Data Strategy Archives - Tiger Analytics

Blog Tags: Data Strategy

Solving Merchant Identity Extraction in Finance: Snowpark’s Data Engineering Solution

Learn how a fintech leader solved merchant identification challenges using Snowpark and local testing. This case study showcases Tiger Analytics’ approach to complex data transformations, automated testing, and efficient development in financial data processing. Discover how these solutions enhanced fraud detection and revenue potential.

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.

Beyond the Boardroom: A Data Leader’s Comprehensive Guide to Planning, Building, and Launching Generative AI Projects

Learn what questions business leaders need to be asking their teams and what frameworks and guidelines they can use to successfully harness Generative AI for their business.

Decoding the Dilemma: AI-Driven Analytics Products – To Build or To Buy?

Understand the pros and cons of creating vs. purchasing AI-based analytics solutions based on factors like cost, customization, time-to-market, etc. Know when to develop in-house capabilities and when to purchase ready-made solutions with real-world and strategic considerations.

Decoding the 80/20 Rule: Data Science and the Pareto Principle

See how the Pareto Principle, or the 80-20 rule, can transform your data science practices and leverage this powerful tool for project prioritization, problem scoping, data planning, analysis, modeling, and business communication. Get information on how focusing on a few can optimize efforts and drive impactful results in analytics projects.