What is natural language analytics?
Natural language analytics is a type of search-driven analytics that allows users to interact with their data in natural language rather than resorting to complex searches or programming. It is a departure from traditional BI solutions that require users to know SQL or manually create reports.
Instead, natural language analytics enables users to type or speak questions in their own words and get instant visualizations, insights, and explanations. In short, natural language analytics is a system that changes how organizations engage with their data by providing an intuitive, conversational, and contextually aware analytics language.
With natural language analytics, business users, analysts, and decision-makers can instantly discover trends, patterns, and anomalies in their data without waiting for specialized teams to create dashboards and reports.
Natural language analytics can also overlap with search-based analytics and natural language search, as these systems are designed to interpret human language queries and turn them into data queries. When combined with machine learning (ML), the system can improve its understanding of terms, synonyms, and user intent.
How Natural Language Analytics Works
Natural language analytics is based on a set of AI technologies such as natural language processing (NLP), machine learning, and advanced data indexing. The entire process is as follows:
Query Interpretation: The user asks questions in natural language. The system interprets the meaning, context, and entities in the question. For instance, “Show me last quarter’s revenue by region” is interpreted without knowing the names of the database columns.
Query Translation: The system converts the natural language question into structured queries that can be executed on underlying databases. This is where search-driven analytics principles come in, as the platform maps keywords, synonyms, and concepts to the correct data sources.
Data Retrieval and Analysis: The translated query is executed against the relevant datasets. Depending on the system’s capabilities, it may perform aggregations, trend analysis, filtering, comparisons, and even predictive modeling.
Result Visualization and Narrative: Responses are provided in visual forms like graphs, charts, or tables, and may also include text descriptions created using analytics language. More advanced systems are capable of conversational analytics, where further questions can be asked without having to repeat the context.
Continuous Learning: By tracking user interactions and feedback, natural language analytics systems improve their ability to understand ambiguous queries, new terminology, and evolving business metrics.
In practice, natural language search or natural search capabilities can also incorporate voice input, enabling hands-free interaction and real-time data insights.
The Benefits of Natural Language Analytics
Adopting natural language analytics offers the following benefits:
- Users without SQL or programming knowledge can ask questions directly and get instant insights.
- Queries can be run in seconds, which accelerates the time taken by organizations to make decisions.
- In this way, the process of accessing analytics can be made simpler, and more employees will be able to use data to make decisions.
- Natural language analytics can automatically identify trends, outliers, and correlations in data, providing explanations for why certain metrics have changed.
- The systems are capable of handling large amounts of data and can adapt to new sources of data without much setup.
In addition, these platforms often incorporate search-based analytics features such as autocomplete, suggested searches, and keyword recommendations, further enhancing usability and adoption.
