A certain cross-country survey revealed that the Internet of Things (IoT) was first adopted by the Healthcare Industry. Organizations that adopted the IoT, believed that they could meet business challenges like patient engagement, loyalty, safety and satisfaction without any delay.
According to Laura DiDio, Director of IoT Strategies Enterprise, the need for IoT is felt highly by enterprises to address and solve pragmatic business issues. Andrew Brown, SA’s Executive Director of IoT and Mobility says that IoT can also be used for cost savings, development of new services, increased competitiveness and better understanding of the business.
It can be said that the Internet of Things has great potential in the field of healthcare and with evolvement of mobile capabilities its growth rate will be escalated.
Read more at: http://healthitanalytics.com/news/healthcare-industry-is-an-early-internet-of-things-adopter

The Internet of Things has the ability to benefit the healthcare sector vastly in the future. With EHR clinical analytics and sophisticated machine learning combining with big data from wearable, home monitors, medical devices, the Internet of Things has a huge potential for creating a productive environment of predictive analytics and prescriptive insights. Yet the concept is met not without challenges. In order to successfully reap the benefits, Jennifer Bresnick suggests development of certain core competencies, like "Developing the Mindset", "Building the health IT infrastructure" and others. Read More at: http://healthitanalytics.com/news/five-core-competencies-for-the-healthcare-internet-of-things

Finding balance between data size and analytical modeling needs can be a problematic process in big data analytics. But it is essential for the success of big data analytics projects. The size of big data sources at social networking companies can obstruct analytics efforts. So, restraining the opportunity of an analysis by using data sampling techniques can be helpful. On the other hand, there are many businesses that lack the data needed to answer key business questions. For those organizations, development of effective analytical models can only take place after new data types are acquired and their analytics infrastructure is built. To know more, go through the article by Ed Burns (site editor of SearchBusinessAnalytics) at: http://searchbusinessanalytics.techtarget.com/feature/Balance-required-between-big-data-volume-analytics-needs