Challenges and Concepts in Enterprise Knowledge Discovery

In today's data-driven enterprises, the quest for critical knowledge often feels like searching for a needle in a haystack. Employees routinely spend countless hours hunting for relevant data, resulting in a significant productivity drain. Modern organizations can ill afford this inefficiency. 

Why is this challenge so pernicious – and how can organizations respond? This blog explores data discovery concepts and explains the challenges data consumers in enterprises face in their knowledge discovery efforts.

Challenges in enterprise knowledge discovery

As businesses strive to improve knowledge discovery, they face several challenges that impact efficiency and user experience. These challenges stem from diverse user personas, data duplication, limitations of keyword-based search, and the growing demand for direct insights rather than raw data. Addressing these issues is critical to building an effective knowledge discovery system that enhances search precision, minimizes noise, and delivers meaningful results to both technical and business users.

Diverse user personas

Enterprise users come from diverse backgrounds, ranging from technical experts to business professionals with varying levels of data literacy:

• Technical users prefer hierarchical navigation and are willing to refine search queries but face inefficiencies due to noise.

• Business users often begin with conceptual or term-based searches, leading to confusion when technical results dominate.

For example, users from banking industries have highlighted the difficulty of locating tables with exact names due to interference from irrelevant results. Similarly, legal professionals have expressed frustration when searching acronyms, as technical data objects overshadow business-relevant metrics.

Duplicated content

Duplicated tables are the result of uncontrolled copying of the original tables, often to avoid any unintended impacts to downstream data derived from the original tables. Columns with identical names are common, either because columns propagate through a data journey, or users inadvertently use column names that are already defined for other purposes. Duplicated documents occur because creators did not know something identical or similar already existed.  Identifying and removing duplications is both the means and the end. 

Duplications introduce noise to search results. They affect the target results, reducing their chances of ranking top and making it more difficult for users to identify the target results from a sea of duplications.

Keyword search versus semantic search

If users do not know precisely what they are searching for, traditional keyword searches often yield irrelevant results. In contrast, semantic search, which matches meanings rather than exact words, provides more accurate outcomes.  

For example, if you are an analyst searching with the phrase "European data protection law", you may not receive search results matching the exact search phrase if there are no keyword matches in your data. However, because semantic search understands the meaning of the phrase, you’ll see relevant results that are close in meaning to your search phrase, such as "General Data Protection Regulation (GDPR)." This empowers users to quickly locate information even if they do not know the specific terminology used in data assets across the data intelligence platform.

Search for data versus search for answers

Modern users increasingly seek answers rather than raw data. Instead of just finding the table that contains the data, users expect queries to be made to the table and results analyzed so that their data questions can be answered. This shift requires AI agents to disambiguate users’ intentions, generate queries to retrieve datasets, confirm the validity of results, analyze results for insight, and provide meaningful answers to data questions.

Data discovery concepts

Effective data discovery is essential for users to efficiently locate, explore, and utilize valuable data assets within an organization. Different discovery methods cater to various user needs, whether they require direct access, broad search capabilities, proactive recommendations, or structured navigation. The following concepts—Locate, Search, Suggest, Navigate, and Retrieve—outline the core strategies that empower users to find and leverage data effectively.

• Locate: For users who know exactly what they need, Locate enables quick access through bookmarks, object IDs (e.g. Chrome Extension), or saved search queries. Object names are served as handles to pinpoint desired data assets. 

• Search: Search is the backbone of discovery, designed for broad and exploratory data retrieval. This concept supports users entering free-text queries to access a wide array of potential results. Technical users want to improve the precision of results, while business users benefit from a simplified, intuitive search experience.

• Suggest: Suggestive discovery introduces proactive engagement by recommending relevant data assets based on past searches, cohort behavior, and organizational trends. Suggest leverages machine learning (ML) algorithms to identify patterns and, curatecurating personalized recommendations. Business users who may not have clearly defined search objectives will benefit from inspiration-driven discovery. Suggested content based on behavior or profile affiliation facilitates knowledge sharing, promotes catalog engagement, and helps surface underutilized data assets.

• Navigate: Navigate focuses on guiding users through hierarchical and relational paths within the catalog. This pathway-driven approach lets users traverse metadata, related objects, and taxonomies organically, mimicking exploratory workflows. By linking assets through relationships and providing visual navigation aids, Navigate enhances users’ abilities to follow the lineage of data, trace transformations, and uncover relevant contextual information.

• Retrieve: Retrieval and Search are two closely related capabilities for data discovery. Search through a search engine locates one or a few objects related to a search string, and the ranking of the result is critically important. Retrieval finds data stored in a database that meets certain filtering criteria. Retrieval finds the subset of data that meets the criteria, but the result ranking does not matter.  

Empowering enterprise users through discovery

Alation’s mission is to unlock the power of high-value data and AI, addressing the key challenges in enterprise knowledge discovery. By leveraging advanced algorithms, personalization, and proactive discovery features, Alation redefines productivity and engagement in data-driven organizations.

Curious to see what powerful enterprise data search looks like in practice? Book a demo with us today to see for yourself.