Querying in Natural Language opens up corporate Databases to non-technical business users
Business efficiency and overall business productivity depends on the speed and quality of the decisions that the business users and executives make. Business executives and business users make operational decisions using data they access from corporate databases.
However, one significant hurdle persists: databases that store the data require employing cryptic query languages and elaborate BI applications to access the data. Business users lack the skills to use the query languages or the training needed to engage the BI applications.
Typical corporate databases support different query languages: SQL, SPARQL, Graph QL, OData APIs, etc. But, the steep learning curve discourages especially the business users from using these query languages, resulting in inefficient and often incomplete exploration and analysis of data leading to poor decision processes.
An elegant solution powered by AI
Current state of the art AI (Generative AI, Natural Language Processing and Knowledge Graph Technology) enables solutions that support querying using Natural Language, called Natural Language Querying (NLQ).
NLQ solutions take in user questions in natural language and translate them automatically into the syntax of the particular query language of the target database: SQL, SPARQL, GraphQL, APIs (OData services in case of SAP etc.).
NLQ removes the biggest hurdle that business users face in exploring and analyzing data, resulting in a robust decision environment in the enterprise.
Using NLQ non-technical users can ask questions in plain language. For example, instead of writing the query in complex SQL, the user can type, “Show me last month’s sales in New York for women’s shoes.” NLQ translates the plain English into correct SQL which the database processes and produces the appropriate results.
TextDistil from Lead Semantics comes with a built-in NLQ module for databases that support SQL, SPARQL, GraphQL, JSON-APIs, allowing users to interact with these databases in plain text.
Overall, natural language queries make data in databases more easily accessible to even the non-technical users that don’t know SQL, SPARQL, GraphQL, OData APIs etc. This means business users in marketing, sales, or customer support can explore databases unleashing a new wave of productivity improvements in the organization. Finally, being able to search in natural language makes complex databases more like search engines leading to a better user experience.
Contact us for more information regarding our novel Natural Language Query solution for your SQL, SPARQL, GraphQL, OData APIs and other databases.
