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Some of the most popular trends and technological advancements that will be shaping the data ecosystem in the following years are the internet of things, artificial intelligence and machine learning. So, let’s find out more details about these advancements and how they’ll affect the overall future of big data.
Internet of things
Just a few years ago, the only smart devices we had were smartphones, and the internet of things was nothing but a concept. Fast-forward to today, and we’re completely surrounded by smart TVs, smartwatches, smart glasses, smart fridges, smart light bulbs, smart coffee makers, and smart toasters… we are now living in a true technology-enabled network of all networks.
There are billions of physical devices around the world that are now connected to the internet, all collecting and sharing data. Almost anything can become a part of IoT just by adding digital intelligence to it — that way, you can enable any physical object to communicate real-time data without involving a real human being. Smart devices help us simplify, streamline and automate our tasks and daily routines, and during that process, they collect enormous amounts of data.
A smart city is just one example of how advanced and huge an IoT project can be. Collecting data from citizens and devices, then processing and analyzing that data to monitor and manage the whole urban ecosystem — traffic and transportation, schools, hospitals, information systems, power plants, utilities, water supply networks, waste management…
By 2025, there will be 75 billion Internet-of-Things (IoT) devices in the world.
As technology keeps on advancing, the price of new smart devices keeps decreasing, and that enables more and more users to start embracing the internet of things lifestyle. This trend will continue in the next few years, and we can expect exponential growth in the amount of data smart devices will be generating.
More data means more precise insights, more value and, ultimately, more chance for monetization.
Artificial intelligence and machine learning
Artificial intelligence and machine learning are still in their infancy, and that is especially true when we talk about widespread commercial use, but their potential is enormous as they have the ability to provide businesses with extremely deep insights when it comes to data analysis. Top that with a large amount of money that is being invested in artificial intelligence, and it’s easy to see how both AI and machine learning are poised to become key components of the data industry as they can be used to make predictions and get valuable insight into business operations.
37% of organizations have implemented AI in some form – that’s a 270% increase over the last four years.
Artificial intelligence and machine learning enable systems to learn from data, identify patterns and make decisions with minimal human intervention. The benefits of using artificial intelligence and machine learning within big data ecosystems are actually twofold. AI aspect can provide extremely personalized user experiences which can lead to users generating much more data of a much higher quality, and machine learning’s ability to perform extremely complex data analysis is what will enable businesses to use it as a very precise decision-making guide.
Some big companies, such as Amazon and Netflix, use AI-powered recommender algorithms. Amazon has “Customers who bought this item also bought” and Netflix has its AI recommendation system that saves the company about $1 billion each year — over 75% of user picks on Netflix come from those recommendations. You can also notice that the number of devices with face recognition is rapidly increasing and that growth is due to the improvement in accuracy in facial recognition technology that is also powered by AI.
According to the emarketer study, about 112 million people in the US would use a voice assistant for various purposes — Siri, Cortana, Google Assistant, and Amazon Alexa are just some of the examples of intelligent personal assistants. Furthermore, with machine learning technology, you can automate responses to cyber-attacks without the need for human intervention.