LatentView Analytics
LatentView Analytics has been helping enterprises make smarter, data-driven decisions for 20+ years. Founded in 2006 by Venkat Viswanathan and Pramad Jandhyala, the company has grown into one of the world’s leading digital analytics firms — and became the first analytics company in India to go public in 2021.
LatentView brings deep expertise in end-to-end data engineering, business analytics, GenAI, and predictive modeling to 50+ Fortune 500 clients across tech, retail, financial services, and CPG. The company’s work spans everything from AI-powered sales forecasting and customer lifetime value modeling to large-scale data platform builds and GenAI implementation.
A publicly traded company serving the US, India, Canada, Europe, and Singapore, LatentView has been recognized in Forrester’s Customer Analytics Service Providers Landscape and among India’s Best Workplaces for Millennials. In 2024, the company expanded its capabilities through the acquisition of Decision Point Analytics, adding Revenue Growth Management and GenAI product solutions to its portfolio.
LatentView’s insights are shaped by real client engagements across industries — from building ML frameworks for telecom network reliability to developing guest lifetime value models for leading cruise lines. The perspectives shared on this blog draw from that hands-on experience working at the intersection of data, AI, and business strategy.
Learn more at latentview.com.
Blogs
Agentic AI vs AI Assistants: Key Differences and Comparison (2026)
Agentic AI and AI assistants differ in autonomy, proactivity, and execution scope. An AI assistant functions as a reactive helper
Agentic AI In Supply Chain: Uses Cases, Technologies & Future Trends
Agentic AI in supply chain replaces static automation with autonomous agents that sense disruptions, plan responses, and act in real
Azure Data Factory (ADF) vs Databricks: A Detailed Comparison
Azure Data Factory (ADF) and Azure Databricks serve different but complementary roles: ADF orchestrates and manages data workflows, while Databricks
What Is Data Ingestion? Types, Architecture & Best Practices
Data ingestion helps Enterprises collect and consolidate data from diverse sources into a centralized system, enabling efficient processing, transformation, and
Generative AI for Business Intelligence: Use Cases Turning Data into Decisions in 2026
Generative AI transforms business intelligence by replacing static dashboards with conversational interfaces, enabling users to query data in natural language.
Predictive Analytics in Banking: Examples, Use cases & Future Trends
Predictive analytics in banking uses data, statistical models, and AI to forecast outcomes, helping banks shift from reactive reporting to
Enterprise Data Architecture: Components, Frameworks & Implementation
Enterprise Data Architecture (EDA) is a master blueprint that standardizes how an organization collects, stores, governs, integrates, and uses data.
Databricks Migration: A Strategic Guide for Enterprise Data Teams
A Databricks migration moves data warehouses or legacy workspaces to the Databricks Lakehouse Platform using tools like Lakebridge and Lakeflow
Transfer Learning in NLP
Pre-trained models in NLP is definitely a growing research area with regular improvements in existing models.
Reach vs Engagement in Social Media: Differences, Metrics & Strategies
Reach and engagement are core social media metrics that measure content performance. Reach counts unique users who see content for