CIO’S GUIDE TO DATA ANALYTICS & MACHINE LEARNING

INTRODUCTION

Today, data is everywhere. It streams in from connected devices at dizzying speeds, in an array of formats, from billions of users. Big Data is often cast as an opportunity, but only for businesses that are structured to handle its volume and diversity. For other companies, the flood of data can represent a risk—that potential insights go untapped, customer needs go unmet, and businesses keep making uninformed decisions.

Two factors make the current landscape different from past evolutions:

  1. Exponential increase in the volume and diversity of data being generated by billions of users and devices.
  2. Demand for immediate access to high-quality data and insights.

Each has brought new urgency to how companies manage data. In addition, the cost and performance of many cloud capabilities have reached a tipping point, helping make machine learning (ML) and artificial intelligence (AI) accessible to every business.

Despite widespread recognition of the value of data, few companies have implemented modern data strategies.Building on original research and Google’s own contributions in the cloud, this guide is designed to help IT and business leaders implement modern, cloud-based strategies for data management. In each section, we highlight technologies helping companies turn a vast, complex data landscape into useful business insights.

Google Cloud’s Guide to Data Analytics & Machine Learning draws upon Google’s twenty years of tackling some of the industry’s toughest data problems. Along the way, we’ve contributed original research that has helped to shape the Big Data landscape: from two research papers in late 2003 and 2004, which together spawned the Hadoop movement; to the Dremel paper, which forms the basis for the cloud data warehouse capability you’ll read about in this guide.