ABOUT

Everywhere we look today, data is becoming more central to how everything works. Data plays a far larger role in our lives now than it did a decade ago, and all signs point to its influence growing in the coming years.

But while technologists have been tinkering with data for 30+ years, it wasn’t until the mid-2000s that data became a breakout success and started massively transforming the way people view its role in their lives.

Why the holdup? The first wave of data (Data 1.0) was all about business analytics – tracking and measuring company performance in the real world. Super valuable; extremely complex and difficult. Despite hundreds of billions of dollars flowing into the industry year after year, the technology never truly delivered.

The second wave (Data 2.0) is when data started to become hot. All of a sudden, a new class of digital disruptors arrived on the scene, upending everything from retail, finance, media and entertainment, all the way out to automotive manufacturing and everything in between.

The companies succeeding in Data 2.0 are seen as masters of the universe. In reality, they are just solving a different data problem. Unresolved Data 1.0 problems remain a thorn in their side.

For those responsible for solving Data 1.0 problems, the pressure is on. The success of Data 2.0 is being lauded over them while the differences between these two categories of data problems are overlooked or ignored. That’s frustrating because, no matter how you cut it, the systems and that make 2.0 so successful cannot move the needle on 1.0 problems. It takes a fundamentally different strategy.

Data 2.0 is the realm of machine learning – closed loop environments where AIs make thousands of decisions per second without any human involvement beyond from the initial setup and ongoing maintenance. Aside  from the engineering, there are no humans in the loop. Data 1.0, on the other hand, is all about data-informed human decision making. In essence, helping people make better decisions by using the data inside their business applications.

When you solve Data 1.0, you can start to answer questions about what happened in the past. Solving Data 2.0 takes it a step further and helps unravel why it happened. Bring them both together into a seamless experience and you begin to unlock Data 3.0 – the holy grail of data analysis – figuring out what happens next.

In this keynote, Matthew Halliday, co-founder and EVP of Product at Incorta, will provide a new framework for thinking about data in more holistic terms, along with strategies for moving to Data 3.0 successfully.