- Published on March 4, 2024
- In Deep Tech
Democratize data analysis and insights generation through the seamless translation of Natural Language into SQL queries
By leveraging custom scoring algorithms and integrating business context into algorithmic analysis, the framework streamlines SQL query generation from English language queries.
- By Anshika Mathews
In the realm of modern data analytics, the ability to seamlessly translate natural language queries into actionable insights has emerged as a transformative capability. This advancement empowers users to interact with complex datasets effortlessly, extracting valuable insights and facilitating informed decision-making. However, the journey towards achieving this feat is rife with challenges, ranging from the complexity of diverse datasets to semantic sensitivity and algorithmic limitations.
Navigating the Complexity of Data
The datasets under scrutiny encompass over 500 variables across 10 distinct datasets, each varying in level of aggregation and granularity. Within this landscape, datasets with identical names may carry different meanings, while others may yield similar solutions from disparate data sources. This complexity underscores the challenge of prioritizing tables for analysis and crafting SQL queries with pinpoint accuracy. Primarily sourced from banking datasets, the
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Anshika Mathews
Anshika is the Senior Content Strategist at AIM Research. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@analyticsindiamag.com.
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