We’re all consumers, and companies realize that in order to compete they must put the consumer at the center of their activities. They must understand the consumer, anticipate their needs and provide easy ways to engage. We have all become used to that, and are increasingly expecting the same kind
It seems that everyone these days is interested in big data: using more data, more quickly and making better decisions from it. How does your company interact with data? Or, more specifically, how do business users interact with data?
I've spent more years than I want to admit on the business side of the insurance industry. In my current role I work on the value that big data and analytics can deliver to an insurer and, in turn, to their policyholders.
On the other side of things, I own insurance policies, just like most.
Big data is poised to reshape the way we live, work and think. The telecommunications sector, which has been slow to realize the shifting ground, is catching up to redefine the customer digital experience, using big data and analytics.
When I think back to last year’s Information on Demand (now Insight) conference, one customer story in particular comes to mind: Memorial Healthcare System’s uncovering of vendor fraud, a bid rigging scheme and a potential staff risk in what began as an effort to simply streamline and improve the
Insight 2014, formerly Information On Demand, will bring more than 13,000 business and IT professionals together to exchange ideas and share experiences around harnessing the data coming from all directions in real time. Industry pundits, peers and thought leaders will connect the dots between big
Many make out the data scientist to be a Renaissance woman or man who can single-handedly elevate the organization’s analytics savvy. However, preparing students for corporate roles in data science means training them for many positions on a team. At Arizona State University, we work closely with
A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively.
The need for a data scientist is all the rage right now. At every marketing conference I go to, companies are clamoring for their skills, but the supply and demand is not coming to the needed equilibrium. We are faced with the choice of continuing to wait, or to employ a solution that is already at