These days, talking about the future can get quite exciting when you think about all the possible advancement in technology that could happen in the next decade alone. Technology is the future but in order for technology to be all that it can, there are some assets that have to be available to help. The most important of the assets is data. Data Is a very important asset in the future technology because every advancement, every development, every invention, relies heavily on data. The availability of data, the sharing f data, the storage of data and the interpretation of data all is important to the future of technology.
Some of the important invention using data are applications like Open source systems. One survey found that nearly 60 percent of enterprises expect to have open source systems running in production by the end of this year. Many experts continue to look to expand into open source and big data processing. Many will be seeking technologies that allow them to access and respond to data in real time.
In-memory technology is a technological system that allows companies to speed up their processing of big data. One of the technologies that companies are investigating in an attempt to speed their big data processing is in-memory technology. In a traditional database, the data is stored in storage systems equipped with hard drives or solid state drives (SSDs). In-memory technology stores the data in RAM instead, which is significantly faster.
Cloud computing is the use of scalable computing services over the internet to stored data and information that can be easily accessible from anywhere in the world using any device. Anybody can gain access to a shared network of computer systems and pay for only the level of computing services he/she uses. Before the advent of cloud services, many companies couldn’t leverage big data because of the associated costs of IT systems. Data analysts and researchers have predicted that by 2020, a minimum one-third of all data will be stored and analyzed using cloud computing.
Machine learning which is also sometimes referred to is artificial intelligence is used by companies who are more advanced in the technological space. As big data analytics capabilities have progressed, more and more of these companies have begun investing in machine learning (ML). Machine learning is a branch of artificial intelligence that focuses on allowing computers to learn new things without being explicitly programmed. In other words, it analyzes existing big data stores to come to conclusions which change how the application behaves.
According to Gartner machine learning is one of the top 10 strategic technology trends for 2017. It noted that today’s most advanced machine learning and artificial intelligence systems are moving “beyond traditional rule-based algorithms to create systems that understand, learn, predict, adapt and potentially operate autonomously.”
Though their terms machine learning and artificial intelligence are used interchangeably, they are quite different. With the computing power gotten by artificial intelligence (AI), a scientist can be able to spend less time analyzing data. In the healthcare industry, pharmaceutical R&D labs are using AI to predict the relationships between biological mechanisms and disease symptoms. AI has the potential to improve the diagnosis and treatment plans of patients, too.
But we must still be sure that expert human judgment can be applied to AI’s findings. Massachusetts Institute of Technology (MIT) is a hotbed of AI discoveries, so its recent multi-million dollar initiative with Harvard to develop ethical standards in the field is a good example of matching an eagerness to advance AI with a commitment to also understanding its limits. Companies like Apple, Google, Facebook, and Tesla are also advancing rapidly in the field of AI.
Wearable devices help patients capture data about their health and this data is becoming increasingly important to healthcare professionals who provide treatment to these patients. For example, devices like the Apple Watch are becoming more ubiquitous, they are poised to become a valuable tool for incentivizing healthy behavior. For example, a life insurance provider John Hancock said it would offer its policyholders an Apple Watch for just $25, provided they exercise regularly for the next two years. Those who don’t maintain the regimen will have to pay off the full price of the watch in installments. And it’s been reported that other policy providers like Aetna may be considering something similar. One con of this is that your insurance company will have full and maybe real-time access to the data about your health and probably anything else you do with your apple watch so your phone, details, messages, etc. Scary how invasive that could be. Wearable devices can generate real-time data about patients and allow them to report their own subjective symptoms more accurately.
There is a close relationship with predictive analysis and machine learning. Machine learning systems provide the engines used for predictive analysis software. Analytics tools can be used to investigate occurrences with data. With predictive analysis, companies can use the big data analysis to predict what will happen in the future. The number of organizations using predictive analytics in 2016 was surprisingly low—only 29 percent according to a 2016 survey from PwC. However, numerous vendors have recently come out with predictive analytics tools, so that number could skyrocket in the coming years as businesses become more aware of this powerful tool.
Intelligent applications often incorporate big data analytics, analyzing users’ previous behaviors in order to provide personalization and better service. One example that has become very familiar is the recommendation engines that now power many e-commerce and entertainment apps. There are big prospects of what intelligent apps could achieve in the future. It is expected to be a long-term trend. Same goes for intelligent security. Many organizations are integrating their security information and event management (SIEM) software with big data platforms like Hadoop. Others are turning to security vendors whose products incorporate big data analytics capabilities.