Traditionally, the term data retrieval has referred to the use of query languages to retrieve structured data from databases. However, as data volumes expand and technology advances, the term has become associated with the retrieval of myriad data types, whether structured or unstructured.
Data retrieval is used by organizations to leverage the increasingly rich collections of data both within their own systems and from third-party repositories. Through data retrieval tools, enterprise users, researchers and others can find answers to questions and locate key data points—from sources that would be difficult or even prohibitive to access through manual approaches.
Once limited to rudimentary database searches, data retrieval systems today are often enhanced with automation and artificial intelligence (AI) technologies that can manage complex data requests, connect to more knowledge bases and dynamically optimize query execution. Machine learning, natural language processing and retrieval augmented generation (RAG) help to improve the accuracy and relevance of data provided in response to queries.
Smart decision-making happens when organizations can cull insights from high-quality data.
But before analysis can take place, organizations must access that data. This task can be especially challenging when the data resides within a large dataset or vast data estate, such as an expansive scientific research database or a sprawling hybrid multicloud storage system.
Explosive data growth intensifies these challenges: More than 400 million terabytes of data are created each day, according to some estimates, while enterprises themselves often manage one petabyte of data or more.1
Advances in artificial intelligence have also changed enterprise data needs. AI workflows require fast data access, including access to large volumes of unstructured data.
Historically, data retrieval processes focused on queries from structured sources such as relational database management systems. However, rather than use time-intensive, manual approaches to comb through today’s massive internal and external data sources, organizations turn to modern data retrieval. This approach uses technologies such as vector databases and retrieval augmented generation to satisfy demand for data that resides outside internal, relational databases.
Agentic RAG, in particular, has proven especially powerful in meeting this demand. David Levy, an Advisory Technology Engineer for Client Engineering at IBM, explained agentic RAG’s capabilities in a presentation for IBM Technology.
“Agentic RAG is an evolution in how we enhance the RAG pipeline by moving beyond simple response generation to more intelligent decision-making. By allowing an agent to choose the best data sources and potentially even incorporate external information, like real-time data or third-party services, we can create a pipeline that’s more responsive, more accurate and more adaptable,” Levy said.
The result? Enterprises and other organizations can take greater advantage of their own structured and unstructured enterprise data as well as the growing volumes of data produced outside their ecosystems. They’re empowered to access the precise data they need when they need it, enabling analytics and data-driven insights that drive better business outcomes.
The terms data retrieval and information retrieval (IR) are often used interchangeably—and for good reason.
While they have traditionally been associated with different types of data (structured for data retrieval; unstructured for IR), developments in data science have muddied the distinction. Not only can data retrieval now cover unstructured data, but some IR systems allow for “structured document retrieval” (through the use of XML to index text documents).
Arguably, the more salient difference between the two manifests in the types of results each produces. Data retrieval focuses on returning exact matches to user queries while IR systems, which form the backbone of web search engines, provide multiple results (such as web pages) ranked by their relevant information.
Both data retrieval and information retrieval are also at times conflated with data mining. Here, however, the differentiation is clear cut: While data retrieval and IR focus on accessing and delivering data, data mining entails uncovering patterns and insights from data. In other words, it encompasses analysis, not just retrieval. In addition, data mining is applied to large datasets, while data retrieval and IR can be used for data collections of any size.
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Data retrieval methods can be divided into two categories: traditional techniques and AI techniques.2 Traditional techniques include:
Data is retrieved from classic database management systems (DBMS) through query languages. The most prominent query language is structured query language, or SQL, which is used for relational databases. Users deploy SQL commands to retrieve data and accomplish other tasks, including additions, updates and deletions.
Indexing is the creation of searchable data structures that point to data records in larger tables. Search operations can scan indexes instead of entire tables, resulting in faster and more efficient query processing.
In database management systems, query optimization tools improve query performance by choosing the most efficient choice among different query plans, or different ways to perform queries. Optimizers decide, for instance, whether indexes should be used, which way to read a table and, when a join is requested, the order in which tables are joined.
These well-established techniques have proven effective for retrieving structured data and supporting basic search operations, but they have also been known to fall short in multiple areas, including retrieving unstructured data, executing complex queries, capturing semantic meaning, supporting scalability and delivering real-time results.3
AI-driven techniques for data retrieval help compensate for the shortcomings of traditional data retrieval techniques, improving query performance and user experiences.4
In vector databases, various types of data, including text and images, are stored as numerical representations known as vector embeddings. Vector embeddings that bear similar dimensions are grouped together. During a vector search, systems retrieve relevant data and documents with vector embeddings that are similar to the search terms. Such searches typically rely on nearest-neighbor algorithms that infer connections between data points based on their proximity.
Machine learning algorithms trained on historical data and user behavior can provide query recommendations to users based on common query patterns—and then surface relevant data. Additionally, a subset of machine learning known as deep learning can help retrieve unstructured data. For instance, convolutional neural networks (CNNs) power computer vision, which can be used to search image and video files.5
Natural language processing, or NLP, enables user-friendly search queries by allowing users to word queries conversationally, rather than structuring them as query language commands. Then, instead of relying solely on keyword matching, NLP-powered search engines can engage in semantic search: They identify relevant results that reflect the intent of the query even if the exact search terms aren’t present in a document.
Retrieval augmented generation connects large language models to external knowledge bases using application programming interfaces, or APIs. This enables systems to retrieve information that is both domain-specific and timely.
Agentic RAG systems add advanced capabilities to traditional RAG, with agentic reasoning that dynamically optimizes queries and elevates data retrieval performance. Components of leading agentic RAG systems include:
Data retrieval techniques and solutions can improve data access and data management across myriad industries and disciplines.
A services provider to healthcare facilities used natural language processing and retrieval augmented generation to accelerate the retrieval of business-critical data by 90%.
A fintech company deployed a RAG-powered customer service chatbot that retrieved real-time information, , reducing average interaction time by 80% compared to traditional call centers.
E-commerce companies are enabling shoppers to upload photos of what they intend to purchase, and computer vision-powered search solutions retrieve information on products similar to those pictured.
As enterprises explore data retrieval solutions, it’s important to take potential challenges into account.
As enterprises become more successful at retrieving data, they might find some of it riddled with gaps and errors. Data quality management practices, such as data profiling and data cleansing, can help organizations optimize datasets for accuracy, completeness, consistency and other dimensions of quality.
Implementing enhanced data retrieval capabilities can be risky without the right security measures in place to ensure sensitive data can’t be retrieved by the wrong people. Governed data platforms can include built-in security, identity and access controls to prevent unauthorized access and support regulatory compliance.
Proprietary data solutions often bundle data retrieval, orchestration and AI models into closed systems, limiting organizations to vendor-controlled technology stacks. Open source data solutions featuring agentic RAG and other technologies provide an alternative, allowing enterprises more control over their technology stacks and data management functions.
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1 “AI & Information Management Report.” AvePoint. 2024.
2, 3, 4, 5 “AI for Intelligent Data Retrieval.” Advances in Smart Computing and Applications. 15 August 2025.