When we talk about GenAI in data analytics, people often mix up two important concepts - AI models and AI applications. But they're actually quite different!
Think of it like a data pipeline: the algorithm is powerful, but you need the whole system to actually analyze your data and generate insights.
Engagement Message
Can you guess which part AI models are like?
An AI model is the "brain" or "analytical engine" - it's the mathematical core that's been trained on data to recognize patterns or generate predictions.
Models like GPT-4 or specialized analytics models are algorithms with billions of learned parameters. They're pure intelligence without a way to process real business data.
Engagement Message
What would a data algorithm be without proper data infrastructure?
An AI application (or system) is the complete package - it takes that model and wraps it with everything needed to make it useful for data analysis.
This includes data connectors, visualization dashboards, query interfaces, and all the code that lets you actually analyze datasets and generate reports.
Engagement Message
Starting to see why modern BI tools' AI features are more than just the underlying models?
Tableau's Ask Data feature is the application - it provides the natural language interface, connects to your databases, handles data security, and presents visualizations.
Inside, it uses language models as its "engine" to understand your questions and generate queries. But without Tableau's application layer, those models would just be code without access to your data!
Engagement Message
What other components might a data analytics AI application need?
Data AI applications typically include ETL pipelines, data governance systems, business logic (rules for data access and processing), cloud infrastructure, and reporting tools.
Think of Salesforce Einstein - the predictive algorithms are the "models," but the platform includes CRM data, dashboards, automated workflows, and much more!
