Analytics

LLM and AI advancements ignite a new chapter of FinOps

Organizations across all industries are racing to understand large language models (LLMs) and how to incorporate the generative artificial intelligence (AI) capabilities provided by LLMs into their business activities. Thanks to LLMs’ broad utility in classifying, editing, summarizing, answering questions, and drafting new content, among other tasks they are being embedded into existing processes and used to create new applications and services.

Disney Parks Data Transformation Powered By Snowflake's Data Cloud

While companies have different motivations for modernizing their data strategy and platform, we all agree that investment in data is necessary and valuable. With the pace at which technologies are changing, it can be difficult to make the right decisions for your organization. By starting with a strong foundational data strategy, you can choose the right tools and architecture to transform your business. Listen in to learn how Disney Parks is leveraging Snowflake to implement a modern data strategy and lead a multifaceted transformation to disrupt not only the technology but also the culture of how engineering and business teams use and collaborate with data.

Coupler.io: Your Business Data Analytics and Automation Solution

Companies no longer question the importance of data analytics for their business success. With the help of data, brands can predict business outcomes, detect purchasing patterns, track customer behavior, and improve overall decision-making. However, many organizations still struggle with implementing the needed steps for robust data analysis. They often lack the time and expertise to use data to its fullest potential.

What's new in ThoughtSpot Analytics Cloud 9.4.0

ThoughtSpot Analytics Cloud 9.4.0 let's you catch up on your notifications as content is shared with you, view data from multiple countries in a single viz, and live query your data in Amazon Aurora and Amazon RDS. Plus lots of new features and enhancements you’ve been waiting for. See what's new in ThoughtSpot today!

Benefits of Real-Time Data Integration for Business Intelligence

Data-driven businesses like yours require up-to-the-second actionable insights to stay ahead of the curve. According to recent industry reports, businesses that can make timely decisions based on real-time data have been shown to outperform their competitors by 20%. Real-time data integration processes and transfers data to a centralized location as quickly as possible, enabling you to make informed decisions and address organizational challenges immediately, rather than in a few hours or days.

Streaming Pipelines to Data Warehouses - Use Case Implementation

Data pipelines do much of the heavy lifting in organizations for integrating, transforming, and preparing data for subsequent use in data warehouses for analytical use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.

The Tech Executive's Guide to Data Streaming Systems

In today's fast-paced business world, relying on outdated data can prove to be an expensive mistake. To maintain a competitive edge, it's crucial to have accurate real-time data that reflects the status quo of your business processes. With real-time data streaming, you can make informed decisions and drive value at a moment's notice. So, why would you settle for being simply data-driven when you can take your business to the next level with real-time data insights??

Implementing MLOps: 5 Key Steps for Successfully Managing ML Projects

MLOps accelerates the ML model deployment process to make it more efficient and scalable. This is done through automation and additional techniques that help streamline the process. Looking to improve your MLOps knowledge and processes? You’ve come to the right place. In this blog post, we detail the steps you need to take to build and run a successful MLOps pipeline.