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Using Gen AI to Automate Text Analysis for Actionable BI Insights
LLM-powered automation unified data sources, reduced manual work, and accelerated business insights.
80% faster
text data processing with automated LLM pipelines.
Consolidated data
that enables enabling advanced BI insights & visualizations.
Technologies
Google BigQuery
Google Dataform
Data Warehouse (DWH)
Vertex AI
Google Gemini
SQL
JavaScript
Git/GitHub
LookerStudio
Expertise
Business intelligence
Gen AI
LLM
Data analysis
Vertex AI
Challenges
Automating Complex Text Analysis for Business Intelligence
Analyzing corporate data often requires examining various texts, such as customer reviews, open-ended survey responses, and third-party product descriptions.
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Traditional methods complexity
Traditionally, this involves complex methods to prepare the text for analysis: translating, removing unnecessary characters and stop words, breaking it into words or phrases, and standardizing the text.
Specialized algorithms need
Automating this process is challenging. Each specific task often requires a specialized algorithm, and complex analyses might still need manual processing.
Underutilized data
Due to these complexities, text data is underutilized in business intelligence (BI), despite its potential to provide valuable insights for decision-making.
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Workflow
Proprietary Algorithm Development
With the introduction of large language models (LLMs) in natural language processing (NLP), Zoolatech has developed a streamlined approach to simplify and automate text analysis for BI.
Phase 1
Data consolidation
Consolidation of all data, the analysis of which is necessary, into a DWH.
Phase 2
Data pre-processing automation
Introduction of automated data pre-processing using Google Dataform.
Phase 3
LLM functions implementation
Implementation of data processing functions and patterns using LLM.
Phase 4
Streamlined approach
By leveraging LLMs and advanced data management platforms, Zoolatech has significantly simplified the process of text analysis, making it more accessible and effective for business intelligence purposes.
We turned complex, unstructured text into a strategic asset—modernizing data workflows with GenAI to unlock deeper insights, operational efficiency, and long-term business value.
Solution
Utilizing Google Cloud and Gemini for Automated BI Processing
The Vertex AI platform, fully integrated with BigQuery, enables the use of the Google Gemini model in the BI cycle.
Data warehouse consolidation
All input data and processing results are stored in Google BigQuery Data Warehouse (DWH). BigQuery offers built-in features for machine learning and integrates seamlessly with Vertex AI.
Automated data pipelines
The data processing, both batch and manual, is automated using Google Dataform. It allows analysts to write code in SQL and JavaScript, store scripts in Git/GitHub, and run them manually or automatically. It also supports running unit tests, significantly simplifying the analysts’ workflow.
LLM-powered data processing
This integration allows for advanced data processing functions and patterns, enhancing the efficiency and quality of text analysis. The use of LLMs and advanced data management platforms has significantly simplified the process of text analysis, making it more accessible and effective for business intelligence purposes.
Results
Faster Processing and Access to Richer Business Insights
As a result, the company gained reliable access to more sources of valuable business information.
The new big data platform improved operational visibility, strengthened analytics capabilities, and enabled ML-driven optimization across the retailer’s value chain.
Time reduction
The applied solution made it possible to reduce the time for certain types of text data by up to 80%.
Automated analysis
In particular, examples have been developed that include the following types of diagrams and analysis: frequency diagrams based on categorization; annotating text; summarization and anonymization of responses; tag clouds and grouped responses based on sentiment analysis.
Data visualization
The results allowed text data to be analyzed and displayed alongside traditional numerical data in LookerStudio.
Business Value
Enhanced Decision-Making and Strategic Expertise Development
The introduction of an automated data processing process, including text processing using LLM, made it possible to significantly reduce the amount of manual work.
Workflow simplification
The automated process enabled significant reductions in manual work, consolidation of heterogeneous data sources, and the application of the same tools to them.
New insights access
The company gained reliable access to more sources of valuable business information.
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