October 25, 2016
I read “Success Factors for Enterprise Search.” The write up spells out a checklist to make certain that an enterprise search system delivers what the users want—on point answers to their business information needs. The reason a checklist is necessary after more than 50 years of enterprise search adventures is a disconnect between what software can deliver and what the licensee and the users expect. Imagine figuring out how to get across the Grand Canyon only to encounter the Iguazu Falls.
The preamble states:
I’ll start with what absolutely does not work. The “dump it in the index and hope for the best” approach that I’ve seen some companies try, which just makes the problem worse. Increasing the size of the haystack won’t help you find a needle.
I think I agree, but the challenge is multiple piles of data. Some data are in haystacks; some are in odd ball piles from the AS/400 that the old guy in accounting uses for an inventory report.
Now the check list items:
- Metadata. To me, that’s indexing. Lousy indexing produces lousy search results in many cases. But “good indexing” like the best pie at the state fair is a matter of opinion. When the licensee, users, and the search vendor talk about indexing, some parties in the conversation don’t know indexing from oatmeal. The cost of indexing can be high. Improving the indexing requires more money. The magic of metadata often leads back to a discussion of why the system delivers off point results. Then there is talk about improving the indexing and its cost. The cycle can be more repetitive than a Kenmore 28132’s.
- Provide the content the user requires. Yep, that’s easy to say. Yep, if its on a distributed network, content disappears or does not get input into the search system. Putting the content into a repository creates another opportunity for spending money. Enterprise search which “federates” is easy to say, but the users quickly discover what is missing from the index or stale.
- Deliver off point results. The results create work by not answering the user’s question. From the days of STAIRS III to the latest whiz kid solution from Sillycon Valley, users find that search and retrieval systems provide an opportunity to go back to traditional research tools such as asking the person in the next cube, calling a self-appointed expert, guessing, digging through paper documents, or hiring an information or intelligence professional to gather the needed information.
The check list concludes with a good question, “Why is this happening?” The answer does not reside in the check list. The answer does not reside in my Enterprise Search Report, The Landscape of Search, or any of the journal and news articles I have written in the last 35 years.
The answer is that vendors directly or indirectly reassure that their software will provide the information a user needs. That’s an easy hook to plant in the customer who behaves like a tuna. The customer has a search system or experience with a search system that does not work. Pitching a better, faster, cheaper solution can close the deal.
The reality is that even the most sophisticated search and content processing systems end up in trouble. Search remains a very difficult problem. Today’s solutions do a few things better than STAIRS III did. But in the end, search software crashes and burns when it has to:
- Work within a budget
- Deal with structured and unstructured data
- Meet user expectations for timeliness, precision, recall, and accuracy
- Does not require specialized training to use
- Delivers zippy response time
- Does not crash or experience downtime due to maintenance
- Outputs usable, actionable reports without having to involve a programmer
- Provides an answer to a question.
Smart software can solve some of these problems for specific types of queries. Enterprise search will benefit incrementally. For now, baloney about enterprise search continues to create churn. The incumbent loses the contract, and a new search vendors inks a deal. Months later, the incumbent loses the contract, and the next round of vendors compete for the contract. This cycle has eroded the credibility of search and content processing vendors.
A check list with three items won’t do much to change the credibility gap between what vendors say, what licensees hope will occur, and what users expect. The Grand Canyon is a big hole to fill. The Iguazu Falls can be tough to cross. Same with enterprise search.
Stephen E Arnold, October 25, 2016
October 24, 2016
I spent a few minutes catching up with the news on the Attivio blog. You can find the information at this link. As I worked through the write ups over the past five weeks, I was struck by the diversity of Attivio’s marketing messages. Here are the ones which I noted:
- Attivio is a cognitive computing company, not a search or database company
- Attivio has an interest in governance and risk / compliance
- Attivio is involved in Big Data management
- Attivio is active in anti fraud solutions
- Attivio embraces NoSQL
- Attivio knows about modernizing an organization’s data architecture
- Attivio is a business intelligence solution.
My reaction to these capabilities is two fold:
First, for a company which has its roots in Fast Search & Transfer type of software, Attivio has added a number of applications to basic content processing and information access. Attivio embodies the vision Fast Search articulated before the company ran into some “challenges” and sold to Microsoft in 2008. Fast Search, as I understood the vision, was a platform upon which information applications could be built. Attivio appears to be heading in that direction.
The second reaction is that Attivio is churning out capabilities which embody buzzwords, jargon, and trends. Like a fisherman in a bass boat, the Attivio approach is to use different lures in order to snag a decent sized bass. I find it difficult to accept the assertion that a company rooted in search can deliver in the array of technical niches the blog posts reference.
The major takeaway for me was that Attivio has hired a new Chief Revenue Officer whose job is to generate revenue from the company’s “data catalog” business. I learned from “Attivio Names New Chief Revenue Officer”:
Connon [the insider who took over the revenue job] sees his new role as a reflection of the growing demand for technology that can break down data silos and help successful companies answer, not just the question of “what” the data is reporting, but identify correlation and patterns to answer critical “why” questions. Connon is passionate when he talks about the value of Attivio’s newest technology solution—the Semantic Data Catalog–and its ability to unify a wide array of data for a diverse customer base. “The Semantic Data Catalog is not just for financial service industries. It’s truly a horizontal technology solution that can benefit companies in any industry with data—in other words, with any company, in any industry,” explains Connon. “Our established Cognitive Search and Insight technology provides the foundation for our Semantic Data Catalog to provide companies with a self-service, permission-based ability to locate, sort, and analyze key information across an unlimited number of data applications,” adds Connon.
For me, Attivio’s “momentum” in marketing has to be converted to sustainable revenue. My assumption is that almost every professional at a software / services company sells and generates revenue. When a company lags in revenue, will one person be able to generate revenue?
I don’t have an answer. Worth monitoring to learn if the Chief Revenue Officer can deliver the money.
Stephen E Arnold, October 24, 2016
October 19, 2016
Here’s a quote to note from “Slack CEO Describes Holy Grail of Virtual Assistants.” Slack seeks to create smart software capable of correlating information from enterprise applications. Good idea. The write up says:
Slack CEO Stewart Butterfield has an audacious goal: Turning his messaging and collaboration platform into an uber virtual assistant capable of searching every enterprise application to deliver employees pertinent information.
Got it. Employees cannot locate information needed for their job. Let me sidestep the issue of hiring people incapable of locating information in the first place.
Here’s the quote I noted:
And if Slack succeeds, it could seal the timeless black hole of wasted productivity enterprise search and other tools have failed to close.
I love the “timeless black hole of wasted productivity of enterprise search.” Great stuff, particularly because outfits like Wolters Kluwer continue to oscillate between proprietary search investments like Qwant.com and open source solutions like Lucene/Solr.
Do organizations create these black holes or is software to blame? Information is a slippery fish, which often find “timeless black holes” inhospitable.
Stephen E Arnold, October 19, 2016
October 13, 2016
I read “Search Terminology. Web Search, Enterprise Search, Real Time Search, Semantic Search.” I have included glossaries in some of my books about search. I did not realize that I could pluck out four definitions and present them as a stand alone article. Ah, the wonders of content marketing.
If you want to read the definition with which one can die, either for or with, have at it. May I suggest that you consider these questions prior to your perusing the content marketing write up thing:
- What’s the method for password protected sites and encrypted sites which exist under current Web technology?
- What Web search systems build their own indexes and which send a query to multiple search systems and aggregate the results? Does the approach matter?
- What is the freshness or staleness of Web indexes? Does it matter that one index may be a few minutes “old” and another index several weeks “old”?
- How does an enterprise search system deliver internal content points and external content pointers?
- What is the consequence of an enterprise search user who accesses content which is incomplete or stale?
- What does the enterprise search system do with third party content such as consultants’ reports which someone in the organization has purchased? Ignore? Re-license? Index the content and worry later?
- What is the refresh cycle for changed and new content?
- What is the search function for locating database content or rich media residing on the organization’s systems?
Real time search
- What is real time? The indexing of content in the millisecond world of Wall Street? Indexing content when machine resources and network bandwidth permit?
- How does a user determine the latency in the search system because marketers can write “real time” while programmers implement index update options which the search administrator selects?
- What search system indexes videos in real time? YouTube struggles with 10 minute or longer latency with some videos requiring hours before the index points to those videos?
- What is the role of human subject matter experts in semantic search?
- What is the benefit of human-intermediated systems versus person-machine or automated smart indexing?
- How does one address concept drift as a system “learns” from its indexing of information?
- What happens to taxonomies, dictionary lists of entities, and other artifacts of concept indexing?
- What does a system do when encountering documents, audio, and videos in a language different from the language of the majority of a system’s users?
Get the idea that zippy, brief definitions cannot deliver Gatorade to the college football players studying in the dorm the night before a big game?
Stephen E Arnold, October 13, 2016
October 10, 2016
An interesting and brief search related content marketing white paper “InnovationQ Plus Search Engine Technology” attracted my attention. What’s interesting is that the IEEE is apparently in the search engine content marketing game. The example I have in front of me is from a company doing business as IP.com.
What does InnovationQ Plus do to deliver on point results? The write up says:
This engine is powered by IP.com’s patented neural network machine learning technology that improves searcher productivity and alleviates the difficult task of identifying and selecting countless keywords/synonyms to combine into Boolean syntax. Simply cut and paste abstracts, summaries, claims, etc. and this state-of-the art system matches queries to documents based on meaning rather than keywords. The result is a search that delivers a complete result set with less noise and fewer false positives. Ensure you don’t miss critical documents in your search and analysis by using a semantic engine that finds documents that other tools do not.
The use of snippets of text as the raw material for a behind-the-scenes query generator reminds me of the original DR-LINK method, among others. Perhaps there is some Syracuse University “old school” search DNA in the InnovationQ Plus approach? Perhaps the TextWise system has manifested itself as a “new” approach to patent and STEM (scientific, technology, engineering, and medical) online searching? Perhaps Manning & Napier’s interest in information access has inspired a new generation of search capabilities?
My hunch is, “Yep.”
If you don’t have a handy snippet encapsulating your search topic, just fill in the query form. Google offers a similar “fill in the blanks” approach even thought a tiny percentage of those looking for information on Google use advanced search. You can locate the Google advanced search form at this link.
Part of the “innovation” is the use of fielded search. Fielded search is useful. It was the go to method for locating information in the late 1960s. The method fell out of favor with the Sillycon Valley crowd when the idea of talking to one’s mobile phone became the synonym for good enough search.
To access the white paper, navigate the IEEE registration page and fill out the form at this link.
From my vantage point, structured search with “more like this” functions is a good way to search for information. There is a caveat. The person doing the looking has to know what he or she needs to know.
Good enough search takes a different approach. The systems try to figure out what the searcher needs to know and then deliver it. The person looking for information is not required to do much thinking.
The InnovationQ Plus approach shifts the burden from smart software to smart searchers.
Good enough search is winning the battle. In fact, some Sillycon Valley folks, far from upstate New York, have embraced good enough search with both hands. Why use words at all? There are emojis, smart software systems predicting what the use wants to know, and Snapchat infused image based methods.
The challenge will be to find a way to bridge the gap between the Sillycon Valley good enough methods and the more traditional structured search methods.
IEEE seems to agree as long as the vendor “participates” in a suitable IEEE publishing program.
Stephen E Arnold, October 10, 2016
October 4, 2016
I read an article I found quite thought provoking. “Why Companies Make Their Products Worse” explains that reducing costs allows a manufacturer to expand the market for a product. The idea is that more people will buy a product if it is less expensive than a more sophisticated version of the product. The example which I highlighted in eyeshade green explained that IBM introduced an expensive printer in the 1980s. Then IBM manufactured the different version of the printer using cheaper labor. The folks from Big Blue added electronic components to make the cheaper printer slower. The result was a lower cost printer that was “worse” than the original.
Perhaps enterprise search and content processing is a hybrid of two or more creatures?
The write up explained that this approach to degrading a product to make more money has a name—crimping. The concept creates “product sabotage”; that is, intentionally degrading a product for business reasons.
The comments to the article offer additional examples and one helpful person with the handle Dadpolice stated:
The examples you give are accurate, but these aren’t relics of the past. They are incredibly common strategies that chip makers still use today.
I understand the hardware or tangible product application of this idea. I began to think about the use of the tactic by text processing vendors.
The Google Search Appliance may have been a product subject to crimping. As I recall, the most economical GSA was less than $2000, a price which was relatively easy to justify in many organizations. Over the years, the low cost option disappeared and the prices for the Google Search Appliances soared to Autonomy- and Fast Search-levels.
Other vendors introduced search and content processing systems, but the prices remained lofty. Search and content processing in an organization never seemed to get less expensive when one considered the resources required, the license fees, the “customer” support, the upgrades, and the engineering for customization and optimization.
My hypothesis is that enterprise content processing does not yield compelling examples like the IBM printer example.
Perhaps the adoption rate for open source content processing reflects a pent up demand for “crimping”? Perhaps some clever graduate student would take the initiative to examine the content processing product prices? Licensees spend for sophisticated solution systems like those available from outfits like IBM and Palantir Technologies. The money comes from the engineering and what I call “soft” charges; that is, training, customer support, and engineering and consulting services.
At the other end of the content processing spectrum are open source solutions. The middle between free or low cost systems and high end solutions does not have too many examples. I am confident there are some, but I could identify Funnelback, dtSearch, and a handful of other outfits.
Perhaps “crimping” is not a universal principle? On the other hand, perhaps content processing is an example of a technical software which has its own idiosyncrasies.
Content processing products, I believe, become worse over time. The reason is not “crimping.” The trajectory of lousiness comes from:
- Layering features on keyword retrieval in hopes of finding a way to generate keen buyer interest
- Adding features helps justify price increases
- The greater the complexity of the system, the less likely the licensee will be able to fiddle with the system
- A refusal to admit that content processing is a core component of many other types of software so “finding information” has become a standard component for other applications.
If content processing is idiosyncratic, that might explain why investors pour money into content processing companies which have little chance to generate sufficient revenue to pay off investors, generate a profit, and build a sustainable business. Enterprise search and content processing vendors seem to be in a state of reinventing or reimagining themselves. Guitar makers just pursue cost cutting and expand their market. It is not so easy for content processing companies.
Stephen E Arnold, October 4, 2016
October 4, 2016
Before I shifted from worker bee to Kentucky dirt farmer, I attended a presentation in which a wizard from Findwise explained enterprise search in 2011. In my notes, I jotted down the companies the maven mentioned (love that alliteration) in his remarks:
- ISYS Search
There were nodding heads as the guru listed the key functions of enterprise search systems in 2011. My notes contained these items:
- Federation model
- Indexing and connectivity
- Interface flexibility
- Management and analysis
- Mobile support
- Platform readiness
- Relevance model
- Semantics and text analytics
- Social and collaborative features
I recall that I was confused about the source of the information in the analysis. Then the murky family tree seemed important. Five years later, I am less interested in who sired what child than the interesting historical nuggets in this simple list and collection of pretty fuzzy and downright crazy characteristics of search. I am not too sure what “analysis” and “analytics” mean. The notion that an index is required is okay, but the blending of indexing and “connectivity” seems a wonky way of referencing file filters or a network connection. With the Harvard Business Review pointing out that collaboration is a bit of a problem, it is an interesting footnote to acknowledge that a buzzword can grow into a time sink.
There are some notable omissions; for example, open source search options do not appear in the list. That’s interesting because Attivio was at that time I heard poking its toe into open source search. IBM was a fan of Lucene five years ago. Today the IBM marketing machine beats the Watson drum, but inside the Big Blue system resides that free and open source Lucene. I assume that the gurus and the mavens working on this list ignored open source because what consulting revenue results from free stuff? What happened to Oracle? In 2011, Oracle still believed in Secure Enterprise Search only to recant with purchases of Endeca, InQuira, and Rightnow. There are other glitches in the list, but let’s move on.
October 1, 2016
Search vendors can save their business by embracing text analytics. Sounds like a wise statement, right? I would point out that our routine check of search and content processing companies turned up this inspiring Web page for Attensity, the Xerox Parc love child and once hot big dog in text analysis:
Attensity joins a long list of search-related companies which have had to reinvent themselves.
The company pulled in $90 million from a “mystery investor” in 2014. A pundit tweeted in 2015:
In February 2016, Attensity morphed into Sematell GmbH, a company with interaction solutions.
I mention this arabesque because it underscores:
- No single add on to enterprise search will “save” an information access company
- Enterprise search has become a utility function. Witness the shift to cloud based services like SearchBlox, appliances like Maxxcat, and open source options. Who will go out on a limb for a proprietary utility when open source variants are available and improving?
- Pundits who champion a company often have skin in the game. Self appointed experts for cognitive computing, predictive analytics, or semantic link analysis are tooting a horn without other instruments.
Attensity is a candidate to join the enterprise search Hall of Fame. In the shrine are Delphes, Entopia, et al. I anticipate more members, and I have a short list of “who is next” taped on my watch wall.
Stephen E Arnold, October 1, 2016
September 30, 2016
Enterprise search has taken a back a back seat to search news regarding Google’s next endeavor and what the next big thing is in big data. Enterprise search may have taken a back seat in my news feed, but it is still a major component in enterprise systems. You can even speculate that without a search function, enterprise systems are useless.
Lexmark, one of the largest suppliers of printers and business solutions in the country, understand the importance of enterprise search. This is why they recently updated the description of its Perceptive Enterprise Search in its system’s technical specifications:
Perceptive Enterprise Search is a suite of enterprise applications that offer a choice of options for high performance search and mobile information access. The technical specifications in this document are specific to Perceptive Enterprise Search version 10.6…
A required amount of memory and disk space is provided. You must meet these requirements to support your Perceptive Enterprise Search system. These requirements specifically list the needs of Perceptive Enterprise Search and do not include any amount of memory or disk space you require for the operating system, environment, or other software that runs on the same machine.
Some technical specifications also provide recommendations. While requirements define the minimum system required to run Perceptive Enterprise Search, the recommended specifications serve as suggestions to improve the performance of your system. For maximum performance, review your specific environment, network, and platform capabilities and analyze your planned business usage of the system. Your specific system may require additional resources above these recommendations.”
It is pretty standard fare when it comes to technical specifications, in other words, not that interesting but necessary to make the enterprise system work correctly.
September 27, 2016
This week’s HonkinNews video tackles Yahoo’s data breach. Stephen E Arnold reveals that Beyond Search thinks Yahoo is a hoot and tags the company Yahoot. Plus, HonkinNews suggests that Oliver Stone may want to do a follow up to Snowden. The new film could be “Marissa: Purple Sun Down.” Other stories include Hewlett Packard Enterprise’s opportunity to see the light with Dr. Michael Lynch’s Luminance. The video explains puppy bias and comments on Harvard’s interest in sugar and fat. You can view the seven minute video at https://youtu.be/64rJdlj4Lew.
Kenny Toth, September 27, 2016