January 12, 2023

The Definition of Data Mesh: What Is It and Why Do I Need One?

Today’s leading businesses prioritize data mesh. The modern data mesh craze ultimately came about to streamline data democratization – that is, making the right data available to the right people at the right time. Democratization is a huge departure from…

Posted by: Elif Tutuk

January 10, 2023

Data Analytics is The Foundation of an Enterprise AI Strategy

There is a massive global investment in enterprise AI. While the potential for AI-driven value is huge, organizations are struggling with a common set of challenges to realizing this potential: Lack of data scientists, difficulty in moving more models to…

Posted by: Dave Mariani

December 20, 2022

Using Augmented Analytics for Predictive and Prescriptive Analyses

There are six key areas for effectively consuming data for business insights: data, access, model, analyze, consume, and insights. For organizations looking to advance in each of these areas, AtScale’s Data and Analytics Maturity Model Workshop explains how teams can…

Posted by: Dave Mariani

December 15, 2022

How to Maximize the Value of AI and BI at Scale

A few weeks ago, I spoke with two industry experts to get a first-hand view of maximizing AI and BI at scale. The panelists included Deepak Jose, Global Head of On-Demand Data and Analytics Solutions at Mars, and Jigyasa Grover,…

Posted by: Dave Mariani

December 6, 2022

Deploying “Data as Code” Throughout the Entire Enterprise

How analysts consume data is a key factor in determining how quickly, accurately, and efficiently stakeholders can make data-driven decisions. Without the ability to freely and speedily share data insights, it can take a lot of time, effort, and expertise…

Posted by: Dave Mariani

December 1, 2022

Where The Semantic Layer Fits In Your Data Strategy

This blog is part of a series from Vin Vashishta, Founder and Technical Advisor with V Squared. V Squared advises businesses on AI strategy, data product strategy, transformation, and data organizational build-out services. They have helped clients deliver products with…

Posted by: Vin Vashishta

November 29, 2022

How Semantic Layer Helps Scale up DataOps

This blog is written by Vidhi Chugh, Staff Data Scientist at Walmart Global Tech. Vidhi is an award-winning AI/ML innovation leader who works at the intersection of data science, product and research teams to deliver business value and insights. She carries…

Posted by: Vidhi Chugh

November 17, 2022

Making Everyone a Power Data Analyst with a Semantic Layer

The number of data analysts at your organization is a limiting factor that determines how many stakeholders are able to make data-driven decisions. To make better-informed decisions, organizations need to increase the number of people with the tools and expertise…

Posted by: Dave Mariani

November 15, 2022

How to Streamline Data Science Workloads and Feature Engineering in Snowflake

Our partnership with Snowflake has drastically sped up our customers' data processing capabilities by eliminating the need for tedious data engineering tasks. By driving workloads directly to Snowflake, users can create accelerated time-to-prediction rates and a more efficient method to…

Posted by: Daniel Gray

January 12, 2023

The Definition of Data Mesh: What Is It and Why Do I Need One?

Today’s leading businesses prioritize data mesh. The modern data mesh craze ultimately came about to streamline data democratization – that is, making the right data available to the right people at the right time. Democratization is a huge departure from…

Posted by: Elif Tutuk

January 10, 2023

Data Analytics is The Foundation of an Enterprise AI Strategy

There is a massive global investment in enterprise AI. While the potential for AI-driven value is huge, organizations are struggling with a common set of challenges to realizing this potential: Lack of data scientists, difficulty in moving more models to…

Posted by: Dave Mariani

December 20, 2022

Using Augmented Analytics for Predictive and Prescriptive Analyses

There are six key areas for effectively consuming data for business insights: data, access, model, analyze, consume, and insights. For organizations looking to advance in each of these areas, AtScale’s Data and Analytics Maturity Model Workshop explains how teams can…

Posted by: Dave Mariani

December 15, 2022

How to Maximize the Value of AI and BI at Scale

A few weeks ago, I spoke with two industry experts to get a first-hand view of maximizing AI and BI at scale. The panelists included Deepak Jose, Global Head of On-Demand Data and Analytics Solutions at Mars, and Jigyasa Grover,…

Posted by: Dave Mariani

December 6, 2022

Deploying “Data as Code” Throughout the Entire Enterprise

How analysts consume data is a key factor in determining how quickly, accurately, and efficiently stakeholders can make data-driven decisions. Without the ability to freely and speedily share data insights, it can take a lot of time, effort, and expertise…

Posted by: Dave Mariani

December 1, 2022

Where The Semantic Layer Fits In Your Data Strategy

This blog is part of a series from Vin Vashishta, Founder and Technical Advisor with V Squared. V Squared advises businesses on AI strategy, data product strategy, transformation, and data organizational build-out services. They have helped clients deliver products with…

Posted by: Vin Vashishta

November 29, 2022

How Semantic Layer Helps Scale up DataOps

This blog is written by Vidhi Chugh, Staff Data Scientist at Walmart Global Tech. Vidhi is an award-winning AI/ML innovation leader who works at the intersection of data science, product and research teams to deliver business value and insights. She carries…

Posted by: Vidhi Chugh

November 17, 2022

Making Everyone a Power Data Analyst with a Semantic Layer

The number of data analysts at your organization is a limiting factor that determines how many stakeholders are able to make data-driven decisions. To make better-informed decisions, organizations need to increase the number of people with the tools and expertise…

Posted by: Dave Mariani

November 15, 2022

How to Streamline Data Science Workloads and Feature Engineering in Snowflake

Our partnership with Snowflake has drastically sped up our customers' data processing capabilities by eliminating the need for tedious data engineering tasks. By driving workloads directly to Snowflake, users can create accelerated time-to-prediction rates and a more efficient method to…

Posted by: Daniel Gray