{"id":2627,"date":"2016-11-22T07:00:24","date_gmt":"2016-11-22T01:30:24","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=2627"},"modified":"2016-11-21T23:26:59","modified_gmt":"2016-11-21T17:56:59","slug":"big-data-trends-that-will-dominate-2017","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/big-data-trends-that-will-dominate-2017\/","title":{"rendered":"Big Data Trends that will Dominate 2017"},"content":{"rendered":"

\"big-data-cortana\"<\/a>
\n<\/a><\/p>\n

1. Internet of Things<\/h1>\n

Internet of things (IoT)<\/a> is the concept of connecting devices to the Internet or to each other through the internet. IoT is widely used in many industries. These devices are able to collect and transmit data via the Internet, contributing to our big data world. IoT can enable an exchange of data never available before, and bring users information in a more secure way. Cisco estimates that IoT will consist of 50 billion devices connected to the Internet by 2020. We can gain deeper insights into analytics using IoT to enhance productivity, create new business models, and generate new revenue streams. In B2B scenarios, IoT can be useful in many different categories including asset tracking and inventory control, shipping and location, security, individual tracking, and energy conservation. Also, It has proved to be helpful in the health care industry and cyber security.<\/p>\n

<\/h6>\n

IoT has created a vast number of job opportunities. Fastest growing IoT job positions include systems software developers (215% growth in the past year), information security analysts (113% growth), and computer systems engineers (110% growth). Also, the economic impact and benefits of IoT will be huge. Gartner predicts that the aggregated value and economic benefits of IoT will exceed $1.9 trillion in the year 2020 alone.<\/p>\n

2. Machine Learning<\/h1>\n

Machine learning includes algorithms that enable computers to learn from experiences over time. This will be an incredibly useful tool in big data and predictive analysis. There will be a rise of deep neural nets (DNNs), a type of advanced machine learning that uses sets of algorithms to model complex nonlinear relationships. Thus, machines will be able to perceive the world around them. Systems like Apple’s Siri<\/a> and Microsoft’s Cortana<\/a> are precursors to such full-scale autonomous agents.<\/p>\n

<\/a><\/p>\n

3. Virtual Digital Assistants<\/h1>\n

Virtual digital assistants are automated digital systems that assist users through understanding natural language in written or spoken form. This technology represents the intersection between speech recognition, natural language processing, and artificial intelligence. For example, customers can use the phone so that a retail brand’s digital assistant can recognize her and check her in when she arrives at the store. The assistant can pick up the conversation with the customer where it left off when they last interacted. Some of the promising mobile digital assistant technologies are, Cortana, Google Now, Siri, and Amazon Echo. Customers are willing to adopt these technologies to help them sift through increasingly large amounts of information, choices and purchasing decisions.<\/p>\n

4. Robo-Boss<\/h1>\n

Robo-bosses will increasingly make decisions that previously could only have been made by human managers. Evaluation of employee performance will become even more accurate as smart machines become the primary means of analyzing performance. Smart machines can effectively track activities and events that are too difficult for human managers to measure. Robo-bosses use advanced machine learning techniques like deep learning, to automatically learn and improve with experience. A good example of deep learning is Google’s self-driving car project.<\/p>\n

<\/h6>\n

So these are some of the big data trends which are about to arrive in 2017. What do you think will make the biggest impact this year? Comment and let us know.<\/p>\n","protected":false},"excerpt":{"rendered":"

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