Gen AI Pulse | Augmented people Aug 16, 2024 Get into the PULSE of Gen AI with real-world data, actionable insights, and the transformative impact of this ongoing revolution. This is our quarterly paper written by CI&T specialists who are implementing AI solutions at the forefront with our clients. Learn more
2021 - The Year of the Digital Revolution Apr 16, 2021 Recently, Bob Wolheim, our CSO, interviewed Sérgio Vezza, Vice President of BEES, the startup behind Ambev and AB InBev, owners of brands like Budweiser and Stella Artois. Learn more
Business Impact: The Beginning of Transformation Apr 23, 2019 Named as the biggest challenge by C-Levels today, achieving success in the process of transforming companies into digital has a secret: the generation of business results from the beginning. Learn more
Five years in five months: the leap from traditional to digital operations Aug 07, 2020 With a 45-year history in Brazil, the Carrefour chain has built a solid operation that today has 72,000 employees and 498 stores distributed in 26 states. Learn more
An American Mass Media Company: Transforming Data Management Learn more Goals ● Serve as a central self-service portal and repository for specific analytical data● Allow for retrieval and analysis of data across the organization ● Cater to different stakeholders’ data needs and address varying levels of technical proficiency● Enable both quick business insights and deeper data science initiatives Challenges ● Current data exists in silos across the organization● Retrieval is not self-service ● Background and definitions require human interaction Workstream Pillars 1. Data Management The goal was to establish "data process" routines and platforms separated from "data access" methods and applications to increase readiness for analytic platform capabilities—Date Science & Machine Learning. 2. Business Intelligence and AnalyticsWe enabled self-service for data visualization using presentation layer tools like Tableau. We build a layer where minor items can be turned around quickly to enable any given business requirement. 3. Automation and Continuous Integration We established CI-CD driven data extraction, transformation, and load routines in conjunction with deploying automated testing, error tracking, and alerts for optimized performance. 4. Cloud Infrastructure We recommended establishing a Cloud infrastructure, which is both economic and competitive. It provides a scalable infrastructure and high-performance ETL and Business Intelligence layers. Architecture Overview The architecture behind the Analytics Platform is made of custom components built on top of Cloud-based services and products. These components are responsible for managing a variety of data manipulation steps, such as extraction, transformation, and load, among others. They are built in such a way that they can be easily reused throughout the platform and connected to each other in order to create pipelines that are more complex.You can think of these components as independent services that migrate data from one place to another or that transform from one format into another format. The output of a step can be the input of another step and so on. Usually, all the pipelines are built so the data ends in a Cloud-based Data Warehouse solution, which enables businesses to query and work on top of that data—the main business goal of the Analytics Platform.