Augmented analytics vs. BI

“What happened?”

“Why?”

“What’s next?”

Every strategy session hinges on these three questions. But for most companies, answering them means wrestling with powerful — yet limited by their predefined structure — BI tools.

BI analytics works, but it puts the burden on you 🫵

• Data analysts hold the keys to your data, crafting reports, dashboards, and running complex SQL queries to unlock its story
• Dashboards are built for known questions, not the ones you’ll have
• Decision-making is delayed by manual data preparation and modeling cycles
• Visuals arrive with limited context and no clear next steps

Thanks to AI, augmented analytics takes the lead where BI falls short 🫰

• Business users ask questions in plain language and get instant, clear answers
• Data preparation is largely automated and handled behind the scenes
• Anomalies, correlations, and trends surface without being explicitly asked for
• You don’t just get a chart — you get a conclusion and a next-best action

To truly delivery, however, augmented analytics needs the right setup 

1️⃣ Augmented analytics is only as good as the data it’s fed. Train the underlying LLM or SLM on your data and fine-tune the system through prompt engineering.

2️⃣ Address data governance properly:

→ Set up role-based access control to manage who sees what

→ Implement a robust data security strategy to protect sensitive information

→ Use metadata management and data lineage tracking to ensure transparency and traceability

→ Apply explainable AI (XAI) techniques to make model decisions clear and audit-ready

3️⃣ Treat augmented analytics as an engineering investment, not a one-off business project.

→ Build a scalable, modular architecture that supports multiple use cases

→ Design components for reuse and long-term flexibility

→ Establish a continuous improvement process so the system evolves with your business

4️⃣ Instill a data-literate culture where your teams can challenge AI insights instead of just accepting them blindly.

Ready to take your data to the next level without hiring more analysts? Augmented analytics is your answer. Let’s set it up together

More experts’ insights

Focus On Results Not Process: Augmented Analytics Guide >>

Case Study: ML-Powered Demand Forecasting For a Canadian Ecommerce Company >>

Case Study: Real-time Data Visualization For an HR Department >>

Cut Through the Complexity of ML Solutions and Address Your Biggest Concerns to Promote Smooth Adoption >>

Subscribe to stay sharp on tech trends and insights >>