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April 24, 2025

How to Get Executive Buy-In for a Semantic Layer: Turning Technical Wins Into Business Value

Why Executives Need to See the Business Value of a Semantic Layer After months of rigorous testing, Alex, the data engineer at PrecisionWorks, has validated a semantic layer that transforms analytics across the enterprise. (New to this story? Catch up…

Posted by: Dave Mariani

April 22, 2025

How Vodafone Portugal Transformed Telecom Analytics with AtScale’s Semantic Layer + Celfocus

In telecommunications, data is abundant, but turning it into useful insights can be extremely difficult. Communication Service Providers (CSPs) operate in a fast-paced, highly competitive market, where success depends on efficiently managing massive volumes of data quickly and reliably. But…

Posted by: Cort Johnson

April 17, 2025

Evaluating a Semantic Layer: How Technical Teams Drive ROI Through Smart Data Infrastructure

Picking Up From Part 1: Why Technical Evaluation Matters After realizing a semantic layer could solve PrecisionWorks’ biggest data headaches, Alex, a data engineer, moves on to a critical step: evaluating vendors. (Missed the beginning? Catch up on Part 1:…

Posted by: Dave Mariani

April 15, 2025

Connect Excel to Snowflake Without ODBC: Smarter Alternatives for Business Users

Excel remains a go-to tool for business users who want quick, flexible access to data. But when it comes to connecting Excel to Snowflake, the traditional route — via ODBC — can be a technical nightmare. Driver installs, authentication headaches,…

Posted by: Macaulay Parker

April 10, 2025

Top 5 Considerations When Choosing a Semantic Layer for Modern Data Stacks

Understanding the Challenge: A Day in the Life of a Data Engineer Meet Alex, a data engineer at PrecisionWorks, a global manufacturer managing a large-scale enterprise data ecosystem. Every day, Alex battles: Data silos across CRM, ERP, and third-party systems…

Posted by: Dave Mariani

April 9, 2025

Modernizing OLAP for the Cloud: Why Legacy BI Needs a Semantic Upgrade

Organizations that have relied on OLAP cubes for years are hitting a wall. The traditional OLAP approach—great for fast, multidimensional analysis—was never designed for the modern cloud data ecosystem. And with the rise of cloud data platforms like Snowflake, Databricks,…

Posted by: Dave Mariani

April 7, 2025

Snowflake Cost Management & Control: Best Practices for Enterprise BI Workloads

As organizations scale their Snowflake deployments to support enterprise-level BI workloads, cost control quickly becomes a top priority. While Snowflake’s pay-per-use model is powerful, it can lead to unexpected expenses when query inefficiencies and overprovisioned compute go unchecked. Read on…

Posted by: Macaulay Parker

April 3, 2025

How a Typical Power BI Customer Can Save Millions in Annual Costs

The Rising Cost Challenge for Power BI Customers Organizations across industries rely on Power BI to drive business intelligence and inform strategic decisions. However, as data volumes increase, so do the associated costs. Businesses often face growing expenses related to…

Posted by: Stephen Berg

March 31, 2025

Building Trust in NLQ: How to Improve Cortex Analyst Results in Snowflake

Natural language querying (NLQ) has the potential to make data accessible to everyone — not just analysts and data engineers. But in practice, tools like Snowflake Cortex Analyst often deliver inconsistent or inaccurate results. That inconsistency erodes trust, slows adoption,…

Posted by: Macaulay Parker

March 27, 2025

Breaking Data Silos with Cross-Cloud Federation and Semantic Layers: Key Takeaways from the TDWI Expert Panel

Recently, I had the opportunity to join the TDWI expert panel onBreaking Data Silos with Cross-Cloud Federation and Semantic Layers, alongside industry leaders John Korsak, Enterprise Solutions Engineer from Cube, and Fern Halper, TDWI VP Research, Senior Research Director for…

Posted by: Dave Mariani
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