Ground Hog Day: Smart Enterprise Search
January 7, 2025
I am a dinobaby. I also wrote the Enterprise Search Report, 1st, 2nd, and 3rd editions. I wrote The New Landscape of Search. I wrote some other books. The publishers are long gone, and I am mostly forgotten in the world of information retrieval. Read this post, and you will learn why. Oh, no AI helped me out unless I come up with an art idea. I used Stable Diffusion for the rat, er, sorry, ground hog day creature.
I think it was 2002 when the owner of a publishing company asked me if I thought there was an interest in profiles of companies offering “enterprise search solutions.” I vaguely remember the person, and I will leave it up to you to locate a copy of the 400 page books I wrote about enterprise search.
The set up for the book was simple. I identified the companies which seemed to bid on government contracts for search, companies providing search and retrieval to organizations, and outfits which had contacted me to pitch their enterprise search systems before they were exiting stealth mode. By the time the first edition appeared in 2004, the companies in the ESR were flogging their products.
The ground hog effect is a version of the Yogi Berra “Déjà vu all over again” thing. Enterprise search is just out of reach now and maybe forever.
The enterprise search market imploded. It was there and then it wasn’t. Can you describe the features and functions of these enterprise search systems from the “golden age” of information retrieval:
- Innerprise
- InQuira
- iPhrase
- Lextek Onix
- MondoSearch
- Speed of Mind
- Stratify (formerly Purple Yogi)
The end of enterprise search coincided with large commercial enterprises figuring out that “search” in a complex organization was not one thing. The problem remains today. Lawyers in a Fortune 1000 company want one type of search. Marketers want another “flavor” of search. The accountants want a search that retrieves structured and unstructured data plus images of invoices. Chemists want chemical structure search. Senior managers want absolutely zero search of their personal and privileged data unless it is lawyers dealing with litigation. In short, each unit wants a highly particularized search and each user wants access to his or her data. Access controls are essential, and they are a hassle at a time when the notion of an access control list was like learning to bake bread following a recipe in Egyptian hieroglyphics.
These problems exist today and are complicated by podcasts, video, specialized file types for 3D printing, email, encrypted messaging, unencrypted messaging, and social media. No one has cracked the problem of a senior sales person who changes a PowerPoint deck to close a deal. Where is that particular PowerPoint? Few know and the sales person may have deleted the file changed minutes before the face to face pitch. This means that baloney like “all” the information in an organization is searchable is not just stupid; it is impossible.
The key events were the legal and financial hassles over Fast Search & Transfer. Microsoft bought the company in 2008 and that was the end of a reasonably capable technology platform and — believe it or not — a genuine alternative to Google Web search. A number of enterprise search companies sold out because the cost of keeping the technology current and actually running a high-grade sales and marketing program spelled financial doom. Examples include Exalead and Vivisimo, among others. Others just went out of business: Delphes (remember that one?). The kiss of death for the type of enterprise search emphasized in the ESR was the acquisition of Autonomy by Hewlett Packard. There was a roll up play underway by OpenText which has redefined itself as a smart software company with Fulcrum and BRS Search under its wing.
What replaced enterprise search when the dust settled in 2011? From my point of view it was Shay Banon’s Elastic search and retrieval system. One might argue that Lucid Works (né Lucid Imagination) was a player. That’s okay. I am, however, to go with Elastic because it offered a version as open source and a commercial version with options for on-going engineering support. For the commercial alternatives, I would say that Microsoft became the default provider. I don’t think SharePoint search “worked” very well, but it was available. Google’s Search Appliance appeared and disappeared. There was zero upside for the Google with a product that was “inefficient” at making a big profit for the firm. So, Microsoft it was. For some government agencies, there was Oracle.
Oracle acquired Endeca and focused on that computationally wild system’s ability to power eCommerce sites. Oracle paid about $1 billion for a system which used to be an enterprise search with consulting baked in. One could buy enterprise search from Oracle and get structured query language search, what Oracle called “secure enterprise search,” and may a dollop of Triple Hop and some other search systems the company absorbed before the end of the enterprise search era. IBM talked about search but the last time I drove by IBM Government systems in Gaithersburg, Maryland, it like IBM search, had moved on. Yo, Watson.
Why did I make this dalliance on memory lane the boring introduction to a blog post? The answer is that I read “Are LLMs At Risk Of Going The Way Of Search? Expect A Duopoly.” This is a paywalled article, so you will have to pony up cash or go to a library. Here’s an abstract of the write up:
The evolution of LLMs (Large Language Models) will lead users to prefer one or two dominant models, similar to Google’s dominance in search.
Companies like Google and Meta are well-positioned to dominate generative AI due to their financial resources, massive user bases, and extensive data for training.
Enterprise use cases present a significant opportunity for specialized models.
Therefore, consumer search will become a monopoly or duopoly.
Let’s assume the Forbes analysis is accurate. Here’s what I think will happen:
First, the smart software train will slow and a number of repackagers will use what’s good enough; that is, cheap enough and keeps the client happy. Thus, a “golden age” of smart search will appear with outfits like Google, Meta, Microsoft, and a handful of others operating as utilities. The US government may standardize on Microsoft, but it will be partners who make the system meet the quite particular needs of a government entity.
Second, the trajectory of the “golden age” will end as it did for enterprise search. The costs and shortcomings become known. Years will pass, probably a decade, maybe less, until a “new” approach becomes feasible. The news will diffuse and then a seismic event will occur. For AI, it was the 2023 announcement that Microsoft and OpenAI would change how people used Microsoft products and services. This created the Google catch up and PR push. We are in the midst of this at the start of 2025.
Third, some of the problems associated with enterprise information and an employee’s finding exactly what he or she needs will be solved. However, not “all” of the problems will be solved. Why? The nature of information is that it is a bit like pushing mercury around. The task requires fresh thinking.
To sum up, the problem of search is an excellent illustration of the old Hegelian chestnut of Hegelian thesis, antithesis, and synthesis. This means the problem of search is unlikely to be “solved.” Humans want answers. Some humans want to verify answers which means that the data on the sales person’s laptop must be included. When the detail oriented human learns that the sales person’s data are missing, the end of the “search solution” has begun.
The question “Will one big company dominate?” The answer is, in my opinion, maybe in some use cases. Monopolies seem to be the natural state of social media, online advertising, and certain cloud services. For finding information, I don’t think the smart software will be able to deliver. Examples are likely to include [a] use cases in China and similar countries, [b] big multi-national organizations with information silos, [c] entities involved in two or more classified activities for a government, [d] high risk legal cases, and [e] activities related to innovation, trade secrets, and patents, among others.
The point is that search and retrieval remains an extraordinarily difficult problem to solve in many situations. LLMs contribute some useful functional options, but by themselves, these approaches are unlikely to avoid the reefs which sank the good ships Autonomy and Fast Search & Transfer, and dozens of others competing in the search space.
Maybe Yogi Berra did not say “Déjà vu all over again.” That’s okay. I will say it. Enterprise search is “Déjà vu all over again.”
Stephen E Arnold, January 7, 2025
The Hay Day of Search Has a Ground Hog Moment
December 19, 2024
This blog post is the work of an authentic dinobaby. No smart software was used.
I think it was 2002 or 2003 that I started writing the first of three editions of Enterprise Search Report. I am not sure what happened to the publisher who liked big, fat thick printed books. He has probably retired to an island paradise to ponder the crashing blue surf.
But it seems that the salad days of enterprise search are back. Elastic is touting semantics, smart software, and cyber goodness. IBM is making noises about “Watson” in numerous forms just gift wrapped with sparkly AI ice cream jimmies. There is a start up called Swirl. The HuggingFace site includes numerous references to finding and retrieving. And there is Glean.
I keep seeing references to Glean. When I saw a link to the content marketing piece “Glean’s Approach to Smarter Systems: AI, Inferencing and Enterprise Data,” I read it. I learned that the company did not want to be an AI outfit, a statement I am not sure how to interpret; nevertheless, the founder of Glean is quoted as saying:
“We didn’t actually set out to build an AI application. We were first solving the problem of people can’t find anything in their work lives. We built a search product and we were able to use inferencing as a core part of our overall product technology,” he said. “That has allowed us to build a much better search and question-and-answering product … we’re [now] able to answer their questions using all of their enterprise knowledge.”
And what happened to finding information? The company has moved into:
- Workflows
- Intelligent data discovery
- Problem solving
And the result is not finding information:
Glean enables enterprises to improve efficiency while maintaining control over their knowledge ecosystem.
Translation: Enterprise search.
The old language of search is gone, but it seems to me that “search” is now explained with loftier verbiage than that used by Fast Search & Transfer in a lecture delivered in Switzerland before the company imploded.
Is it now time for write the “Enterprise Knowledge Ecosystem Report”? Possibly for someone, but it’s Ground Hog time. I have been there and done that. Everyone wants search to work. New words and the same challenges. The hay is growing thick and fast.
Stephen E Arnold, December 19, 2024
Backpressure: A Bit of a Problem in Enterprise Search in 2024
March 27, 2024
This essay is the work of a dumb dinobaby. No smart software required.
I have noticed numerous references to search and retrieval in the last few months. Most of these articles and podcasts focus on making an organization’s data accessible. That’s the same old story told since the days of STAIRS III and other dinobaby artifacts. The gist of the flow of search-related articles is that information is locked up or silo-ized. Using a combination of “artificial intelligence,” “open source” software, and powerful computing resources — problem solved.
A modern enterprise search content processing system struggles to keep pace with the changes to already processed content (the deltas) and the flow of new content in a wide range of file types and formats. Thanks, MSFT Copilot. You have learned from your experience with Fast Search & Transfer file indexing it seems.
The 2019 essay “Backpressure Explained — The Resisted Flow of Data Through Software” is pertinent in 2024. The essay, written by Jay Phelps, states:
The purpose of software is to take input data and turn it into some desired output data. That output data might be JSON from an API, it might be HTML for a webpage, or the pixels displayed on your monitor. Backpressure is when the progress of turning that input to output is resisted in some way. In most cases that resistance is computational speed — trouble computing the output as fast as the input comes in — so that’s by far the easiest way to look at it.
Mr. Phelps identifies several types of backpressure. These are:
- More info to be processed than a system can handle
- Reading and writing file speeds are not up to the demand for reading and writing
- Communication “pipes” between and among servers are too small, slow, or unstable
- A group of hardware and software components cannot move data where it is needed fast enough.
I have simplified his more elegantly expressed points. Please, consult the original 2019 document for the information I have hip hopped over.
My point is that in the chatter about enterprise search and retrieval, there are a number of situations (use cases to those non-dinobabies) which create some interesting issues. Let me highlight these and then wrap up this short essay.
In an enterprise, the following situations exist and are often ignored or dismissed as irrelevant. When people pooh pooh my observations, it is clear to me that these people have [a] never been subject to a legal discovery process associated with enterprise search fraud and [b] are entitled whiz kids who don’t do too much in the quite dirty, messy, “real” world. (I do like the variety in T shirts and lumberjack shirts, however.)
First, in an enterprise, content changes. These “deltas” are a giant problem. I know that none of the systems I have examined, tested, installed, or advised which have a procedure to identify a change made to a PowerPoint, presented to a client, and converted to an email confirming a deal, price, or technical feature in anything close to real time. In fact, no one may know until the president’s laptop is examined by an investigator who discovers the “forgotten” information. Even more exciting is the opposing legal team’s review of a laptop dump as part of a discovery process “finds” the sequence of messages and connects the dots. Exciting, right. But “deltas” pose another problem. These modified content objects proliferate like gerbils. One can talk about information governance, but it is just that — talk, meaningless jabber.
Second, the content which an employees needs to answer a business question in a timely manner can reside in am employee’s laptop or a mobile phone, a digital notebook, in a Vimeo video or one of those nifty “private” YouTube videos, or behind the locked doors and specialized security systems loved by some pharma company’s research units, a Word document in something other than English, etc. Now the content is changed. The enterprise search fast talkers ignore identifying and indexing these documents with metadata that pinpoints the time of the change and who made it. Is this important? Some contract issues require this level of information access. Who asks for this stuff? How about a COTR for a billion dollar government contract?
Third, I have heard and read that modern enterprise search systems “use”, “apply,” “operate within” industry standard authentication systems. Sure they do within very narrowly defined situations. If the authorization system does not work, then quite problematic things happen. Examples range from an employee’s failure to find the information needed and makes a really bad decision. Alternatively the employee goes on an Easter egg hunt which may or may not work, but if the egg found is good enough, then that’s used. What happens? Bad things can happen? Have you ridden in an old Pinto? Access control is a tough problem, and it costs money to solve. Enterprise search solutions, even the whiz bang cloud centric distributed systems, implement something, which is often not the “right” thing.
Fourth, and I am going to stop here, the problem of end-to-end encrypted messaging systems. If you think employees do not use these, I suggest you do a bit of Eastern egg hunting. What about the content in those systems? You can tell me, “Our company does not use these.” I say, “Fine. I am a dinobaby, and I don’t have time to talk with you because you are so much more informed than I am.”
Why did I romp though this rather unpleasant issue in enterprise search and retrieval? The answer is, “Enterprise search remains a problematic concept.” I believe there is some litigation underway about how the problem of search can morph into a fantasy of a huge business because we have a solution.”
Sorry. Not yet. Marketing and closing deals are different from solving findability issues in an enterprise.
Stephen E Arnold, March 27, 2024
HP Autonomy: A Modest Disagreement Escalates
May 15, 2023
Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
About 12 years ago, Hewlett Packard acquired Autonomy. The deal was, as I understand the deal, HP wanted to snap up Autonomy to make a move in the enterprise services business. Autonomy was one of the major providers of search and some related content processing services in 2010. Autonomy’s revenues were nosing toward $800 million, a level no other search and retrieval software company had previously achieved.
However, as Qatalyst Partners reported in an Autonomy profile, the share price was not exactly hitting home runs each quarter:
Source: Autonomy Trading and Financial Statistics, 2011 by Qatalyst Partners
After some HP executive turmoil, the deal was done. After a year or so, HP analysts determined that the Silicon Valley company paid too much for Autonomy. The result was high profile litigation. One Autonomy executive found himself losing and suffering the embarrassment of jail time.
“Autonomy Founder Mike Lynch Flown to US for HPE Fraud Trial” reports:
Autonomy founder Mike Lynch has been extradited to the US under criminal charges that he defrauded HP when he sold his software business to them for $11 billion in 2011. The 57-year-old is facing allegations that he inflated the books at Autonomy to generate a higher sale price for the business, the value of which HP subsequently wrote down by billions of dollars.
Although I did some consulting work for Autonomy, I have no unique information about the company, the HP allegations, or the legal process which will unspool in the US.
In a recent conversation with a person who had first hand knowledge of the deal, I learned that HP was disappointed with the Autonomy approach to business. I pushed back and pointed out three things to a person who was quite agitated that I did not share his outrage. My points, as I recall, were:
- A number of search-and-retrieval companies failed to generate revenue sufficient to meet their investors’ expectations. These included outfits like Convera (formerly Excalibur Technologies), Entopia, and numerous other firms. Some were sold and were operated as reasonably successful businesses; for example, Dassault Systèmes and Exalead. Others were folded into a larger business; for example, Microsoft’s purchase of Fast Search & Transfer and Oracle’s acquisition of Endeca. The period from 2008 to 2013 was particularly difficult for vendors of enterprise search and content processing systems. I documented these issues in The Enterprise Search Report and a couple of other books I wrote.
- Enterprise search vendors and some hybrid outfits which developed search-related products and services used bundling as a way to make sales. The idea was not new. IBM refined the approach. Buy a mainframe and get support free for a period of time. Then the customer could pay a license fee for the software and upgrades and pay for services. IBM charged me $850 to roll a specialist to look at my three out-of-warranty PC 704 servers. (That was the end of my reliance on IBM equipment and its marvelous ServeRAID technology.) Libraries, for example, could acquire hardware. The “soft” components had a different budget cycle. The solution? Split up the deal. I think Autonomy emulated this approach and added some unique features. Nevertheless, the market for search and content related services was and is a difficult one. Fast Search & Transfer had its own approach. That landed the company in hot water and the founder on the pages of newspapers across Scandinavia.
- Sales professionals could generate interest in search and content processing systems by describing the benefits of finding information buried in a company’s file cabinets, tucked into PowerPoint presentations, and sleeping peacefully in email. Like the current buzz about OpenAI and ChatGPT, expectations are loftier than the reality of some implementations. Enterprise search vendors like Autonomy had to deal with angry licensees who could not find information, heated objections to the cost of reindexing content to make it possible for employees to find the file saved yesterday (an expensive and difficult task even today), and howls of outrage because certain functions had to be coded to meet the specific content requirements of a particular licensee. Remember that a large company does not need one search and retrieval system. There are many, quite specific requirements. These range from engineering drawings in the R&D center to the super sensitive employee compensation data, from the legal department’s need to process discovery information to the mandated classified documents associated with a government contract.
These issues remain today. Autonomy is now back in the spot light. The British government, as I understand the situation, is not chasing Dr. Lynch for his methods. HP and the US legal system are.
The person with whom I spoke was not interested in my three points. He has a Harvard education and I am a geriatric. I will survive his anger toward Autonomy and his obvious affection for the estimable HP, its eavesdropping Board and its executive revolving door.
What few recall is that Autonomy was one of the first vendors of search to use smart software. The implementation was described as Neuro Linguistic Programming. Like today’s smart software, the functioning of the Autonomy core technology was a black box. I assume the litigation will expose this Autonomy black box. Is there a message for the ChatGPT-type outfits blossoming at a prodigious rate?
Yes, the enterprise search sector is about to undergo a rebirth. Organizations have information. Findability remains difficult. The fix? Merge ChatGPT type methods with an organization’s content. What do you get? A party which faded away in 2010 is coming back. The Beatles and Elvis vibe will be live, on stage, act fast.
Stephen E Arnold, May 15, 2023
The Mysterious Knowledge Management and Enterprise Search Magic Is Coming Back
March 23, 2023
Note: This post was written by a real, still alive dinobaby. No smart software needed yet.
In the glory days of pre-indictment enterprise search innovators, some senior managers worried that knowledge loss would cost them. The fix, according to some of the presentations I endured, was to use an enterprise search system from one of the then-pre-eminent vendors. No, I won’t name them, but you can hunt for a copy of my Enterprise Search Report (there are three editions of the tome) and check out the companies’ technology which I analyzed.
The glory days, 2nd edition is upon us if I understand “A Testing Environment for AI and Language Models.”
Not having information generates “digital friction.” I noted this passage:
According to a recent survey of 1,000 IT managers at large enterprises, 67% expressed concern over the loss of knowledge and expertise when employees leave the company. The cost of knowledge loss and inefficient knowledge sharing is significant, with IDC estimating that Fortune 500 companies lose approximately $31.5 billion each year by failing to share knowledge. This figure is particularly alarming, given the current uncertain economic climate. By improving information search and retrieval tools, a Fortune 500 company with 4,000 employees could save roughly $2 million per month in lost productivity. Intelligent enterprise search is a critical tool that can help prevent information islands and enable organizations to effortlessly find, surface, and share knowledge and corporate expertise. Seamless access to knowledge and expertise within the digital workplace is essential. The right enterprise search platform can connect workers to knowledge and expertise, as well as connect disparate information silos to facilitate discovery, innovation, and productivity.
Yes, the roaring 2000s all over again.
The only question I have is what start up will be the “new” Autonomy, Delphi, Entopia, Fast Search & Transfer, Grokker, Klevu, or Uniqa, et al? Which of the 2nd generation of enterprise search systems will have an executive accused of financial Fancy Dancing? What will the 2nd edition’s buzzwords do to surf on AI/ML, neural nets, and deep learning?
Exciting. Will these new systems solve the problem of employees’ quitting and taking their know how and “knowledge” with them? Sure. (Why should I be the one to suggest that investors’ dreams could be like Silicon Valley Bank’s risk management methods? And what about “knowledge”? No problem, of course.
Stephen E Arnold, March 23, 2023
Enterprise Search: Bold Predictions and a Massive Infowarp
July 12, 2022
Writing about enterprise search was a “thing” in the mid to late 2000s. There were big deals. Microsoft bought Fast Search & Transfer as an investigation in the firm’s financial methods. Then the Autonomy acquisition happened, and, as you may know, that sage continues to unfold. Vivisimo was acquired by IBM, and it’s rather useful clustering and metasearch system disappeared into the outstanding management environment of Big Blue. Enterprise search vendors flipped and pivoted: Some became customer support systems. Others morphed into smart news. A few from the Golden Age of Search hung in, and these firms are still pitching enterprise search but with a Silicon Valley, New Era spin.
I read “Enterprise Search Market to Witness Massive Growth by 2028: IBM Corporation, Lucid Work Incorporation [not the well funded name of the outfit, however], Microsoft Corporation, Dassault System” [not the correct spelling of the firm’s name]. How much can one trust a write up which misspells the names of the companies subjected to an intensive analysis process?
My answer is, “Not at all.”
Let’s take a look at some of the information in the write up.
The list of vendors included in the report is:
Attivio Software Incorporation
Coveo Corporation
Dassault Systems S.A. [The accepted spelling is Dassault Systèmes]
IBM Corporation
Lucid Work Incorporation. [Wow, the name of the company is LucidWorks. Pretty careless.]
Microsoft Corporation
SAP AG
Oracle Corporation
X1 Technologies Inc.
Okay, the names of some of the companies is incorrect. Bad.
Second, I loved this passage:
The research covers the most recent information about current events. This information is useful for businesses planning to produce significantly improved things, as well as for customers gaining an idea of what will be available in the future.
I have zero clue what this quoted passage means. Current events to me and many others involves the financial crisis, Russia’s non war war, and assorted pandemics. Monkeypox. Boo!
Third, did you notice that the vendor providing search and retrieval to numerous companies and to many vendors is not included in the report. I am referring to Elastic, cheerleader for the widely popular Elasticsearch. Why omit the vendor with many installations. I can see skipping over Algolia, Sinequa, and Yext, among others. But Elastic? Yikes.
Here’s my take on this report:
- I am not sure it will be useful
- I don’t see an indication that the features of the specific search engines are compared, contrasted, and evaluated. Oracle has a number of search solutions. Will these be evaluated or will the analysts focus on structured query language, ignoring Endeca and other systems the firm owns?
- Misspellings are easy to make with smart software helpfully replacing words automatically. However, getting the company names wrong is a red light.
Net net: Enterprise search will indeed witness – that is, be an observer of rapid growth in certain software sectors – I just think that enterprise search is now a utility. More modern methods of fusing and locating high value information are available. Buying a report which describes ageing dinosaurs may not be a prudent use of available funds.
Stephen E Arnold, July 12, 2022
SeMI: Yet Another Smart Search System
May 2, 2022
Once upon a time, search engines were incapable of understanding queries phrased like a question. With the advent of smarter technology, particularly machine learning and AI, search engines are almost as smart as a human. TechCrunch discusses how one company has created its take on smart search: “SeMI Technologies’ Search Engine Opens Up New Ways To Query Your Data.”
SeMi Technologies invented Weaviate, a vector search engine that uses a unique AI-first database with machine outputting vectors aka embedding. The company wishes to commoditize the technology and has an open source business model. Bob can Luijt is the CEO and co-founder of SeMI. He wants his vector search engine to remain open source so it can help people and businesses that truly need it. SeMi did not create the models used in Weaviate, instead, they deliver the power and systems recommendations.
SeMI Technologies has had over one hundred use cases, including startups powered by vector search engines and use Weaviate to deliver results. SeMi was not actively seeking investors when it received funding in 2020:
“SeMI raised a $1.2 million seed in August 2020 from Zetta Venture Partners and ING Ventures and since then has been on the radar of venture capital companies. Since then, its software has been downloaded almost 750,000 times, growth of about 30% per month. Van Luijt didn’t give specifics on the company’s growth metrics, but did say the number of downloads can correlate to sales of enterprise licenses and managed services. In addition, the spike in usage and understanding of the added value of Weaviate has caused all growth metrics to go up, and the company to exhaust its seed funding.
The company has received more funding in a Series A round that ended with $16 million. The CEO will use the money to hire more employees in the US and Europe, expand its open source community, focus on go-to-market and products centered on the open source core, and invest in research where machine learning overlaps with computer science.
Whitney Grace, May 2, 2022
Deepset: Following the Trail of DR LINK, Fast Search and Transfer, and Other Intrepid Enterprise Search Vendors
April 29, 2022
I noted a Yahooooo! news story called “Deepset Raises $14M to Help Companies Build NLP Apps.” To me the headline could mean:
Customization is our business and services revenue our monetization model
Precursor enterprise search vendors tried to get gullible prospects to believe a company could install software and employees could locate the information needed to answer a business question. STAIRS III, Personal Library Software / SMART, and the outfit with forward truncation (InQuire) among others were there to deliver.
Then reality happened. Autonomy and Verity upped the ante with assorted claims. The Golden Age of Enterprise Search was poking its rosy fingers through the cloud of darkness related to finding an answer.
Quite a ride: The buzzwords sawed through the doubt and outfits like Delphis, Entopia, Inference, and many others embraced variations on the smart software theme. Excursions into asking the system a question to get an answer gained steam. Remember the hand crafted AskJeeves or the mind boggling DR LINK; that was, document retrieval via linguistic knowledge.
Today there are many choices for enterprise search: Free Elastic, Algolia, Funnelback now the delightfully named Squiz, Fabasoft Mindbreeze, and, of course, many, many more.
Now we have Deepset, “the startup behind the open source NLP framework Haystack, not to be confused with Matt Dunie’s memorable “haystack with needles” metaphor, the intelware company Haystack, or a basic piles of dead grass.
The article states:
CEO Milos Rusic co-founded Deepset with Malte Pietsch and Timo Möller in 2018. Pietsch and Möller — who have data science backgrounds — came from Plista, an adtech startup, where they worked on products including an AI-powered ad creation tool. Haystack lets developers build pipelines for NLP use cases. Originally created for search applications, the framework can power engines that answer specific questions (e.g., “Why are startups moving to Berlin?”) or sift through documents. Haystack can also field “knowledge-based” searches that look for granular information on websites with a lot of data or internal wikis.
What strikes me? Three things:
- This is essentially a consulting and services approach
- Enterprise becomes apps for a situation, department, or specific need
- The buzzwords are interesting: NLP, semantic search, BERT, and humor.
Humor is a necessary quality which trying to make decades old technology work for distributed, heterogeneous data, email on a sales professionals mobile, videos, audio recordings, images, engineering diagrams along with the nifty datasets for the gizmos in the illustration, etc.
A question: Is $14 million enough?
Crickets.
Stephen E Arnold, April 29, 2022
Enterprise Search Vendor Buzzword Bonanza!
April 25, 2022
Enterprise search vendors are similar to those two Red Bull-sponsored wizards who wanted to change aircraft—whilst in flight. How did that work out? The pilots survived. That aircraft? Yeah, Liberty, Liberty Mutual as the YouTube ads intone.
Enterprise search vendors want to become something different. Typical repositionings include customer support which entails typing in a word and scanning for matches and business intelligence which often means indexing content, matching words and phrases on a list, and generating alerts. There are other variations which include analyzing content and creating a report which tallies text messages from outraged customers.
Let’s check out reality. “Enterprise search” means finding information. Words and phrase are helpful. Users want these systems to know what is needed and then output it without asking the user to do anything. The challenge becomes assigning a jazzy marketing hook to make enterprise search into something more vital, more compelling, and more zippy.
Navigate to “What Should We Remember?” Bonanza. The diagram is a remarkable array of categories and concepts tailor-made for search marketers. Here’s an example of some of the zingy concepts:
- Zero-risk bias
- Social comparison
- Fundamental attribution
- Barnum effect — Who? The circus person?
Now mix in natural language processing, semantic analysis, entity extraction, artificial intelligence, and — my fave — predictive analytics.
How quickly will outfits in the enterprise search sector gravitate to these more impactful notions? Desperation is a motivating factor. Maybe weeks or months?
Stephen E Arnold, April 25, 2022
Enterprise Search Vendors: Sure, Some Are Missing But Does Anyone Know or Care?
April 20, 2022
I came across a site called Software Suggest and its article “Coveo Enterprise Search Alternatives.” Wow. What’s a good word for bad info?
The system generated 29 vendors in addition to Coveo. The options were not in alphabetical order or any pattern I could discern. What outfits are on the list? Here are the enterprise search vendors for February 2022, the most recent incarnation of this list. My comments are included in parentheses for each system. By the way, an alternative is picking from two choices. This is more correctly labeled “options.” Just another indication of hippy dippy information about information retrieval.
AddSearch (Web site search which is not enterprise search)
Algolia (a publicly trade search company hiring to reinvent enterprise search just as Fast Search & Transfer did more than a decade ago)
Bonsai.io (another Eleasticsearch repackager)
Coveo (no info, just a plea for comments)
C Searcher(from HNsoft in Portugal. desktop search last updated in 2018 according to the firm’s Web site)
CTX Search (the expired certificate does bode well)
Datafari (maybe open source? chat service has no action since May 2021)
Expertrec Search Engine (an eCommerce solution, not an enterprise search system)
Funnelback (the name is now Squiz. The technology Australian)
Galaktic (a Web site search solution from Taglr, an eCommerce search service)
IBM Watson (yikes)
Inbenta (A Catalan outfit which shapes its message to suit the purchasing climate)
Indica Enterprise Search (based in the Netherlands but the name points to a cannabis plant)
Intrasearch (open source search repackaged with some spicy AI and other buzzwords)
Lateral (the German company with an office in Tasmania offers an interface similar to that of Babel Street and Geospark Analytics for an organization’s content)
Lookeen (desktop search for “all your data”. All?)
OnBase ECM (this is a tricky one. ISYS Search sold to Lexmark. Lexmark sold to Highland. Highland appears to be the proud possessor of ISYS Search and has grafted it to an enterprise content management system)
OpenText (the proud owner of many search systems, including Tuxedo and everyone’s fave BRS Search)
Relevancy Platform (three years ago, Searchspring Relevancy Platform was acquired by Scaleworks which looks like a financial outfit)
Sajari (smart site search for eCommerce)
SearchBox Search (Elasticsearch from the cloud)
Searchify (a replacement for Index Tank. who?)
SearchUnify (looks like a smart customer support system, a pitch used by Coveo and others in the sector)
Site Search 360 (not an enterprise search solution in my opinion)
SLI Systems (eCommerce search, not enterprise search, but I could be off base here)
Team Search (TransVault searches Azure Tenancy set ups)
Wescale (mobile eCommerce search)
Wizzy (the name is almost as interesting as the original Purple Yogi system and another eCommerce search system)
Wuha (not as good a name as Purple Yogi. A French NLP search outfit)
X1 Search (from Idea Labs, X1 is into eDiscovery and search)
This is quite an incomplete and inconsistent list from Software Suggest. It is obvious that there is considerable confusion about the meaning of “enterprise search.” I thought I provided a useful definition in my book “The Landscape of Enterprise Search,” published by Panda Press a decade ago. The book, like me, is not too popular or well known. As a result, the blundering around in eCommerce search, Web site search, application specific search, and enterprise search is painful. Who cares? No one at Software Suggest I posit.
My hunch is that this is content marketing for Coveo. Just a guess, however.
Stephen E Arnold, April xx, 2022
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