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Should you have a Chief Data Officer/Chief Narrative Officer? By Kim Reid, Principal Analyst For years, higher education has been called data-rich and information-poor. Now, the ability to collect and analyze institutional data is becoming sophisticated enough to tap into strategic insight at very high levels. The era of big data in higher education is here, ready and waiting for analysis. Higher education has a complicated web of data sources to manage and understand. Is it time to appoint a Chief Data and Narrative Officer who is responsible for directing a coherent institutional understanding based on robust data?

Two plenary sessions at the recent ACT Enrollment Planner­s Conference reminded us of the growing importance of data in higher education management. Eric Newberger from the U.S. We now have an unprecedented amount of data available to us in higher education: CRM data, web analytics, enrollment data, student data, learning management system data, and alumni data. Exploring High-End Visualization for Research and Education -- Campus Technology. 2015 Innovators Awards Exploring High-End Visualization for Research and Education Georgia State University created a technology-rich visualization space that supports research and instruction and explores the transformative potential of visual media across all disciplines. By Meg Lloyd07/22/15 Category: Education Futurists Institution: Georgia State University Project: CURVE: Collaborative University Research & Visualization Environment Project lead: Bryan Sinclair, associate dean, university library Tech vendors/partners: Apple, CineMassive Displays, Dell, Nvidia Standing 24 feet wide, CURVE's interactive video wall is used not only for big data research, but also for instruction that leverages dynamic data explorations.

Working together, university units contributed $1.2 million to transform an entire 3,300-square-foot floor of the university library, giving CURVE a central home on campus and sending a clear message that the technology is there for everyone to use. About the Author. Lorem. Artificial Intelligence and the future | André LeBlanc | TEDxMoncton. Professor Stuart Russell - The Long-Term Future of (Artificial) Intelligence. The long-term future of AI(and what we can do about it): Daniel Dewey at TEDxVienna. Google's DeepMind co-founder Demis Hassabis on the future of AI. Where The Future Of AI Is Headed. Demis Hassabis - The Future of Artificial Intelligence. The future of artificial intelligence. Elon Musk on the Future - Prediciting A.I. and the World of 2035. The Future of Artificial Intelligence - Up Next.

Lorem. Moneyball for Litigation? A Conversation with Premonition’s Toby Unwin. Even if you’re not a baseball fan, there is a good chance you’ve heard of Moneyball. The term originally referred to the book written by Michael Lewis about the Oakland A’s, but it’s taken on its own meaning, especially after Brad Pitt played the part of Billy Beane.

The simple takeaway from Moneyball is that (in my best business school voice) by focusing on certain metrics and not others, you can expose inefficiencies and get ahead of the market before the market corrects itself. Is there a Moneyball for litigation? Several companies think so. Last month, I had an opportunity to catch up with Owen Byrd from Lex Machina, a well established venture backed company in Silicon Valley that provides its customers with critical information about what happens in the courtroom. They have even referred to themselves as “Moneyball law.” Data, Startups And Doing Things That Matter. Prominent voices in tech journalism are criticizing the global startup community for building software products and services that cater to the 1 percent or have zero societal value.

They claim that curated shopping services are not really revolutionary; that food delivery services aren’t changing the world; and that ridesharing is nice but not exactly earth-shattering. And, clearly, building ways to sell more ads adds nothing to the global karmic bottom line. Everything about Silicon Valley is a lie. So goes the thinking about the lack of meaningful startups. However, in my world, I see numerous critical tools and services coming from startups that have world-changing potential. These tools and services help mere mortals wrangle data and use it to greater effect. Data is power. Breaking down data Bellingcat was launching an effort to crowdsource sightings of Russian armor, vehicles and heavy weapons moving in and around the Ukraine.

The project cost? Backing the data revolution. Seldon - Open Source Machine Learning for Enterprise. Premonition - uses artificial intelligence, predictive analytics. The current state of machine intelligence 2.0. A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive.

Figure 1 showcases the new landscape of machine intelligence as we enter 2016: Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways. Machine intelligence already helps us get the important business information we need more quickly, monitors critical systems, feeds our population more efficiently, reduces the cost of health care, detects disease earlier, and so on.

Reflections on the landscape Achieving autonomy The new (in)human touch 50 shades of grey markets The great verticalization. Creating Value through Advanced Analytics. We know from extensive research that decisions matter—a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it (see Figure 1). Decisions are where advanced analytics can add real value, quickly. So let’s examine how these organizations use the tools of analytics to improve their decisions and thus their performance.

Identifying the decisions that benefit most from advanced analytics Look first at one extreme of the decision spectrum: the small, everyday decisions that add up to a lot of value over time. In the middle of the spectrum, however, lies a vast and largely unexplored territory. Imagine, for example, a property and casualty company that specializes in insuring multinational corporations. What is the outcome? Cocreating analytics-based solutions.