GenAI on Business Intelligence and Analytics
- [Gondolas on Canals in Venice, Italy - Walks of Italy]
- Overview
Generative AI, a new set of AI and machine learning (ML) algorithms that allow new content to be created from existing data, is taking the consumer and business world by storm. Its adoption is expected to rise rapidly, with the generative AI market expected to reach $110.8 billion by 2030.
For individuals, use cases for the technology span creative, entertainment and practical, while for businesses, generative AI, deployed correctly and responsibly, has the potential to increase efficiency, reduce costs and increase profitability.
The Impact of GenAI on Business Intelligence (BI):
- Faster insights generation: GenAI can significantly accelerate the process of data exploration and analysis, allowing for quicker decision-making.
- Enhanced data storytelling: Generating more engaging visualizations and narratives to effectively communicate data insights to diverse audiences.
- Uncovering hidden patterns: The ability to analyze large volumes of unstructured data with GenAI can reveal previously undetected trends and patterns.
- GenAI Data Scientists in Business Intelligence (BI)
A GenAI Data Scientist in business intelligence (BI) is a data professional who leverages generative AI (GenAI) technologies to extract deeper insights from data, automating repetitive tasks and enabling faster, more comprehensive analysis, allowing them to focus on the "why" and "how" behind data patterns rather than just the "what", while ensuring ethical and responsible AI practices are followed throughout the process; essentially acting as a bridge between raw data and actionable business insights with the aid of advanced AI capabilities.
GenAI Data Scientists require following knowledge of GenAI and data science, including:
- GenAI: LLM, Code Generation, Automation, Workflows, Agents, RAG, Cloud
- Data Science: Python, Machine Learning, Forecasting, Math, Statistics, Data Analysis, Prediction
The Key aspects of this emerging role of GenAI data scientists include:
- Data preparation and cleansing for GenAI models: Ensuring the quality of data used to train generative AI models by cleaning, structuring, and annotating it appropriately.
- Prompt engineering: Crafting effective prompts to guide GenAI models to generate relevant and insightful outputs, tailoring them to specific business questions.
- Interpreting GenAI outputs: Critically evaluating the results generated by AI models, understanding their limitations, and ensuring the insights are accurate and aligned with business context.
- Developing hybrid approaches: Combining traditional data analysis techniques with generative AI to generate richer narratives and uncover complex relationships within data.
- Ethical considerations: Implementing safeguards to address potential biases and privacy concerns arising from the use of generative AI models.
- Collaboration with business stakeholders: Translating complex data insights into actionable business recommendations, facilitating communication between technical teams and business leaders.
[More to come ...]
