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TickSmith Releases a Python Tool for the New Generation of Financial Data Scientists

TickSmith, a leader in Big Data applications, released an open-source Python API feature to obtain data from its flagship TickVault big data platform.  Based on Hadoop technology, TickVault processes, stores, and analyzes massive amounts of capital market data. The addition of the Python API  toolkit to TickVault provides data scientists fine-grained access to historical exchange […]

How NLP Can Help Healthcare “Catch Up”

In this special guest feature, Simon Beaulah, Senior Director of Healthcare at Linguamatics, discusses how natural language processing (NLP) has become a crucial tool in healthcare and the life sciences as these sectors struggle to catch up to other industries and transform their big data into actionable data.

Three Quarters of Fashion Retailers Will Invest in AI Over the Next 24 Months

Two separate research studies, funded by our friends Apptus and carried out by IMRG and Hive, have found that fashion retailers are planning significant investment in artificial intelligence (AI) over the next two years, as they seek to deliver the ‘real time relevant’ customer experiences that are vital to improving sales performance.

2017 is the Year IIoT Becomes Normal

In this special guest feature, Saar Yoskovitz, CEO at Augury, explores the efficiencies (both cost and speed) of the IIoT and why the next 12 months will see more companies normalizing IIoT benefits.

Medable Launches Cloud-Based Machine Learning Solution for Healthcare

MEDABLE Inc., a leading application and analytics platform for healthcare, announced Cerebrum, the first cloud-based machine learning solution created specifically for healthcare apps. Cerebrum leverages data gathering smartphones with a first-of-its-kind machine learning engine, resulting in health events becoming more easily predicted, such as warning an elderly relative when he is at greatest risk of a fall, or preventing an asthmatic child from triggering a life threatening episode.

Data Systems Strategies for Today’s Challenges

To address today’s challenges and come up with viable data systems strategies, pharmaceutical companies are looking outside the industry to leverage scientific and technical innovation in other markets.

How Big Data Can Help Save Our Planet

Big data analytics company and ETH Zurich spin-off Teralytics, Telefónica NEXT and sustainability solution provider South Pole Group conducted a study in Nuremberg, Germany that reveals the analysis of mobile network data as an effective way to estimate CO2 and NOX emissions in urban areas at very low cost.

Big Data Use Case – Ticketmaster: Using the Cloud Capitalizing on Performance, Analytics, and Data to Deliver Insights

The insideBIGDATA technology use case guide – Ticketmaster: Using the Cloud Capitalizing on Performance, Analytics, and Data to Deliver Insights provides an in-depth look at a high-profile cloud migration use case. One of the most widely discussed topics in IT today is moving workloads to cloud. The process of deciding whether or when to migrate to the cloud can be daunting. Further, finding the right technology fit for your enterprise objectives can be challenging with a cloud solution ecosystem filled with alternatives. To make the cloud adoption process more straightforward, this white paper provided a number of areas for consideration when evaluating a cloud platform.

Commercial Analytics and It’s Defining Role In Pharma Marketing

Pharma companies are working hard to build effective patient access programs and implement an effective commercial strategy around it. Expiring patents and competitive pressure is a real challenge and companies need to explore opportunities with the help of the vast amounts of data they own. Big Data and Analytics capabilities are helping companies define that strategy and execute a plan, which will help them succeed. Our friends over at Prescriber360 put together the infographic below containing facts and figures to emphasize the need of a well-planned analytics strategy.

MIT Sloan Professor Builds New Meta-analysis Method to Help Settle Unresolved Debates

Science progresses when researchers build on prior work to extend, test, and apply theories. Aggregating the quantitative findings from prior research – meta-analysis — plays a significant role in advancing science, however current techniques have limitations. They assume prior studies share similar substantive factors and designs, yet many studies are heterogenous. A new method, co-created by MIT Sloan School of Management Prof. Hazhir Rahmandad, solves this problem by aggregating the results of prior studies with different designs and variables into a single meta-model.