Enterprise Search

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.

Search, the Enterprise Orphan

It seems that everywhere I go, I hear how bad enterprise search is. Users, IT staff, and management complain, and eventually organizations decide that replacing their existing vendor is the best solution. I’d wager that companies switch their search platforms more frequently than any other mission-critical application

While the situation is frustrating for organizations that use search, the current state isn’t as bad for the actual search vendors: if prospects are universally unhappy with a competing product, it’s easier to sell a replacement technology that promises to be everything the current platform is not. It may seem that the only loser is the current vendor; and they are often too busy converting new customers to the platform to worry much.

But in fact, switching search vendors every few years is a real problem for the organization that simply wants its employees and users to find the right content accurately, quickly and without any significant user training. After all, employees are born with the ability to use Google!

 

Higher level story

Why is enterprise search so bad? In my experience, search implemented and managed properly is pretty darned good. As I see it, the problem is that at most organizations, search doesn’t have an owner.  On LinkedIn, a recent search for “vice president database” jobs shows over 1500 results. Searching for “vice president enterprise search”? Zero hits.

This means that search, recognized as mission-critical by senior management, often doesn’t have an owner outside of IT, whose objective is to keep enterprise applications up and running. Search may be one of the few enterprise applications where “up and running” is just not good enough.

Sadly, there is often no “search owner”; no “search quality team”; and likely no budget for measuring and maintaining result quality.

Search Data Quality

We’ve all heard the expression “Garbage In, Garbage Out”. What is data quality when it comes to search? And how can you measure it?

Ironically, enterprise content authors have an easy way to impact search data quality; but few use it. The trick? Document Properties – also known as ‘metadata’.

When you create any document, there is always data about the document – metadata. Some of the metadata ‘just happens’: the file date, its size, and the file name and path. Other metadata depends on the author-provided properties like a title, subject, and other fielded data like that maintained in the Office ‘Properties’ tab. And there are tools like the Stanford Named Entity Recognition tool (licensed under the GNU General Public License) that can perform advanced metadata extraction from the full text of a document

Some document properties happen automatically. In Microsoft Office, for example, the Properties form provides a way to define field values including the author name, company and other fields. The problem is, few people go to the effort of filling the property fields correctly, so you end up for bad metadata. And bad data is arguably worse than no metadata.

On the enterprise side, I heard about an organization that wanted to reward employees who authored popular content for the intranet. The theory was that recognizing and rewarding useful content creation would help improve the overall quality and utility of the corporate intranet.

An organization we did a project for a few years ago were curious about poor metadata in their intranet document repository, so they did a test. After some testing of their Microsoft Office documents, , they discovered that one employee had authored nearly half of all their intranet content! It turned out that one employee, an Office Assistant, had authored the document that everyone in the origination used as the starting point for a of their common standard reports.

Solving the Problem

Enterprise search technology has advanced to an amazing level. A number of search vendors have even integrated machine learning tools like Spark to surface popular content for frequent queries. And search-related reporting has become a standard part of nearly all search product offerings, so metrics such as top queries and zero hits are available and increasingly actionable.

To really take advantage of these new technological solution, you need to have a team of folks to actively participate in making your enterprise search a success so you can break the loop of “buy-replace”.

Start by identifying an executive owner, and then pull together a team of co-conspirators who can help. Sometimes just by looking at the reports you have and taking action can go a long way.

Review the queries with no results and see if there are synonyms that can find the right content without even changing the content.  Identify the right page for your most popular queries and define one or two “best bets’. If you find that some frequent queries don’t really have relevant content? Work with your web team to create appropriate content.

Funding? Find the right person in your organization to convince that spending a little money on fixing the problems now will break the “buy-replace’ problem and save some significant but needlessly recurring expenses.

Like so many things, a little ongoing effort can solve the problem.