in “Watson, IBM’s Big-Data Program Is Also A Startup Incubator.”
While IBM sells Watson’s ability to scan and understand terabytes of data, the company also welcomes developers to use Watson for new ideas. What is even more amazing is that IBM gives developers the ability to use Watson for free for a limited time.
“In Ecosystem, everyone is invited to play with Watson for free (for a limited time); some 77,000 developers have accepted. If your Watson-powered startup shows promise, it becomes a “partner,” often via a quasi-incubator model, and enjoys access to IBM business and technology advisers–and a shot at a capital infusion from the $100 million IBM is making available to Watson startups…”
Ecosystem has been used for startups that feature lifestyle coaching, personal shopping, infrastructure guards, veterinarian advice, fantasy sports calculator, 311 information, and even a hotel butler.
To quote the biblical justification for propagation: “Go forth and multiply the [Watson startups].”
Whitney Grace, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Getting Smart About Cutting the Cable Cord
December 21, 2015
A few years ago, I read an article about someone who was fed up with streaming content because he wanted new shows and access to all the channels so they resubscribed to cable. I have to admit the easiest thing to do would be to pay a monthly cable bill and shell out additional fees for the premiere channels. The only problem is that cable and extra channels are quite expensive. It has since become easier to cut the cord.
One of the biggest problems viewers face is finding specific and new content. Netflix, Hulu, iTunes, and Amazon Prime are limited with licenses and their individual content and having to search each one is time consuming. Even worse is trying to type out a series name using a remote control instead of a keyboard. Technology to the rescue!
The Verge talks about “Yahoo’s New App Is A TV Guide For Cord Cutters” called Yahoo Video Guide that allows viewers to search by a name and instantly watch it.
“Whenever users find what they want to watch, they can click a button to “Stream Now,” and the app will automatically launch a subscription service that hosts the film. If the program isn’t available online, users can buy it, instead.”
The coolest feature is that if viewers want to channel surf all they do so with GIFs. The viewer picks a GIF that fits their mood and the app will sort out content from there.
Finally, all those moving images have a different function than entertaining reddit users.
Whitney Grace, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Two AI Paths Pondered by Teradata
December 20, 2015
I read the content marketing write up by Karthik Guruswamy. I like the “guru” part of the expert’s name. I am stuck with the “old” part of my name.
The write is called “Data Science: Machine Learning Vs. Rules Based Systems.” I know a little bit about both of these methods, and I know a teeny tiny bit about Teradata, an outstanding data warehouse solution chugging along with its stock in the high $20s per share. The Google finance chart suggests that the company has some challenges with net income and profit margin to my unlearned eye:
Looks like some content marketing oomph is needed to move that top line number.
I learned in the write up:
Rules based systems will work effectively if all the situations, under which decisions can be made, are known ahead of time.
Okay. Insight. Know everything ahead of time and one can write rules to cover the situation. Is this expensive? Is this a never ending job? Consultants sure hope so.
There is an alternative:
Enter Machine Learning or ML! If we classify the data into good vs. bad data sets or categorize them into different labels like A, B, C, D etc., the Machine Learning algorithms can help build rules on the fly. This step is called training which results in a model. During operationalization, this model is used by the prediction algorithm to classify the incoming data in the right way which in turn leads to sound decision making.
I recall that Autonomy used this approach for its system. Those familiar with Autonomy have some experience with retraining, Bayesian drift, and other exciting facets of machine learning based systems. Consultants love to build new training sets.
The write up asserts:
With Machine Learning, one can iteratively achieve good results by cleansing & prepping the data, changing or combining algorithms or merely tweaking the algorithm parameters. This is becoming much easier thanks to the increased awareness and the availability of different types of data science tools in the market today.
High five.
My view is that the write up left out some information. But there is one omission which warrants a special comment.
Neither of these systems works without human intervention.
Bummer. Reality is sort of a drag, but maybe that’s why Teradata is wrestling with revenue and net profit alligators. Consultants, on the other hand, can bill to enhance either approach.
What about the customer? Well, some customers of brand name data warehouse systems struggle to get data into and out of this whiz bang systems in my experience. Regardless of the craziness involved with Hadoop and Spark, these open source approaches may make more sense than pumping six or seven figures into a proprietary system.
Consultants can still bill, of course. That’s one upside of any approach one wishes to embrace.
Stephen E Arnold, December 20, 2015
Plentiful, Presaging Prognostications
December 19, 2015
I read a roll up type article. “Industry Speaks: Top 33 Big Data Predictions for 2016” presents a fulsome suite of forecasts about 2016’s technology trends. If you are a fan of the race track tout approach to winners, you will want to print out this article and keep it with you.
I would love to comment on each prediction, but that, gentle reader, is a lot of work. I would prefer to return to my analysis of Palantir.
I did circle three of the predictions which I found somewhat intriguing. My hope is that you will want to dig deeply into the other 30 future forward conjectures. Here we go:
- Big Data will die. My hunch is that one would have to kill off the PR spouting spawn of marketing and sales departments before the monster of Big Data is tamed. Nice effort, bold prediction. My view is that it is pretty loco given the present environment.
- Companies will hire chief insight officers. Wow. My view is that folks struggling to deliver revenues will change their titles. I am not sure that human resources will work hand in glove with senior executives to hire a new person to be in charge of “insight.” I thought business intelligence software delivered this insight stuff.
- Spark will kill Hadoop. Interesting. I assume I was incorrect in thinking that Hadoop could be thought of as a variant of Google’s really old MapReduce technology. Hadoop is a bit of a challenge, but “killing” seems a bit of a stretch.
For the other 30 previsons, check out the original. Amazing stuff. Most of the horoscopes are like newspaper horoscopes; that is, data free.
Stephen E Arnold, December 19, 2015
Smart Software Sort of Snares Sarcasm
December 18, 2015
I read “Scientists Devise Algorithm That Detects Sarcasm Better Than Humans.” My first reaction was, “How well do humans detect sarcasm?” In my experience, literalism is expected. Sarcasm and its kissing cousins cynicism and humor are surprises.
I read:
In at least one study, by UC Berkeley’s David Bamman and the University of Washington’s Noah A. Smith, computers showed an accuracy rate of 75 percent—notably better than the humans in the 2005 study.
There you go. (Not sarcasm) Smart software can detect a statement designed to deliver a payload which has the surprise thing going for it. (Sarcasm)
The write up asserted:
Bamman (smart software champion) says sentiment analysis can be useful, for instance, when conducting an analysis of reviews on Amazon, to determine whether the reviewer actually liked a product. “One thing that can really interfere with that,” he says, “is whether or not the person is being sarcastic.” Accurate sentiment analysis can also be valuable to national security. In 2014, the Secret Service posted a work order requesting analytics software that can detect sarcasm on social media—the idea being that the ability to identify sarcasm would help them discern jokes from actual emergencies.
Okay. (Sarcasm). More of the good enough approach to understanding text. Hey, maybe the system is better than a word list? (Sarcasm)
Human language is a slippery fish. The researchers are trying to create a net to snag the elusive creatures like “Hell is empty and all the devils are here.” (Sarcasm)
Stephen E Arnold, December 21, 2015
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