Tiger Analytics

How QSR Enterprises Are Scaling Real-Time POS Data on AWS and Apache Iceberg for Near-Real-Time Decisioning

What does it take to make millions of daily POS transactions usable while the lunch rush is still unfolding? In this blog, we explore how a global QSR brand re-architected its data platform on AWS and Apache Iceberg to improve real-time visibility, scalability, and operational responsiveness across stores, supply chains, and analytics systems. For data and engineering leaders in QSR and retail, the piece offers a closer look at how modern data architectures are reshaping the speed and quality of operational decision-making.

Reimagining Usage-Based Insurance with Scalable Telematics Data Platform

Telematics is transforming usage-based insurance from static pricing to continuous, behavior-led decisioning. The real challenge is no longer data collection, but ensuring that high-frequency, multi-source trip data is trusted, consistent, and usable at the point of entry. This blog explores how insurers can move beyond fragmented pipelines by embedding governance, validation, and streaming-led architecture directly into the data foundation. The result is a scalable telematics platform that turns raw driving signals into reliable, real-time underwriting intelligence.

Building AI-Ready Data Products at Scale: Inside the Tiger Analytics Industry Domain Framework

Scaling AI-ready data products requires embedding context, ownership, and trust into how data is designed and consumed. As enterprises move from isolated use cases to interconnected ecosystems, fragmented metrics, siloed teams, and weak governance can limit both adoption and impact. Our Industry Domain Framework addresses this by combining domain-driven design, interoperable architecture, and persona-linked intelligence to create data products that are not only reusable and scalable, but also interpretable and actionable. The result is a connected blueprint that enables organizations to move from data proliferation to decision intelligence at scale.

How Federated Domain Models are Changing the Conversation on Data-Led Outcomes

As data demand scales, centralized models start to strain, but because of distance from real business context. This blog explores how federated domain models, delivered through data products, bring ownership closer to where decisions are made, improving trust, reuse, and speed. From aligning data with workflows to enabling more reliable AI systems, it lays out what it takes to move from fragmented consumption to outcome-driven data ecosystems.

How Policies-as-Code Enable Trusted, Interoperable Systems: The Data Leader’s Guide to Computational Governance

Traditional governance can’t keep up with fragmented, high-speed enterprise ecosystems. This blog breaks down how Policies-as-Code and computational governance embed trust, compliance, and interoperability directly into systems. Read on to see the tech pillars, real-world use cases, and what it takes to operationalize governance at scale.

How to Replace Reactive Discounting with Trajectory-Based Pricing Governance

Retail discounting often reacts to short-term sales signals, even when the real issue is timing, availability, or operational noise. This blog explores how trajectory-based pricing governance and predictive metrics help retailers interpret demand more accurately and avoid premature markdowns. Read on to see how demand discipline can protect margins and improve seasonal inventory decisions.

The AI Engineer’s Guide to Content Summarization at Scale on AWS Bedrock

Scaling summarization on AWS Bedrock looks simple until you confront real workloads, rate limits, and model behaviour that doesn’t follow the brochure. This article breaks down what actually holds up in production and where engineers need to rethink their defaults.

Why Every Enterprise Needs Robust Model Risk Management in 2025?

As AI moves into mission-critical workflows, enterprises can’t rely on intuition or legacy controls to keep models trustworthy. This blog examines how Model Risk Management has evolved into a cross-industry requirement, shaped by new forms of risk and rising regulatory expectations. It lays out what modern MRM actually entails across traditional, machine-learning, and generative models. For leaders trying to scale AI responsibly, the piece offers a clear view of the disciplines needed to do it well.

AI Agent Companion: Transforming Frontline Productivity and Customer Experience in Retail

Agentic AI for frontline workers is getting a lot of attention, yet most discussions gloss over the operational realities inside a store. This piece looks at what it actually takes to make AI useful on the floor, as a context-aware companion that reduces cognitive load, shortens task cycles, and brings consistency to customer interactions. It lays out how multimodal agents shift day-to-day execution, where the real friction points lie, and why this matters for retailers trying to balance scale, compliance, and service quality in an increasingly complex environment.

Always-On Brand Equity: Building a Resilient Digital Presence in a 24/7 Active World

Brand equity is often treated as a periodic activity, even though most signals now emerge from continuous digital interactions. This blog outlines a more integrated view of how those touchpoints collectively influence perception over time. It highlights the role of a unified Brand Equity score in making that picture clearer and more actionable. The aim is to offer a grounded, data-informed way to understand how brand equity evolves in a modern digital environment.

Transforming Investment Research with the Financial Advisor Assistant

AI is opening a new chapter in investment research, where insight generation becomes continuous, contextual, and tailored to fast-moving markets. By unifying diverse data streams and elevating analytical depth, next-generation assistants are helping firms operate with greater clarity and strategic precision. This blog examines how these capabilities can strengthen advisory workflows and unlock a more adaptive, intelligence-rich research model.

Clinical Study Report Generator: Transforming Regulatory Reporting with Generative AI

Generative AI is reshaping regulatory documentation, bringing new precision and scalability to Clinical Study Report creation. As clinical programs grow more complex, automated summarization and compliant, data-aligned narratives are redefining what efficient reporting looks like. This blog explores how AI-driven CSR generation can modernize workflows and strengthen scientific rigor across the submission lifecycle.

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