Enterprise Search

Do you have big data? Or just lot of it?

Of course, I’m a search nerd. I've been involved in enterprise search for over 20 years. I see search and big data as related technologies, but in most cases, I do not see them as synonymous.

And I'd also say that, while most enterprises have a lot of data, the term ‘big data’ is not applicable to most organizations.

Consider that Google (and others) define ‘big data’ as “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions”.

Yes, the data that Amazon, Google, Facebook, and others collect qualifies as big data. These companies mine everything you do when you're using their sites. Amazon wants to be able to report “people like you bought …” to sell more product; Google wants to know what ‘people like you’ look at after a query so they can suggest it to the next person like you; Facebook.. well, they want to know what to try to sell you as you chat with and about your friends. Is search involved? Maybe; but more often some strong machine learning and internal analytics are key.

Do consulting firms like Ernst & Young or PWC have big data? Well, my bet is they have alot of information about their clients, business practices, accounting, etc.. but is it ‘big data’? Probably not.

Solr, Elastic and other search technologies can search-enable huge sets of data, so often big data is indexed to be searchable by humans. And both Solr and Elastic come with some great analytical tools.. Kibana on Elastic, and Banana, the port of Kibana for Solr based engines.

But again, is that big data Or just lots of it?

I’d vote the latter.

 

Lucidworks Announces Search as a Service

Not Your Grandfather's Site Search

Some of you may know that New Idea Engineering spun off a company called SearchButton.com in the mid-90s, offering Verity-powered site search for thousands of clients. Sadly, our investors insisted we violate the cardinal rule of business I learned at HP - "be profitable" - so when the "dot-com' bubble exploded, we were back to being New Idea Engineering again!

I remain a fan of hosted search to this day and have been pleasantly surprised to see companies like Algolia; Swiftype - now part of Elastic; and a few other "Search as a service" organizations reinventing the capabilities that we - along with a competitor Atomz> - offered more than 20 years ago! And I include with them the 'cloud-based' search services offed by other established enterprise search companies like Coveo, Microsoft, and until recently, Google.

That said, we've always strived to be fully vendor neutral when it comes to recommending products and services to our clients, and we go out of our way to understand and work with all of the major enterprise search vendors.

Over the last several months I've had the opportunity to use early releases of a product Lucidworks announced this morning: Lucidworks Site Search. As I said, I am a fan of hosted search - or 'search as a service'; and in full disclosure, I was a Lucidworks employee a few years back and yes, a shareholder.

I had an opportunity to talk with Will Hayes, Lucidworks' CEO, about Lucidworks' entry into the hosted search market. Even in its initial release, it looks pretty impressive.

First, Lucidworks Site Search is powered by the newest release of their enterprise product, Fusion 4.0, announced just last week and available for download. One of the exciting new capabilities in Fusion 4 is the full integration with Spark to enhance search with machine learning. It's not quite Google's "people like you" out of the box, but it's a giant step towards AI in the enterprise.

Fusion 4 also provides the ability to create, test, and move into production custom 'portable' search applications. When I first looked at the product last week, I confess to not having the vision to see just how powerful that capability is. It seems that the Fusion Site Search announced this morning is an example of a powerful, custom search app written specifically for site search.

But Lucid has great plans for their Site Search product. It can be run in the cloud, initially on AWS but soon expanding to other cloud services including Azure and Google. And reliability, you can elect to have Lucidworks Site Search span multiple data centers and even across multiple cloud services. As you'd expect in an enterprise product, it supports a wide variety of document formats, security, faceted navigation and a full management console. Finally, I understand that plans are in the works Lucidworks Site Search to be installed "on-prem" and even federate results (respecting document security) from the cloud and from your local instance at the same time.

Over the coming weeks and months I'll be writing more about Fusion 4, Lucid Site Search, and search apps. Stay tuned!

Search Is the User Experience, not the kernel

In the early days of what we now call 'enterprise search', there was no distinction between the search product and the underlying technology. Verity Topic ran on the Verity kernel and Fulcrum ran on the Fulcrum kernel, and that's the way it was - until recently.

In reality, writing the core of an enterprise search product is tough. It has to efficiently create an index of all the works in virtually any kind of file; it has to provide scalability to index millions of documents; and it has to respect document level security using a variety of protocols. And all of this has to deliver results in well under a second. And now, machine learning is becoming an expected capability as well. All for coding that no user will ever see.

Hosted search vendor Swiftype provides a rich search experience for administrators and for uses, but Elastic was the technology under the covers. And yesterday, Coveo announced that their popular enterprise search product will also be available with the Elastic engine rather than only with the existing Coveo proprietary kernel. This marks the start of a trend that I think may become ubiquitous.  

Lucidworks, for example, is synonymous with Solr; but conceptually there is no reason their Fusion product couldn't run on a different search kernel - even on Elastic. However, with their investment in Solr, that does seem unlikely, especially with their ability to federate results from Elastic and other kernels with their App Studio, part of the recent Twigkit acquisition.

Nonetheless, Enterprise search is not the kernel: it's the capabilities exposed for the operation, management, and search experience of the product.

Of course, there are differences between Elastic and Coveo, for example, as well as with other kernels. But in reality, as long as the administrative and user experiences get the work done, what technology is doing the work under the covers matters only in a few fringe cases. And ironically, Elastic, like many other platforms, has its own potentially serious fringe conditions. At the UI level, solving those cases on multiple kernels is probably a lot less intense than managing and maintaining a proprietary kernel.

And this may be an opportunity for Coveo: until now, it's been a Cloud and Windows-only platform. This may mark their entry into multiple-platform environments.

Poor data quality gives search a bad rap

If you’re involved in managing the enterprise search instance at your company, there’s a good chance that you’ve experienced at least some users complain about the poor results they see. 

The common lament search teams hear is “Why didn’t we use Google?” when in fact, sites that implemented the GSA but don’t utilize the Google logo and look, we’ve seen the same complaints.

We're often asked to come in and recommend a solution. Sometimes the problem is simply using the wrong search platform: not every platform handles every user case and requirement equally well. Occasionally, the problem is a poorly or misconfigured search, or simply an instance that hasn’t been managed properly. Even the renowned Google public search engine doesn’t happen by itself, but even that is a poor example: in recent years, the Google search has become less of a search platform and more of a big data analytics engine.

Over the years, we’ve been helping clients select, implement, and manage Intranet search. In my opinion, the problem with search is elsewhere: Poor data quality. 

Enterprise data isn’t created with search in mind. There is little incentive for content authors to attach quality metadata in the properties fields of Adobe PDF Maker, Microsoft Office, and other document publishing tools. To make matters worse, there may be several versions of a given document as it goes through creation, editing, reviews, and updates. And often the early drafts, as well as the final version, are in the same directory or file share. Very rarely does a public facing web site content have such issues.

Sometimes content management systems make it easy to implement what is really ‘search engine optimization’ or SEO; but it seems all too often that the optimization is left to the enterprise search platform to work out.

We have an updated two-part series on data quality and search, starting here. We hope you find it helpful; let us know if you have any questions!

One search to rule them all

(Originally published on LinkedIn)

Lucene was ‘born’ in 1999, created by Doug Cutting; and in 2005, it became a top-level Apache project. That year, Gartner Group announced that the search ‘Leaders’ platforms on their Enterprise Search Magic Quadrant included Autonomy, FAST, Endeca, IBM Omnifind, and Verity. The Google Search Appliance was right on the cusp between ‘Challengers’ and ‘Leaders’. Not many people knew about Lucene; and few who did saw it as much more than a quirky little project.

Just a year later, Yonik Seeley and his employer, CNET Networks, published and donated the Solr search server to the Apache Software Foundation, where it became an incubator project in 2006; the two projects soon merged into a single top-level Apache project. That same year, Gartner narrowed the ‘Leaders’ in their 2006 Magic Quadrant for Search to Autonomy (which acquired Verity the previous year), FAST, and Endeca.

Jump forward to the present. FAST is gone, acquired by Microsoft in 2008 and morphed into SharePoint Search. Hewlett-Packard acquired Autonomy in October of 2011, followed a few weeks later by Oracle’s acquisition of Endeca. Endeca is no longer available as a search platform; and Autonomy is mostly seen as a strategy to keep a large number of HP consultants fully employed, often on compliance applications.

Only a spattering of commercial enterprise search platforms that once flooded the market just a few years back exist any more. While Gartner continues to list 14 or 15 products in their Magic Quadrant Enterprise Search grid, about the only pure commercial products we see any more are the Google Search Appliance and Recommind. And Google recently announced that the appliance is scheduled to go ‘end of life’ over the next few years. All of those bright yellow boxes become really nice Dell servers by the end of 2018.

A new crop of search platforms has grown to fill the void.

As an open source product, Solr has grown in its capabilities, and is now widely used for enterprise search and data applications in corporations and government projects. Solr Cloud extends the platform to a scalable high-availability platform for demanding enterprise and data search applications. Solr is an open source solution.

Cloudera also bundles some interesting extra tools including Solr in their HUE bundle; free to download and free to use as long as you like. Cloudera runs a slightly older but stable release, 4.10; but with a committers Yonik Seeley and Mark Miller, I suspect they’re in a good position.

Hortonworks, a Cloudera competitor, also offers Solr/Solr Cloud in their releases, in partnership with Lucidworks - a company with a large number of committers on staff.

There are also three companies that have proprietary offerings based on open source technology.

Attivio, founded in 2007, is a “Leader” in the most recent Gartner Magic Quadrant for Enterprise Search. Their product, while not open source, nonetheless thrives by combining search, BI, data automation, analytics and more.

Elasticsearch has evolved into a strong platform for search and data analytics, and a number of organizations are finding it useful in some tradition enterprise search applications as well. Elastic has also integrated Kibana, a powerful graphical presentation tool that adds value for content analytics, not just search activity reporting.

Lucidworks Fusion is a relative newcomer to enterprise search. It includes many of the rich architectural features that enterprises expect, including a powerful crawler, connectors, and reporting. With its ‘Anda’ crawler and connectors, admin UI, and reporting, some people see it as a contender to replace the Google Search Appliance.

The one thing that all of these ‘proprietary’ products have in common? They are based on Apache Lucene to deliver critical functionality. And when you consider all of the web sites that use some form of Lucene for their site search, I think you'd agree that it really is a powerful little package. It’s available for virtually any operating systems, and can be integrated using just about any programming language from C/C++ to Java to Perl to Python to .NET.

Even more amazing is that these companies with commercial products based on Lucene – and who compete in the marketplace - actually cooperate when it comes time to fix bugs or add new capabilities to Lucene. Given all of the commercial players that have closed their doors - leaving their customers to find replacement platforms – we’ve reached the point where open-source-based software really is the safe choice now. And universally, Lucene is the common element.

The quirky little search API Doug Cutting put together in 1999 has evolved to be the platform that drives the leading search platforms used in big data, NoSQL, enterprise search, and search analytics. And it doesn’t seem like it’s going to be phasing out any time soon.