We, of course, will never know everything. But with the arrival of Big Data, machine learning, data interoperability, and all-to-all connections, our machines are changing the long-settled basics of what we know, how we know, and what we do with what we know. Our old—ancient—strategy was to find ways to narrow knowledge down to what our 3-pound brains could manage. Now it’s cheaper to include it all than to try to filter it on the way in. But in connecting all those tiny datapoints, we are finding that the world is far more complex, delicately balanced, and unruly than we’d imagined. This is leading us to switch our fundamental strategies from preparing to unanticipating, from explaining to optimizing, from looking for causality to increasing interoperability. The risks are legion, as we have all been told over and over. But the change is epochal, and the opportunities are transformative.

Only a small fraction of global firms increase productivity year after year, according to the Organization of Economic Cooperation and Development (OECD). Creating and using unique stocks of data capital is one of the key tactics these firms use to widen their lead. Come learn how two new ideas—data trade and data liquidity—can help all companies, not just superstars, take advantage of the data revolution, and hear examples of firms already putting these ideas into practice.

As the market moves from fascination with the wonders of the current bloom of Big Data technologies to reconsideration of their risks and perils, enterprises and IT operations are left to figure out how these developments might impact their business—and when. Hadley Reynolds, co-founder of the Cognitive Computing Consortium, presents an open reference framework jointly developed with Babson College’s technology management program. The framework gives executives and operating managers a tool to characterize the impact and behaviors of potential AI applications. Beyond impacts and behaviors, the framework integrates profiles of skills and resources required to effectively execute cognitive tasks.

For the last 2 days, we've heard about exciting developments with technologies focused on the business uses for data, including machine learning, cloud computing, Hadoop, and, of course, cognitive computing. What we do with these technologies to advance business opportunities and avoid business risks is now up to each individual attendee. What are the takeaways that impact us?