Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at alan.kalton@aktana.com.

Google Analytics and Salesforce are working together to connect sales, marketing and advertising data, giving you the full view of what's working across your ads, websites, emails, call centers, and more.

As we've reported previously, the Internet of Things is changing data science, and a visiting executive from analytics company SAS went into further detail into how IoT along with artificial intelligence is reshaping the industry. Oliver Schabenberger is the executive VP of SAS's research and development division and recently appointed CTO, and was recently in Sydney to discuss updates to the new SAS Viya analytics platform and to share his thoughts on the future of the analytics industry. He said that IoT has led to the increasing importance of edge analytics, with data now being observed on a continuum, requiring differing techniques to be deployed depending on observation point, accuracy and speed of data movement. "We can no longer just think about processing data in the cloud; we also have to think about event stream processing," he added. He also said that analytics software is quickly transitioning into the cognitive space, where sensing, listening and gesturing will become common forms of input, and reading and writing of human-like responses will become common forms of output.

Machine learning is a branch of artificial intelligence that enables machines to learn on their own, without much human supervision, drinking deeply from the well of Big Data. Computers essentially write and follow their own programs based on the statistical relationships they discover in unstructured data--and are roiling industries ranging from credit cards to automobiles in the process. "It's speed and the ability to learn from data that gives machine learning the power to provide tremendous insights in ways that humans could never do on their own or with basic business-intelligence tools," says Mike Tuchen, CEO of Talend, a Los Altos, California-based big-data integration firm. Or machine learning "can use algorithms to mine historical data for outcomes that are different than with traditional simulation." At financial companies, for instance, machine learning can assess insider-trading activities and identify potential fraudulent activities that could trip a regulatory investigation.