Gartner and Enterprise Search 2014

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:

  1. What are the functional differences among the enterprise search systems?
  2. How does the HP Autonomy “solution” compare to the pre-HP Autonomy solution?
  3. 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.)
  4. What causes a company to move from being a challenger in search to a niche player?
  5. What makes both a printer company and a Microsoft-centric solution qualified to match up with Google and HP Autonomy in enterprise search?
  6. 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

IHS Enterprise Search: Semantic Concept Lenses Are Here

July 29, 2014

I pointed out in http://bit.ly/X9d219 that IDC, a mid tier consulting firm that has marketed my information without permission on Amazon of all places, has rolled out a new report about content processing. The academic sounding title is “The Knowledge Quotient: Unlocking the Hidden Value of Information.” Conflating knowledge and information is not logically satisfying to me. But you may find the two words dusted with “value” just the ticket to career success.

I have not read the report, but I did see a list of the “sponsors” of the study. The list, as I pointed out, was an eclectic group, including huge firms struggling for credibility (HP and IBM) down to consulting firms offering push ups for indexers.

One company on my list caused me to go back through my archive of search information. The firm that sparked my interest is Information Handling Services or IHS or Information Handling Service. The company is publicly traded and turning a decent profit. The revenue of IHS has moved toward $2 billion. If the global economy perks up and the defense sector is funded at pre-drawdown levels, IHS could become a $2 billion company.

IHS is a company with an interesting history and extensive experience with structured and unstructured search. Few of those with whom I interacted when I was working full time considered IHS a competitor to the likes of Autonomy, Endeca, and Funnelback.

In the 2013 10-K on page 20, IHS presents its “cumulative total return” in this way:

image

The green line looks like money. Another slant on the company’s performance can be seen in a chart available from Google Finance.

The Google chart shows that revenue is moving upwards, but operating margins are drifting downward and operating income is suppressed. Like Amazon, the costs for operating and information centric company are difficult to control. Amazon seems to have thrown in the towel. IHS is managing like the Dickens to maintain a profit for its stakeholders. For stakeholders, is the hope is that hefty profits will be forthcoming?

image

Source: Google Finance

My initial reaction was, “Is IHS trying to find new ways to generate higher margin revenue?”

Like Thomson Reuters and Reed Elsevier, IHS required different types of content processing plumbing to deliver its commercial databases. Technical librarians and the competitive intelligence professionals monitoring the defense sector are likely to know about IHS different products. The company provides access to standards documents, regulatory information, and Jane’s military hardware information services. (Yep, Jane’s still has access to retired naval officers with mutton chop whiskers and interesting tweed outfits. I observed these experts when I visited the company in England prior to IHS’s purchase of the outfit.)

The standard descriptions of IHS peg the company’s roots with a trade magazine outfit called Rogers Publishing. My former boss at Booz, Allen & Hamilton loved some of the IHS technical services. He was, prior to joining Booz, Allen the head of research at Martin Marietta, an IHS customer in the 1970s. Few remember that IHS was once tied in with Thyssen Bornemisza. (For those with an interest in history, there are some reports about the Baron that are difficult to believe. See http://bit.ly/1qIylne.)

Large professional publishing companies were early, if somewhat reluctant, supporters of SGML and XML. Running a query against a large collection of structured textual information could be painfully slow when one relied on traditional relational database management systems in the late 1980s. Without SGML/XML, repurposing content required humans. With scripts hammering on SGML/XML, creating new information products like directories and reports eliminated the expensive humans for the most part. Fewer expensive humans in the professional publishing business reduces costs…for a while at least.

IHS climbed on the SGML/XML diesel engine and began working to deliver snappy online search results. As profit margins for professional publishers were pressured by increasing marketing and technology costs, IHS followed the path of other information centric companies. IHS began buying content and services companies that, in theory, would give the professional publishing company a way to roll out new, higher margin products. Even secondary players in the professional publishing sector like Ebsco Electronic Publishing wanted to become billion dollar operations and then get even bigger. Rah, rah.

These growth dreams electrify many information company’s executives. The thought that every professional publishing company and every search vendor are chasing finite or constrained markets does not get much attention. Moving from dreams to dollars is getting more difficult, particularly in professional publishing and content processing businesses.

My view is that packaging up IHS content and content processing technology got a boost when IHS purchased the Invention Machine in mid 2012.

Years ago I attended a briefing by the founders of the Invention Machine. The company demonstrated that an engineer looking for a way to solve a problem could use the Invention Machine search system to identify candidate systems and methods from the processed content. I recall that the original demonstration data set was US patents and patent applications. My thought was that an engineer looking for a way to implement a particular function for a system could — if the Invention Machine system worked as presented — could present a patent result set. That result set could be scanned to eliminate any patents still in force. The resulting set of patents might yield a procedure that the person looking for a method could implement without having to worry about an infringement allegation. The original demonstration was okay, but like most “new” search technologies, Invention Machine faced funding, marketing, and performance challenges. IHS acquired Invention Machine, its technologies, its Eastern European developers, and embraced the tagging, searching, and reporting capabilities of the Invention Machine.

The Goldfire idea is that an IHS client can license certain IHS databases (called “knowledge collections”) and then use Goldfire / Invention Machine search and analytic tools to get the knowledge “nuggets” needed to procure a missile guidance component.

The jargon for this finding function is “semantic concept lenses.” If the licensee has content in a form supported by Goldfire, the licensee can search and analyze IHS information along with information the client has from its own sources. A bit more color is available at http://bit.ly/WLA2Dp.

The IHS search system is described in terms familiar to a librarian and a technical analyst; for example, here’s the attributes for Goldfire “cloud” from an IHS 2013 news release:

  • “Patented semantic search technology providing precise access to answers in documents. [Note: IHS has numerous patents but it is not clear what specific inventions or assigned inventions apply directly to the search and retrieval solution(s)]
  • Access to more than 90 million scientific and technical “must have” documents curated by IHS. This aggregated, pre-indexed collection spans patents, premium IHS content sources, trusted third-party content providers, and the Deep Web.
  • The ability to semantically index and research across any desired web-accessible information such as competitive or supplier websites, social media platforms and RSS feeds – turning these into strategic knowledge assets.
  • More than 70 concept lenses that promote rapid research, browsing and filtering of related results sets thus enabling engineers to explore a concept’s definitions, applications, advantages, disadvantages and more.
  • Insights into consumer sentiment giving strategy, product management and marketing teams the ability to recognize customer opinions, perceptions, attitudes, habits and expectations – relative to their own brands and to those of their partners’ and competitors’ – as expressed in social media and on the Web.”

Most of these will resonate with those familiar with the assertions of enterprise search and content processing vendors. The spin, which I find notable, is that IHS delivers both content and information retrieval. Most enterprise search vendors provide technology for finding and analyzing data. The licensee has to provide the content unless the enterprise search vendor crawls the Web or other sources, creates an archive or a basic index, and then provides an interface that is usually positioned as indexing “all content” for the user.

According to Virtual Strategy Magazine (which presumably does not cover “real” strategy), I learned that US 8666730:

covers the semantic concept “lenses” that IHS Goldfire uses to accelerate research. The lenses correlate with the human knowledge system, organizing and presenting answers to engineers’ or scientists’ questions – even questions they did not think to ask. These lenses surface concepts in documents’ text, enabling users to rapidly explore a concept’s definitions, applications, advantages, disadvantages and more.

The key differentiator is claimed to move IHS Goldfire up a notch. The write up states:

Unlike today’s textual, question-answering technologies, which work as meta-search engines to search for text fragments by keyword and then try to extract answers similar to the text fragment, the IHS Goldfire approach is entirely unique – providing relevant answers, not lists of largely irrelevant documents. With IHS Goldfire, hundreds of different document types can be parsed by a semantic processor to extract semantic relationships like subject-action-object, cause-and-effect and dozens more. Answer-extraction patterns are then applied on top of the semantic data extracted from documents and answers are saved to a searchable database.

According to Igor Sovpel, IHS Goldfire:

“Today’s engineers and technical professionals are underserved by traditional Internet and enterprise search applications, which help them find only the documents they already know exist,” said Igor Sovpel, chief scientist for IHS Goldfire. “With this patent, only IHS Goldfire gives users the ability to quickly synthesize optimal answers to a variety of complex challenges.”

Is IHS’ new marketing push in “knowledge” and related fields likely to have an immediate and direct impact on the enterprise search market? Perhaps.

There are several observations that occurred to me as I flipped through my archive of IHS, Thyssen, and Invention Machine information.

First, IHS has strong brand recognition in what I would call the librarian and technical analyst for engineering demographic. Outside of lucrative but quite niche markets for petrochemical information or silhouettes and specifications for the SU 35, IHS suffers the same problem of Thomson Reuters and Wolters Kluwer. Most senior managers are not familiar with the company or its many brands. Positioning Goldfire as an enterprise search or enterprise technical documentation/data analysis tool will require a heck of a lot of effective marketing. Will positioning IHS cheek by jowl with IBM and a consulting firm that teaches indexing address this visibility problem? The odds could be long.

Second, search engine optimization folks can seize on the name Goldfire and create some dissonance for IHS in the public Web search indexes. I know that companies like Attivio and Microsoft use the phrase “beyond search” to attract traffic to their Web sites. I can see the same thing happening. IHS competes with other professional publishing companies looking for a way to address their own marketing problems. A good SEO name like “Goldfire” could come under attack and quickly. I can envision lesser competitors usurping IHS’ value claims which may delay some sales or further confuse an already uncertain prospect.

Third, enterprise search and enterprise content analytics is proving to be a difficult market from which to wring profitable, sustainable revenue. If IHS is successful, the third party licensees of IHS data who resell that information to their online customers might take steps to renegotiate contracts for revenue sharing. IHS will then have to ramp up its enterprise search revenues to keep or outpace revenues from third party licensees. Addressing this problem can be interesting for those managers responsible for the negotiations.

Finally, enterprise search has a lot of companies planning on generating millions or billions from search. There can be only one prom queen and a small number of “close but no cigar” runner ups. Which company will snatch the crown?

This IHS search initiative will be interesting to watch.

Stephen E Arnold, July 29, 2014

I2E Semantic Enrichment Unveiled by Linguamatics

July 21, 2014

The article titled Text Analytics Company Linguamatics Boosts Enterprise Search with Semantic Enrichment on MarketWatch discusses the launch of 12E Semantic Enrichment from Linguamatics. The new release allows for the mining of a variety of texts, from scientific literature to patents to social media. It promises faster, more relevant search for users. The article states,

“Enterprise search engines consume this enriched metadata to provide a faster, more effective search for users. I2E uses natural language processing (NLP) technology to find concepts in the right context, combined with a range of other strategies including application of ontologies, taxonomies, thesauri, rule-based pattern matching and disambiguation based on context. This allows enterprise search engines to gain a better understanding of documents in order to provide a richer search experience and increase findability, which enables users to spend less time on search.”

Whether they are spinning semantics for search, or if it is search spun for semantics, Linguamatics has made their technology available to tens of thousands of users of enterprise search. Representative John M. Brimacombe was straightforward in his comments about the disappointment surrounding enterprise search, but optimistic about 12E. It is currently being used by many top organizations, as well as the Food and Drug Administration.

Chelsea Kerwin, July 21, 2014

Sponsored by ArnoldIT.com, developer of Augmentext

13 Career Jeopardizing Enterprise Search Issues

July 6, 2014

The ArnoldIT team has combed through our archive of enterprise search data. We have identified the top 13 surprises that enterprise search delivers to licensees. Get hit with several of these surprises and you might find yourself seeking a future in a different line of work.

13 Users don’t like the new system or the old system, for that matter. Dissatisfaction with enterprise search, regardless of vendor, runs at 55 to 70 percent. See Successful Enterprise Search Management at http://www.galatea.co.uk/index.php?option=com_content&task=view&id=35&Itemid=53

12 No one pays attention to search costs until the CFO conducts an audit. Cost overruns plague nine out of 10 enterprise search deployments. The reason is that a comprehensive compilation of costs is not part of an enterprise search deployment. When a system crashes, the costs for emergency and rush work comes from a different line item. Customization is usually taken from a different budget allocation. Consultants and contractors are paid from another budget allocation. When the costs are added up, everyone seems surprised at the money spent for a system few find satisfactory. The hunt for a scapegoat is on.

11 Open source search and proprietary search solutions differ little in costs. Aside from an initial licensing fee, the costs for customization, optimization, contractors, programming, and enhancement are essentially the same.

10 Many major enterprise search systems are now based on open source software. The reason is that the cost for the basic functions are rising and are difficult to control. Therefore, vendors use open source and concentrate on extra cost add ons.

9 Every enterprise search system struggles to process content without human intervention, additional “connectors,” extract transform load activities, and original scripting. When content cannot be acquired, the few who notice will squawk, often loudly.

8 Latency creates a problem because new or changed content imposes significant costs on the licensee to cope with the need to process content in near real time and then refresh the indexes whether these are state of the art in memory systems or old style spinning discs and cached methods. When a user asks, “Why is the system too slow?, it may be difficult to make improvements when budgets are constrained.

7 Modern systems include adds on that permit faceting, query expansion, and linguistic functions. Unless these are “tuned” by subject matter experts or analysts, the outputs can generate irrelevant or off-query outputs. The notion of “smart, automatic” search and retrieval are often chimera.

6 Users typically do not conduct a thorough, research librarian type of investigation of query results. Enterprise search systems that generate laundry lists of results that are stale or irrelevant will be used by the person running the query. The assumption that online systems are “correct” is held by 95 percent of an enterprise system’s users. Only when a user cannot find a document that is supposed to be in the search index will the user realize that the system is not working as assumed. If the issues arises during a crunch, the prudent search manager will polish that résumé.

5 Enterprise search scaling is expensive and complex. The idea that scaling is seamless and economical is false. Improving the “performance” of an enterprise search system requires correct identification of the particular factor or factors creating latency. More frequent updates may not be possible without re-engineering an enterprise search system’s infrastructure. How much has Google’s core method changed in 14 years? What about Amazon? What about Autonomy, Endeca, Lucene, etc.? The answer is, “Not too much.” Search is very, very complicated.

4 Interface does not improve precision and recall of a search system. Interface and cosmetic design changes are easy to talk about and “more fun” to work on that figuring out how to process content more quickly and update the searchable indexes in with significantly less latency. If users grouse, an interface change won’t silence the critics or slow the proliferation of bootleg systems in units that are dissatisfied with the search status quo.

3 Search with text mining functions often rely on standard methods and algorithm  configurations that licensees cannot modify without specialist training. As a result, many systems output results that may be based on assumptions not germane to the licensee’s content. Hence, outputs purporting to provide insight into business intelligence or predictions may be incorrect. Search is not text mining. Search is not a Silver Bullet for Big Data. Search is pretty much type a query and get a laundry lists of stuff that must be reviewed by a human. Automatic reports are often off point.

2 Search appliances are not money savers. The Google Search Appliance costs as much as Autonomy or Endeca to deploy. The cloud is not the big money savers marketers want me to believe it is. Cloud search solutions reduce the need for capital expense, but the on going costs are comparable to on premises solutions. A search appliance may be like handcuffs. The cloud may be overly complicated. No highways leads to the Magic Kingdom for search. If you think search is a slam dunk, you are misinformed.

1 Enterprise search systems are more alike than different. The reason is that computational methods have not changed much since the first commercial systems became available  in the late 1960s. The differences are created by marketing, not by significantly different numerical recipes. Most users cannot differentiate between or among systems. The concepts of precision and recall are unknown. Users believe that search systems are right almost all the time. Yikes.

Stephen E Arnold, July 6, 2014

Presentation by a NoSQL Leader

July 4, 2014

The purported father of NoSQL, Norman T. Kutemperor, made an appearance at this year’s Enterprise Search & Discovery conference, we learn from “Scientel Presented Advanced Big Data Content Management & Search With NoSQL DB at Enterprise Search Summit in NY on May 13” at IT Business Net. The press release states:

“Norman T. Kutemperor, President/CEO of Scientel, presented on Scientels Enterprise Content Management & Search System (ECMS) capabilities using Scientels Gensonix NoSQL DB on May 13 at the Enterprise Search & Discovery 2014 conference in NY. Mr. Kutemperor, who has been termed the Father of NoSQL, was quoted as saying, When it comes to Big Data, advanced content management and extremely efficient searchability and discovery are key to gaining a competitive edge. The presentation focused on: The Power of Content – More power in a NoSQL environment.”

According to the write-up, Kutemperor spoke about the growing need to manage multiple types of unstructured data within a scalable system, noting that users now expect drag-and-drop functionality. He also asserted that any NoSQL system should automatically extract text and build an index that can be searched by both keywords and sentences. Of course, no discussion of databases would be complete without a note about the importance of security, and Kutemperor emphasized that point as well.

The veteran info-tech company Scientel has been in business since 1977. These days, they focus on NoSQL database design; however, it should be noted that they also design and produce optimized, high-end servers to go with their enterprise Genosix platform. The company makes its home in Bingham Farms, Michigan.

Cynthia Murrell, July 04, 2014

Sponsored by ArnoldIT.com, developer of Augmentext

Enterprise Search Adoption Survey: More Shifting Sand

June 27, 2014

I read “Enterprise Search Adoption Remains Low: Survey.” On one hand the notion that most organizations have trouble finding email like the US Internal Revenue Service is a truism. On the other hand, enterprise search is one of the enterprise applications that promises everything and often disappoints a large percentage of users.

The write up asserts:

It asked 300 enterprise IT security professionals at two major security-themed industry events, and found that only 38% of IT departments have invested in or plan to invest in enterprise search capabilities for a range of reasons topped by security concerns. When asked to choose the biggest obstacle to enterprise search adoption, 68% cited the risk of employees locating and accessing files they should not have permission to view.

The article includes a quote from an outfit called Varonis and a plug for a product with which I am unfamiliar, DatAnswers. The full enterprise search study can be found at http://info.varonis.com/enterprise-search-report

Oh, and the sponsor of the study? Varonis and DatAnswers. Enterprise search is nothing if not consistent in its marketing efforts.

Stephen E Arnold, June 27, 2014

Predicting the Future of Search

June 27, 2014

Enterprise search dates back to the 1960s under IBM, but Google has definitely dictated the average user’s expectations regarding modern day information retrieval. So while the past is important, the future is uncertain and inquiring minds want to know what to expect. Docurated turns to the experts in their article, “Enterprise Search: 14 Industry Experts Predict the Future of Search.”

The article begins:

“We wanted to gain a clearer understanding of current state of the enterprise search industry. Given the steady evolution of enterprise search, we also wanted to gain some insight into what the future may hold. To do so, we gathered a select number of industry experts and asked two simple questions:

1. What is your assessment of today’s enterprise search industry?

2. What do you think the future of ‘search’ will look like?”

The results from the experts are mixed. Few think that the model will change dramatically though many do mention continued innovation in the areas of big data, open source, visual search, and others. And even if all the experts did agree, the future would still be uncertain. Those interested in the future of search should stay tuned in for the latest news as it hits, and just hold on for the ride.

Emily Rae Aldridge, June 27, 2014

Sponsored by ArnoldIT.com, developer of Augmentext

Enterprise Graph Search Changes the Retrieval Game

June 27, 2014

New models of information retrieval are emerging, taking the field away from the traditional keyword format. Facebook made quite a stir with its implementation of graph search, and now experts feel it may have implications for the enterprise. Read more in the Synata article, “Enterprise Graph Search: A Game Changer in Information Retrieval.”

The article begins:

“In Facebook Graph Search, results are based on both the content of the user and their friends’ profiles and the relationships between the user and their friends. They’re personalized for the individual user. But what does this have to do with the enterprise? . . . Because of today’s massively scalable infrastructure platforms, API ubiquity, and graph analysis capabilities, the rapid gains in query understanding and information retrieval techniques are about to have resounding implications for enterprise search.”

The graph model allows for extreme relevance, bringing all the floating connections together in a bigger picture, the graph. And while theorists are saying that this type of technology has huge implications, implementation has yet to be realized. Keep an eye out for the breakthrough of graph search. When it hits SharePoint it will have made the mainstream.

Emily Rae Aldridge, June 27, 2014

Sponsored by ArnoldIT.com, developer of Augmentext

Commonsense Conclusions from Azure Consultant

June 26, 2014

An azure chip consultant explains that enterprise search is just what the doctor ordered for big data; Gartner declares that “Enterprise Search Can Bring Big Data Within Reach.” Um, it seems that search is kind of implied in the term. What good are data and data analysis if there is no way to find and use the information? Actually, research director Darin Stewart is talking about gaining the benefits of big data without the cost and turmoil of “overhauling the data center,” which, apparently, some folks don’t understand is an option. He writes:

“Providing big data functionality without overhauling the data center is achievable with a two-pronged approach that combines augmented enterprise search and distributed computing. Search is very good at finding interesting things that are buried under mounds of information. Enterprise search can provide near-real-time access across a wide variety of content types that are managed in many otherwise siloed systems. It can also provide a flexible and intuitive interface for exploring that information. The weakness of a search-driven approach is the fact that a search application is only as good as the indexes upon which it is built….

“Distributed computing frameworks provide the environment necessary to create these indexes. They are particularly well-suited to efficiently collect extremely large volumes of unprocessed, individually low-value pieces of information and apply the complex analytics and operations that are necessary to transform them into a coherent and cohesive high-value collection. The ability to process numerous source records and apply multiple transformations in parallel dramatically reduces the time that is required to produce augmented indexes across large pools of information.”

The article goes on to point out that cloud-friendly open-source tools to support such a framework are readily available. Stewart shares a link to his Gartner document on the topic (registration required), and is scheduled to speak about it at Gartner’s Catalyst Europe conference, held in London in mid-June.

Cynthia Murrell, June 26, 2014

Sponsored by ArnoldIT.com, developer of Augmentext

Trying To Make A Search More Relevant

June 20, 2014

Here is a thought that does not make much sense when taken in the bigger picture scope. PRLog explains the conundrum in “BA Insight To Discuss How To Make Enterprise Search Relevant Through Unified Information Access.” BA Insight’s CTO Jeff Fried and David Schubmehl, a research director at IDC, will host a webinar that shares the same name as the above article. The webinar will discuss how enterprise search technology is lagging:

“Due to the vast explosion of structured and unstructured data, users are experiencing increasing challenges locating and accessing the critical information and expertise needed to excel in their roles. Even the enterprise search technology that has been implemented to resolve these issues is failing to locate relevant information while providing a sub-par user experience. This can have negative consequences, such as the inability to effectively respond to customer queries, widespread duplication of effort, and decreased employee productivity.”

Fried and Schubmehl will focus on how enterprise search is changing, how organizations are driving demand, and how to make enterprise search a killer application. The bigger question is if BA Insight is using this to make their own products more relevant? Has enterprise search really lost its relevancy or is it one observation? The “unified information access” tag is one used by other companies like Sinequa and Attivio. These companies appear to be cut from the same cloth when touting their talents.

Whitney Grace, June 20, 2014
Sponsored by ArnoldIT.com, developer of Augmentext

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