The growing amounts of data that are being generated due to such trends as the Internet of Things (IoT) and cloud computing have naturally beget the need for data scientists who can collect, analyze and, most importantly, interpret these massive stockpiles of complex information to help their companies more quickly and accurately make better business decisions to give them a competitive edge over competitors and to improve their operations and make them more efficient. That in turn has created something of a land rush in what's become a rapidly expanding data science platform market of more than a dozen vendors that range from established companies like IBM, Google, Microsoft and SAS to an array of smaller, younger pure-plays. The goal of all of these companies is to give these data scientists a single place to develop and run algorithms, use machine learning to help build predictive models and then deploy those models into their businesses' operations. IBM offers such products as SPSS Modeler and SPSS Statistics as well as its two-year-old Data Science Experience, a set of tools around such aspects as machine learning via the vendor's Watson cognitive computing technology and the R programming language, through the open-source RStudio offering. SAS has its Visual Suite for data visualization, prep, analytics and model building, while Microsoft offers its Azure Machine Learning platform as part of the cloud-based Cortana Intelligence Suite and Microsoft R for those who want to code in R. Other names in the space include H2O, RapidMiner, Angoss, Knime and Dataiku.
Many executives in all branches of insurance underestimate the disruption that will occur -- and the new talent that is needed. The disruptive power of digital technologies has spread more slowly across the insurance industry than other financial services. This will not last much longer, and many insurance executives risk being caught by surprise by the drastic changes these advanced technologies will inspire. What kind of change is coming? In life insurance, a U.S. company says it can help companies accept or reject new policies by analyzing selfies to determine an applicant's health. In other examples, advanced analytics can help fine-tune prices and segment customers more accurately; machine learning can present precise cross-selling opportunities; and digital interfaces can support single-event policies and purchases without any interaction with human agents. Indeed, the first waves of disruption have already hit automotive insurance, where claims are being processed using smartphone apps and where online aggregators are leading buyers to the lowest-priced offers from a range of companies.
The classic guide for entrepreneurs preparing a pitch is Sequoia's Business Plan Template. This post aims to be a mere addendum to that in the age of machine learning. Why do investors spend so much time focusing on'differentiation'? Because the job of an investor is to allocate money to its best use. Investors shouldn't allocate money to a company unless it is crystal clear that the company is the best one to solve a particularly valuable problem.
HOUSTON & OSLO, Norway--(BUSINESS WIRE)--Arundo Analytics, a software company enabling advanced analytics in heavy industry, announced today the Fall 2017 general availability software release for its Arundo Enterprise suite. The latest release includes significant feature and functionality upgrades in Arundo's Edge Agent, Composer and Fabric software products for advanced analytics and Industrial IoT enablement.
The classic guide for entrepreneurs preparing a pitch is Sequoia's Business Plan Template. This post aims to be a mere addendum to that in the age of machine learning. Why do investors spend so much time focusing on'differentiation'? The job of an investor is to allocate money to its best use. Investors shouldn't allocate money to a company unless it is crystal clear that the company is the best one to solve a particularly valuable problem.