With all the talk about predictive machine learning and deep learning applications, one can lose sight of the data engineering, some might call it data art that is needed to prepare the data to work on. Many questions go into the planning for deep learning applications like should the processing be disturbed; how much noise obscures the signal arriving from internet of things devices such as cell phones. In the case of mobile phone sensors, data preparation for deep learning applications can present unique problems, data preparation can involve considerable preprocessing. Insurance and other industries are entering the golden age of sensor data, but the data needs preprocessing because the data initially is very noisy. Given the Data, the algorithms will figure out the right transformations of the data. Read more at: http://searchdatamanagement.techtarget.com/news/450419925/Data-prep-for-deep-learning-applications-means-careful-planning

Going digital has directly hit demographics and technology of retail industry in a positive way. The economy of the retail industry is also shifting quickly, retail stores must do more to keep their stories intact without damaging their brand name. The retail industry is at a crucial moment with online sales resulting in periodical of all sales. Retailers must first examine and learn from leading physical and online retailers before implementing an e - commerce strategy. Retailers need to enhance their technology user base by collaborating with e - commerce giants of the market. The majority of e - commerce firms use social media like Facebook, Twitter and Instagram, to give a shot to great digital marketing tools, retailers should use these platforms more often to disclose their great offers and collection.  Read more at: https://www.entrepreneur.com/article/290131

Many data analytics firms are working for the betterment of the farmers. These companies integrate satellite, weather, and IoT analytics with the agricultural sector. They use its proprietary machine learning and parallel computing techniques, to resolve complex relationships like crop growth and soil health. Using analytics farmers can opt for a smart sampling procedure using satellite – based crop clustering techniques, which reduces the time for identification of these plots and optimize their locations. While the former requires timely crop intelligence, crop insurance companies need highly accurate assessment of risk. The satellite imaging analytics serves two purposes: First, it ensures that the farmers receive a fair and immediate compensation for crop loss due to adverse climatic conditions. Second, it enables insurers to settle claims speedily due to the availability of data in near-real time without any manual intervention. Read more at: https://yourstory.com/2017/05/satsure/