Manufacturing Archives - Tiger Analytics

Blog Industry: Manufacturing

11 Ways Data Engineers Can Leverage an Agile Mindset to Help Drive Value Efficiently and Effectively

This comprehensive guide explores how Agile methodologies can be applied to data engineering within the Tiger Gene framework. It outlines key principles such as welcoming change, working in small increments, and continuous improvement. The article provides practical scenarios, pros and cons, and solutions for implementing Agile practices in data engineering projects. Data engineers can learn how to drive efficient and effective value through enhanced collaboration, flexibility, and iterative development, ultimately improving their project outcomes and team dynamics.

Implementing Context Graphs: A 5-Point Framework for Transformative Business Insights

This comprehensive guide outlines three phases: establishing a Knowledge Graph, developing a Connected Context Graph, and integrating AI for auto-answers. Learn how this framework enables businesses to connect data points, discover patterns, and optimize processes. The article also presents a detailed roadmap for graph implementation and discusses the integration of Large Language Models with Knowledge Graphs.

Connected Context: Introducing Product Knowledge Graphs for Smarter Business Decisions

Explore how Product Knowledge Graphs, powered by Neo4j, are reshaping data analytics and decision-making in complex business environments. This article introduces the concept of Connected Context and illustrates how businesses can harness graph technology to gain deeper insights, improve predictive analytics, and drive smarter strategies across various functions.

A Comprehensive Guide to Pricing and Licensing on Microsoft Fabric

This comprehensive guide explores Microsoft Fabric’s pricing strategies, including capacity tiers, SKUs, and tenant hierarchy, helping organizations optimize their data management costs. It breaks down the differences between reserved and pay-as-you-go models, explaining Capacity Units (CUs) and providing detailed pricing information. By understanding these pricing intricacies, businesses can make informed decisions to fully leverage their data across various functions, leading to more efficient operations and better customer experiences.

Advanced Data Strategies in Power BI: A Guide to Integrating Custom Partitions with Incremental Refresh

Explore advanced data management strategies in Power BI through a detailed examination of integrating Custom Partitions with Incremental Refresh to efficiently handle large datasets. Key benefits such as improved query performance, more efficient data refresh, and better data organization are outlined, along with a practical guide on implementing these strategies in Power BI environments.

Tiger’s Snowpark-Based Framework for Snowflake: Illuminating the Path to Efficient Data Ingestion

In the era of AI and machine learning, efficient data ingestion is crucial for organizations to harness the full potential of their data assets. Tiger’s Snowpark-based framework addresses the limitations of Snowflake’s native data ingestion methods, offering a highly customizable and metadata-driven approach that ensures data quality, observability, and seamless transformation.

Migrating from Legacy Systems to Snowflake: Simplifying Excel Data Migration with Snowpark Python

Discover how Snowpark Python streamlines the process of migrating complex Excel data to Snowflake, eliminating the need for external ETL tools and ensuring data accuracy.

Empowering BI through GenAI: How to address data-to-insights’ biggest bottlenecks

Explore how integrating generative AI (GenAI) and natural language processing (NLP) into business intelligence empowers organizations to unlock insights from data. GenAI addresses key bottlenecks: enabling personalized insights tailored to user roles, streamlining dashboard development, and facilitating seamless data updates. Solutions like Tiger Analytics’ Insights Pro leverage AI to democratize data accessibility, automate pattern discovery, and drive data-driven decision-making across industries.

Why India-Targeted AI Matters: Exploring Opportunities and Challenges

The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. Explore the challenges and opportunities for AI in India.

Data-Driven Disruption? How Analytics is Shifting Gears in the Auto Market

The presence of legacy systems, regulatory compliance issues and sudden growth of the BEV/PHEV market are all challenges the automotive industry must face. Explore how Analytics can help future-proof their growth plans.

Blog Industry: Manufacturing

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