Coveo recently conducted a survey asking more than 600 tech professionals about the current state of their enterprise search initiatives. A substantial 81% stated that it’s difficult to engage management about search.

Interestingly, most management teams do actually care about the findability of information.

While it’s not uncommon for enterprise search solution to fly under the radar, we wanted to unpack some of the leading obstacles that prevent tech leaders from getting the most out of this core technology. 

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Despite the many challenges (which we’ll explore in short order), there is some consensus around the importance of enterprise search. Eighty four percent of respondents to Coveo’s report do see search as critical to driving digital transformation, for a few notable reasons:

Search Analytics Can Reveal What’s Working and What’s Missing

We know that 92% of respondents use search analytics to find improvements. Of that overwhelming majority: 

  • 67% use it to improve user digital journey
  • 56% use it to profile user preferences and audience attributes
  • 52% use it to inform additional content needs

In short, organizations with enterprise search programs have a tremendous source of data-backed insights (if they want it).

Enterprise Search Improves Most Business Processes

There’s a reason that 98% of respondents say their organizations invested in improving search capabilities to produce more relevant results. Doing so tends to support more efficient and effective business processes.

As for where these improvements manifest, 65% say their search improvement initiatives focused on the intranet or employee portal; another 55% focus their efforts on self-service support and training portals, ostensibly to improve case resolution times and cut call volume (among other important customer service metrics).

A chart details where organizations are investing in enterprise search
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The reasons are myriad. A past report from Coveo found that a lack of alignment was the prevailing obstacle, much of it due to  departmental needs, siloed data, and a lack of IT resources.

While a general lack of alignment around search persists, a few new challenges have risen to the top.

Lack of Executive Engagement

It’s one thing to understand the need for enterprise search capabilities; it’s another thing for business leaders to prioritize these initiatives. Unfortunately, this disconnect affects 96% of respondents to this survey, indicating a corporate-buy-in problem that just won’t go away.

More specifically, 54% of respondents say that business stakeholders lack domain expertise and involvement on the content side, something so critical to effective enterprise search. What’s more, more than half say that business priorities are constantly changing, the implication being that search as an holistic organizational priority remains a moving target. 

A Struggle to Find Expertise (Despite Strong Funding)

The odd thing is that investments in enterprise search technology remain well funded over the past year, with only 2% of respondents reporting a decrease in enterprise search platform budgets. In fact, well over half of respondents reported a slight or significant increase.

Despite all of that investment, 96% of tech leaders still struggle to find expertise in this domain. Here’s the breakdown by category:

A chart details what enterprise search expertise organizations struggle to find.

Conclusion? Enterprise search initiatives require both adequate funding and sufficient talent.

Problems on the Technical Side

Overall, tech professionals are encountering delays while deploying search. Indeed, 59% report complications due to occasional issues, while nearly 30% report many issues and major delays. On the other hand, only 12% say things are streamlined and on schedule.

What gives? 

We already mentioned the lack of alignment, corporate buy-in, and expertise. Yet nearly 100% of tech leaders also point to various technical challenges, such as integrating multiple systems, data indexing, and keeping up with metadata

Sparse Intel On Search Intent

It’s difficult to maximize the return on an enterprise search engine if your team doesn’t have a grip on search intent. When you understand a person’s true end goal — based on data available to you — you’re far better suited to deliver the goods.

Yet today, 99% of tech leaders say they are still relying on manual search tuning. By extension, less than 30% of tech leaders report implementing the following categories “very well” across their search programs: 

  • Factoid
  • Recommendation
  • Compound nouns
  • Nouns vs. verbs
  • Natural language query
  • Similar to
  • Image search
  • Disambiguation
  • Meta knowledge

Without command of all these various search query types, tech leaders will continue struggling to deploy enterprise search to its full potential.

Choosing between build or buy? Here’s why an open source option like Apache Solr or Elastic Enterprise Search might not be the best bet.

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Beyond Elastic: Delivering User-Driven Search

How Search Centers of Excellence Can Help

Today’s typical corporate website has more pages in it than existed on the entirety of the internet 30 years ago.

What makes this growth particularly notable is that the types of content — and as a consequence, the audience of that content — has diverged over time. Different users are going to search the data source they’re most familiar or comfortable with. Think about the different employees across departments.

Someone in accounting is far more likely to search for invoices or sales figures on Microsoft SharePoint. A marketing person is likely to need a document specific to previous campaigns, but that information is in Google Drive. Engineers may look for blueprints or code repositories in Github. How do you combine all of these into a streamlined user experience that personalizes for each, especially when there are concerns about access control?

This often means that different departments will seek different solutions for search. They believe that since their particular search needs are specialized, the search engine that they choose should be, too. Ultimately this results in multiple-yet-disconnected search deployments, retaining the siloed structure that the organization suffers from.

These disconnected systems can be expensive, both in terms of licensing and maintenance, for most moderate to large companies.

Which brings us to … 

What a Search Center of Excellence Really Is

Instead, what organizations need is a center of excellence for their search deployment. An Search Center of Excellence is a team of experts from across various business functions. Their job is to evangelize a unified search platform to each business unit, one that can cross and integrate multiple use cases. 

The idea is to provide a single source of truth, with respect to enterprise search best practices, that’s tailored to the needs of each team. This tends to lead to better adoption, migration, security, and usage of enterprise search capabilities. 

Vital as it might be, the notion of an Search Center of Excellence is rather new to the enterprise environment: only 9% of respondents to the 2023 Tech Report on enterprise search have established one. Then again, close to a third of respondents are starting one. 

Unfortunately, the majority of respondents don’t have an Search Center of Excellence — many of whom don’t plan to begin one at all.

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Learn more about how to effectively build a Search Center of Excellence

In the End, Enterprise Search Doesn’t Have To Be So Difficult

Increasingly, having multiple search engines is unnecessary, especially with the advent of artificial intelligence-based systems. Flexible machine learning cloud search solutions also mean that you need fewer “search engineers” adjusting the search system to optimize it for different target audiences.

These solutions automatically incorporate metadata about the user into the search process, too, such as: 

  • Where the user comes from; 
  • What kind of content they’ve already searched for and encountered; 
  • What they’re really looking for 

Combining this profile information, behavioral data, and content returns a search result page specific to the user’s search behavior. This kind of search experience also learns from the user in real time and is capable of learning from previous queries of similar users. As such, it’s possible to train the search engine to a reasonably high level even before the first search is made. 

And this learned behavior means that there is less need for specific rules engineering, which translates to less fragility when transitioning search from department to department, or replacing sunsetted search systems.

Put simply: there already exists a clear path for centralized enterprise search software that works for everyone.

Dig Deeper

Clearly, intelligent search goes beyond simply looking for content in an index. It relies on user context and historical patterns, as well as more traditional text tools, to determine relevance. It also maintains several feature dimensions tha tsurface content that may be useful in multiple domains. 

All of it carried off transparently in real time, without the complexity inherent to manually tunable systems. 

Frankly it’s one of the best ways to overcome any and all of the challenges to widely adopted, fully maximized enterprise search, most of which I’ve enumerated above..

Interested in more insights from our enterprise search report? Download a copy.

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2023 Tech Report: Enterprise Search – The Unsung Hero