Augmented Intelligence: Six best data analytics practices for moving beyond BI

Traditional business intelligence tools can’t keep up with the fast-changing context in which business decisions are being made today. Insight seekers need more than just predefined dashboards, and insight software needs a deep dose of AI and autonomous learning capabilities to keep up with their dynamic business priorities—versus just delivering data analysis reports. Automating insights discovery and providing rapid access to a higher order of analytics is changing the self-service BI paradigm from just searching for data to making contextual decisions.

Check out this TDWI Checklist Report to learn how:

  • Augmented analytics is ushering in a new era in BI with autonomous insights and guided user journeys
  • Organizations can build a comprehensive business case for augmented analytics
  • Augmented intelligence products support intuitive user experiences and the collaborative sharing of insights
  • Explainability in AI-driven insights is crucial for driving trust and transparency in augmented BI solutions

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Bias in AI: A primer

While Artificial Intelligence (AI) systems can be highly accurate, they are imperfect. As such, they may make incorrect decisions or predictions. Several challenges need to be solved for the development and adoption of technology. One major challenge is the bias in AI systems. Bias in AI refers to the systematic differences between a model's predicted and true output. These deviations can lead to incorrect or unfair outcomes, which can seriously affect critical fields like healthcare, finance, and criminal justice.

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Boosting Decision Intelligence with Lumin on the AWS cloud

Data management and insight generation are the lifelines for any modern-day business. Today, many organizations struggle to manage their data efficiently. Unfortunately, the turnaround time from data to insights to decision to execution takes several days or weeks. This cycle delays efficient business insight generation, thereby incurring significant resource misuse.

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Broker performance analysis solution: Analyzing broker performance as an insurance carrier

Most, if not all, large insurance and re-insurance carriers today work with brokerage agencies to grow their books and ensure a healthy stream of business. Depending on the carrier size, they might often work with dozens of agencies spread across the globe, each with its own operating processes and ways of working. For broker managers, monitoring agency performance and working with them to target the right lines of business, suitable policies and the right customers can be a nightmare. The need to be able to quickly analyze broker performance and take corrective actions to meet submissions. As such, underwriting targets is critical. Lumin's Decision Intelligence capabilities make this task considerably simpler. Let’s dive in.

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