From data silos to decision intelligence- A playbook for CPG leaders

In a fiercely competitive market, it’s more important than ever to be able to turn complex CPG data into meaningful insights.

Read this whitepaper to understand:

The different data level challenges faced by CPG leaders in their data to insights journey
How multiple data sources can be integrated to paint a cohesive picture for category leaders
How Decision intelligence can help brand and category leaders gain the agility they require to succeed.

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Overall equipment effectiveness: is it still relevant in the industry 4.0 era?

Industry 4.0 builds upon the enterprise-wide automation stack (that characterized Industry 3.0), focusing on integrating more (and newer) technologies, like IIoT and data science, into the production environment. The goal is to blur the chasm between the physical (Operational Technology) and the digital (Information Technology) world - creating a fully connected, integrated, data-driven, and autonomous digital factory.

Prompt Engineering: The New Era of AI

Trends are changing in the data science domain every day. Many tools, techniques, libraries, and algorithms are developing daily. This constantly changing landscape keeps the data science domain at the bleeding edge. The techniques and methods used to solve different tasks in Machine Learning (ML)/ Deep Learning (DL) and Natural Language Processing (NLP) are also changing.

Reproducibility in Machine Learning

Usually, Machine Learning (ML) based experimentation follows the 80/20 rule. 80% of the time spent by Data scientists is relegated to finding, cleaning, and organizing data, while the rest, only 20%, goes into analyzing or experimenting.