"In the future we will be designing our world, not just studying," predicted Robert Dijkgraaf, a theoretical physicist at the Institute for Advanced Study at Princeton, a few weeks ago at DLD Summer in Munich.1 Information gathered from our current world will fuel those designs. Artificial intelligence will guide decisions and help us navigate this new world. The theme of the event--"It's Only the Beginning…"--reflected the journey to realizing these predictions. The panel "Doing Better with Data and AI" addressed new frontiers for using AI to augment human intelligence and the modern data infrastructure required to make it happen.2 Zayd Enam, CEO and Founder of Cresta, argues that the current attitude concerning artificial intelligence is "lazy."3

If your aging on-premises data systems are unable to meet current demands for business agility and are time-consuming and expensive to manage, why continue to invest in them? Organizations looking to become more data-driven should consider accelerating their timetables for migrating away from monolithic, siloed systems that inhibit innovation and agility to a modern data infrastructure. The hallmarks of a modern infrastructure are its flexibility and ability to continually and automatically analyze and act on holistic, current data. It lets you store any amount of data at a low cost in open, standards-based data formats. It isn't restricted by inaccessible data silos and empowers people to run analytics or machine learning using their preferred tool or technique.

As an industry, we've gotten exceptionally good at building large, complex software systems. We're now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. In fact, many of today's fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond.