It’s common for advertisers to ask for more analytics from their publishers. They want to know if their ads performed well, along with a series of other factors. But the publishers cannot traditionally share their sensitive or proprietary data.
Two such partners may have their own private lists of users, customers, and products, that they cannot share with each other. Because of PII and other privacy concerns, or simply because this is valuable proprietary data.
Data-driven decision making is accelerating and defining the way organizations work. With this transformation, there has been a rapid adoption of data lakes across the industry.
To fuel this transformation, data lakes have evolved over the last decade making Apache Hive as the de-facto standard for data lakes. However, while Apache Hive can solve some of the issues with processing of data, it falls short at a few other objectives for next generation data processing.
Customers today are in different stages of their Data modernization journey, and they need advice to fully realize the power of data based on their workflows. This article aims to offer a phased approach starting from simple data analytics to complex data workflows, machine learning models and Data Visualization insights using GCP’s Smart Analytics tools. The tool helps customers to accelerate their data adoption strategy and extract meaningful insights based on their data workflows to drive their business forward.