Tiger Analytics

So you’ve decided AI governance can’t wait. What next? Start with visibility. List every app and area using AI and ask yourself: how is our AIOps being governed today? Then, talk to people about risk. Map stakeholder concerns back to your governance principles, and define controls that actually address them. There’s no universal playbook here. The principles exist, but implementation depends on your organization’s maturity, risk tolerance, and where AI is actually being used. When enterprise data governance principles are combined with thoughtful risk identification and mitigation, organizations create a foundation where AI can scale responsibly without slowing innovation. That’s where the AI governance journey truly begins. Read the full guide here: https://lnkd.in/gzdJ-ePq #DataGovernance #AIReadiness #ResponsibleAI #DataQuality #DataTrust #TigerAnalytics

Today we celebrate not just a day, but the foundation of everything we believe in. Here's to democracy, freedom, and the power of standing together as one. 🇮🇳✨ #RepublicDay #FreedomAndUnity #TigerAnalytics

How do we ensure our New Year resolutions last? We stop treating them as one-time decisions and start treating them like systems: tracked, measured, and adjusted as life happens. Healthcare is learning the same lesson at scale. Outcomes improve when decisions are guided by continuous, connected data. In the latest edition of AI of the Tiger, we look at what this shift really looks like in practice: 💊 GenAI accelerating research by unlocking years of scientific knowledge  💊 Predictive models supporting more accurate, timely diagnoses 💊 Metadata-driven pipelines ensuring critical nutrition products reach families reliably 💊 And why responsible, governed AI is non-negotiable when these systems start shaping care Dive in for a closer look at the systems driving better patient outcomes. #AIofTheTiger #Healthcare #Pharma #GenAI #ClinicalResearch #MLPipelines #DiagnosticData #ConnectedData #ResponsibleAI #EthicalAI

#TigerSolves season 4, episode 2: Tweaking campaigns on the fly with GenAI-powered media intelligence Meet Ben, a media planning manager at a global CPG company, whose world is full of consumer touchpoints - TV. Digital. Social. Retail media. He juggles manual reports, scattered dashboards, and data in every format imaginable to identify the secret signal that gets someone to hit “buy”, but insights arrive before the moment to act has passed. We built a GenAI layer on top of media data so Ben can ask questions in plain language and get answers instantly, from quick SQL lookups to deeper reasoning and light modeling, through a single, intuitive interface. Now, Ben can tweak plans, budgets, and creatives in the moment. The impact: 🎯 10% increase in campaign efficiency 🎯 Significant cost savings, driven by faster, better decisions #MediaPlanning #GenAI #CampaignEfficiency #CPG #MediaIntelligence #TigerAnalytics

React or anticipate and act autonomously? RGM has changed a lot, but the path from data to insights to action remains manual and excruciatingly slow. Shravan Pai’s session at NRF 2026 focused on breaking that cycle. Leveraging the Databricks Data Intelligence Platform, we explored how Agentic RGM fundamentally changes the physics of decision-making. The demo highlighted a shift from static modeling to active orchestration, where: ➡️Persona-aware personalization drives revenue lift. ➡️Autonomous agents run hundreds of scenarios in near real-time. ➡️Intelligent tool-calling bridges the gap between high-level revenue strategy and ground-level execution. Thanks to our partners and RGM leaders who stopped by to challenge the status quo and explore what a truly agentic future looks like. Rajat Sinha | Kishor Gummaraju | Sethuraman Janardhanan, PhD | Badrish Prakash | Ujwal Joseph | Parag Shah | Bryan Smith | Jennifer Zavacky | Rob Saker | Matt Jackson | Sidhtara Tep | Nicki Walker | Databricks #NRF2026 #Databricks #DataStrategy #AIOrchestration #EnterpriseAI #RGM #AIAgents

How do you bring ethics, accountability, and trust into data governance - without turning it into just another checklist? In our previous posts, we looked at what serves as the lighthouse for enterprise AI leaders navigating governance decisions and explored the key questions CXOs, LoB leaders, and data owners are asking as they modernize governance for AI-driven experiences. We now take a close look at our practical approach to stronger data governance, one that keeps the human at the center, while balancing the realities of scale, speed, and risk. Our HEal & INtERAcT framework brings together seven essential elements -- ethical, integrity-driven, non-biased, explainable, responsible, accountable, and trustworthy -- as interconnected building blocks that reinforce one another. This framework offers a structured way for data leaders to move from intent to execution. Rather than introducing entirely new frameworks, our approach focuses on accessible AI governance in which existing data governance operations are expanded to include new dimensions, roles, processes, and standards. Read the full guide: https://lnkd.in/gzdJ-ePq #DataGovernance #AIReadiness #ResponsibleAI #DataQuality #DataTrust

You spot a cultural trend. One team analyzes the data. Another figures out how to respond. Each step matters—but together, they cost time. By the time action is taken, the moment has already passed. So how do you collapse the distance between trend detection and commercial activation? At NRF 2026, Ujwal Joseph took the stage at the Google Cloud booth to showcase how we’re closing that gap with TrendAct. The demo showed how agentic intelligence can detect a cultural spike, validate it for commercial viability, and immediately trigger the right campaign. Powered by Google Gemini, TrendAct helps CPG and Retail teams move faster across: ✳️Product design & assortment shaping ✳️In-season forecasting and inventory decisions ✳️Personalized recommendations and search ✳️Creative and campaign activation Thanks to our partners at Google Cloud, and the retail and CPG leaders who joined the session. Srinivas Ayyagari | Jennifer Bejarano | Kishor Gummaraju | Sethuraman Janardhanan, PhD | Badrish Prakash | Shravan Pai | Parag Shah | Damon Moss | Lisa Bassett | Blair Reuling | Carly Wild #NRF2026 #RetailAnalytics #GenerativeAI #GoogleCloud #CPGInnovation #AgenticAI

Infrastructure upgrades often have a hidden cost. They can render legacy safety nets obsolete. That was the situation for a major US credit card issuer. Their data modernization efforts were crucial for growth, but they inadvertently broke the effectiveness of their existing fraud models right before a new product launch. With strict Model Risk Management rules in play, we focused on interpretable speed. By blending third-party signals with internal history, we rebuilt the safety net to be stronger and more transparent than before. This approach secured the launch and delivered annual savings of approximately $5M. Read the case study: https://lnkd.in/gkzm5SdC #RiskManagement #Banking #DataModernization #FraudDetection #AI

A data breach now averages nearly $5 million, a reminder of why privacy, transparency, and control are becoming strategic priorities. As AI scales across systems, it’s an opportunity to rethink how data is governed, with clearer ownership, consent built into workflows, and controls that travel with the data. The first step in building these governance frameworks - Ask the right questions. In our blog, we explore the key questions CXOs, LoB leaders, and data owners are asking as they modernize governance for AI-driven experiences. Read now https://lnkd.in/gzdJ-ePq #DataGovernance #AIReadiness #ResponsibleAI #DataQuality #DataTrust

#TigerSolves: Season 4, Episode 1: No matter wind, nor rain, nor shine, nor snow, crop intelligence ensures the perfect potato is ready for picking at your local store. Meet Cam, the procurement planner at a large CPG company. For a smooth journey from seed to shelf, Cam needed week-by-week visibility into how crops were progressing in the field and early signals that could surface risks while there was still time to act. At Tiger Analytics, we built a satellite-powered crop intelligence system that combined imagery, weather signals, and ML models so C could track growth stage by stage, without the need for constant field visits. What this delivered: - 90% accuracy in growth-stage predictions - 30% fewer manual field visits through early alerts - Real-time visibility that helped teams intervene sooner This is what it looks like when analytics makes meaningful everyday impact. #EnterpriseAnalytics #PredictiveAnalytics #TigerSolves #TigerAnalytics