While the promise of enterprise search—a centralized, searchable database for all your company's information—is undeniably appealing, implementing it isn't always as smooth as searching the web.

Some software solutions are harder to implement than others, and if your vendor didn't ensure a smooth integrating process, hidden complexities, and potential problems can surface once enterprise search is implemented. 

Enterprise search can suffer from relevance issues, where users struggle to find what they need, and integration problems, where connecting the search tool to your existing systems can also face some problems down the road.

Additionally, data protocol adherence can also be a problem if the search tool struggles to work within your company's existing security framework for accessing and handling sensitive information.

Overall, setting up an enterprise search doesn't have to be hard if your vendor understands that setting it up requires careful planning and consideration of the complexity of the existing business ecosystem. 

The idea is to accomplish integration with your business in a way that makes it searchable without disturbing the existing protocols and tools.

Implementing Enterprise Search Requires Effort

One thing that people often don’t seem to realize is that enterprise search software isn’t a buy-it-and-use-it kind of deal. Setting up an enterprise search requires a commitment to get right at the beginning.

You need to make sure it integrates with existing tools and follows all the security protocols set up in the existing tools and databases. Then, depending on the type of software, it needs to crawl and index the company data correctly to make it easily searchable and relevant to the queries (some solutions overcame this step. More on this later).

On top of that, there can be various levels of customization included because no two businesses are the same.

Enterprise search software customization levels can vary depending on the specific needs of the business. This can include customizing the search interface and filters and integrating with different tools and ranking algorithms to ensure that users find the most relevant results.

To avoid headaches at the beginning, make sure that the enterprise search vendor offers the appropriate customer support and has the team and resources to help you at this stage.

What Are the Issues That Arise with Enterprise Search Implementation?

As we mentioned, implementing enterprise search does face a few potential issues. The good news is that many past issues related to content taxonomy and indexing have been largely diminished with the advent of AI solutions (we will discuss this below), but there are still some potential challenges that you need to be aware of. 

Irrelevant Search Results

The main purpose of enterprise search is to provide employees with quick and relevant search results. There are multiple ways in which this is achieved. 

Enterprise search traditionally relies on data indexing, crawling, and taxonomy to organize and retrieve information. Crawling discovers content, indexing structures it for quick access, and taxonomy categorizes it for relevance and navigability. This method, while effective, often requires extensive manual setup and maintenance and might not fully capture the nuanced relationships between different pieces of content or understand user intent. 

With this method, there is a lot of work involved in categorizing and indexing data to make clear why one thing is relevant to finance and the other to marketing. If this metadata is wrong, then the whole search accuracy is compromised.

Contrastingly, enterprise search software that uses vector search employs AI to transform content into numerical vectors, representing them in a high-dimensional space where distances between vectors signify content similarities. This approach allows for a more nuanced understanding of content and queries, focusing on semantic similarity rather than exact keyword matches. 

When the vectors are used to identify similarity, the connections aren’t “mapped” or “organized.”In other words, vectors care whether pieces of data are similar - they don’t care why they’re similar.

Vector search reduces the need for manual taxonomy and metadata and simplifies content organization. This results in a search that is very precise and relevant without burdensome manual work and categorization.

Bad Adaptation to User Behavior

The very purpose of these systems is to make information retrieval easier for employees. Yet, without understanding how users search and what they struggle with, these systems can become cumbersome and frustrating.

One of the most important features to look fo in enterprise search software is its ability to learn from user behavior.

The solution lies in prioritizing user experience. Look for enterprise search solutions that are built with the user in mind. Features like machine learning and personalization can analyze user search behavior and tailor results accordingly. 

Security Risks

Enterprise search applications, like any IT system, especially those with web connections, face constant security threats. Hackers or even internal threats are potential dangers that can lead to exploiting these systems to gain unauthorized access to a company's most sensitive data.

Determining who has access to what information is one of the most complex challenges within enterprise search. It's a balancing act that affects both solution providers and the companies using the search system. On one hand, you want to empower employees with easy access to the information they need to do their jobs. On the other, safeguarding sensitive data is equally important. Finding the right balance between accessibility and security requires careful planning and robust access controls.        

To combat security concerns, it's important to prioritize enterprise search solutions that implement strict security measures and that have a proven method of integrating with other tools within your business. 

Additionally, most enterprise search solutions require that users make a copy of the company data on their own servers so that they can quickly index it and provide accurate and timely search results.

One of the most important differentiators in the level of security in the enterprise search industry is the ability to avoid this step altogether. As mentioned above, vector searching eliminates the need for data indexing and, therefore, allows for processing an organization's data only in working memory, relying on the meta-level pointers to data, without permanently storing it on enterprise search system servers.

This means that you don't need to trust 3rd party data security protocols with your own sensitive data and that, in addition to an intuitive and accurate searching experience, you can have the maximum level of security.

In addition to this, another indicator that enterprise search takes care of security is SOC 2 (Service Organization Control 2) compliance. This standard testifies that the vendor has undergone independent auditing to verify their security practices.

Integrating Latest Technologies

Artificial Intelligence (AI) and Machine Learning (ML) hold immense potential to revolutionize enterprise search. Solutions like Akooda and Coveo demonstrate how these technologies can deliver more relevant and intuitive search experiences. However, unlocking the full potential of AI and ML within enterprise search remains a complex undertaking.

While developing AI and ML algorithms is a challenge in itself, integrating them into existing enterprise search systems presents further difficulties. 

Additionally, the rise of Large Language Models (LLMs) opens a new frontier. Integrating these LLMs through APIs can enable generative AI capabilities within the workplace, allowing search systems to summarize documents, answer complex questions, and even generate new content. This integration, however, demands careful consideration to ensure the technology aligns with an organization's specific needs and security protocols.

Overcoming Issues with Akooda

Akooda provides dedicated support throughout the implementation process to ensure that businesses overcome common and unique enterprise search implementation issues. 

Our team's hands-on approach helps integrate advanced search solutions, makes them suitable to specific business needs, and ensures a smooth operation within existing frameworks.