Analytics

Want to Unlock Gen AI's Promise in Retail? Start with the Data Foundation.

There’s no question which technology everyone’s talking about in retail. Generative AI continues to promote incredible levels of interest with its promise of next-level productivity and new kinds of employee and customer experience. It’s all happening at light speed. When ChatGPT burst onto the scene, it gained hundreds of millions of users in a matter of months.

5 Ways to Approach Data Analytics Optimization for Your Data Lake

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.

Future-Proof Your Analytics Tech Stack

Future-proofing your analytics tech stack is essential for ensuring the longevity and success of your software applications. As the final stage of the data journey, analytics transforms raw data into actionable insights that directly impact business decisions and customer satisfaction. To effectively fulfill this role, analytics systems must possess a high degree of flexibility and scalability, seamlessly integrating with diverse applications and data sources.

Gen AI for Marketing - From Hype to Implementation - MLOps Live #32 with McKinsey and Iguazio

In this MLOps Live session we were joined by Eli Stein, Partner and Modern Marketing Capabilities Leader at McKinsey, to delve into how data scientists can leverage generative AI to support the company’s marketing strategy. We showcased a live demo of a customer-facing AI agent developed for a jewelry retailer, which can be used as a marketing tool to offer personalized product recommendations and purchasing information and support. Following the demo, we held an interactive discussion and Q&A session. Enjoy!

Qlik AutoML Series - Understanding Coordinate SHAP Analytics - Video 5

In this video, we breakdown the analytics you can create from the provided Coordinate SHAP data when used within Qlik AutoML. SHAP data helps determine the WHY behind the machine learning model predictions. Learn how SHAP values break down the influence of individual features on specific outcomes, helping you gain deeper insights into model behavior. This is part 5 of the Qlik AutoML series, focusing on making machine learning more interpretable for users.

ETL, As We Know It, Is Dead

It’s a new world—again. Data today isn’t what it was five or ten years ago, because data volume is doubling every two years. So, how could ETL still be the same? In the early ‘90s, we started storing data in warehouses, and ETL was born out of a need to extract data from these warehouses, transform it as needed, and load it to the destination. This worked well enough for a time, and traditional ETL was able to cater to enterprise data needs efficiently.