Empowering Self-Serve Analytics: LLMs and Semantic Layers

STEP 1

UNDERSTANDING SELF-SERVE ANALYTICS

◉ Self-serve analytics refers to the ability of non-technical users to access and analyze data independently.

◉ It empowers individuals to explore data, generate reports, create visualizations, and make data-driven decisions.

◉ Self-serve capabilities have evolved over time, enabling users to have more control over data analysis processes.

STEP 2

THE ROLE OF LLMS IN SELF-SERVE ANALYTICS

◉ LLMs are advanced AI models trained on vast amounts of text data, enabling them to understand and generate human-like text.

◉ LLMs allow users to interact with data through natural language queries, facilitating a conversational approach to data analysis.

◉ LLMs have limitations, including the lack of business context, which can lead to incorrect or misleading results.

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STEP 3

THE IMPORTANCE OF THE SEMANTIC LAYER

◉ The semantic layer acts as an intermediary between raw data and end users in self-serve analytics.

◉ It defines metrics, dimensions, joins, and other critical elements to ensure the correctness of data analysis.

◉ The semantic layer establishes consistent data definitions, serving as a single source of truth for accurate analytics.

STEP 4

THE SYNERGY OF LLMS AND THE SEMANTIC LAYER

◉ LLMs and the semantic layer complement each other in self-serve analytics.

◉ LLMs excel at comprehension and understanding user intent, while the semantic layer provides the necessary business context. Combining

◉ LLMs and the semantic layer creates a user-friendly and context aware analytics environment.

STEP 5

CHALLENGES IN DATA ANALYSIS

◉ Challenges include translating data, differing definitions of metrics, and handling nuances specific to each organization.

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STEP 6

ENSURING TRUST IN DATA ANALYSIS

◉ Trustworthy data and reliable metrics are crucial in data analysis.

◉ The semantic layer enforces consistency and ensures that calculations and analyses are accurate and reliable.

◉ Incorrect data analysis can have significant consequences, including legal implications.

STEP 7

LIMITATIONS OF TEXT-TO-SQL FOR ANALYTICS

◉ Text-to-SQL alone is insufficient for accurate data analysis.

◉ Understanding business-specific definitions and rules is a challenge that text to-SQL struggles to address.

STEP 8

LEVERAGING LLMS AND THE SEMANTIC LAYER

◉ Proposing the combination of LLMs and the semantic layer to overcome limitations.

◉ LLMs provide comprehension and understanding, while the semantic layer ensures correctness and accuracy.

◉ Zenlytics is a platform that leverages this combination for self-serve analytics.

STEP 9

THE FUTURE OF SELF-SERVE ANALYTICS

◉ Reflecting on the growing hype around LLMs and their potential impact in self serve analytics.

◉ The role of data scientists in self-serve analytics is evolving, allowing them to focus on more complex statistical tasks and innovation.

◉ Encouragement to embrace self-serve analytics and foster a data-driven culture for future success.

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