A look at the evolution of the data landscape, how technology is helping businesses solve for now, and how the data analysis that’s possible today can provide us with a new understanding of how we, as a society, can perform better in the future.
Perhaps the most dramatic outcome of the digital revolution is the amount of data that’s now collected and analyzed. IDCthat in 2010 the world created about two zettabytes of digital information. If that were put into 1 GB thumb drives that were then laid end to end, it would make a line that could stretch across 184 million football fields.
That sounds impressive, until you consider that the World Economic ForumThe World Economic Forum figures there will be 44 zettabytes of digital information collected in 2020.This growth in data produced and collected is profound—and as COVID-19 has caused disruption to economies, businesses, and lives around the world, it has become more urgent to not only marvel at the scale of all this data but also to understand how it’s being put to work. There has never in human history been such an information explosion. You could attribute this to the rise of smartphones, sensors, and connected vehicles and appliances, among other digital artifacts. Much of what we touch and work with now automatically generates data. But the real reason why we’re seeing this increase is the growing utility of data analytics and automated responses to analytic decisions. The past decade’s data explosion created a virtuous circle of data analysis and action, leading to new insights, data creation, and data analysis. We’ve seen companies collect more data than ever before as they’ve raced to transform their businesses and make data-driven decisions. To stay viable and thrive in this rapidly changing environment, businesses need to be good at anticipating what’s next and reacting in real time. This has spurred all kinds of personalized, connected objects, such as industrial machinery that saves money by signaling when it needs maintenance. And it's spurred services that combine digital information from traffic information, and social network graphing. It's also brought greater efficiency to things like supply chains and consumer marketing, to name just a couple of examples. This explosion also pushed other organizations to re-evaluate their strategies and look to cloud infrastructure to provide relief from legacy tools that could not keep up with the growing needs of the business.There have been pandemics in human history. This is the first where people have had access to online digital technologies that enable things like working, schooling, seeing the doctor, grocery shopping, and even attending digital conferences from home—all necessary activities for suppressing the rate of infection. Some of these changes may be permanent, leading to structural changes in the society and the economy. Consequently, data growth and analytics-driven decision-making are likely to continue proliferating. To stay viable and thrive in this rapidly changing environment, businesses need to be good at anticipating what’s next and reacting in real time. For example, with changing consumer demand patterns, retailers need to make their inventory management, supply chain infrastructure, delivery mechanisms, and customer experiences much more data-driven and dynamic. Not surprisingly, we've seen for ourselves that they are leaning heavily on real-time and predictive analytics to do just that—it’s vital for their survival and success. Increasingly, they are also exploring the use of new industrial innovations like virtual showrooms, robotic manufacturing, and driverless delivery fleets in a post-COVID-19 digital economy. Adoption of these new technologies will create even more data and will further accelerate the use of sophisticated analytics. Businesses are exploring the use of new industrial innovations in a post-COVID-19 digital economy. Adoption of these new technologies will create even more data and will further accelerate the use of sophisticated analytics.In my role at Google Cloud, I’ve grown a profound appreciation for the core values of speed, scale, access, and flexibility that are at the heart of good data analytics technologies—in products like
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