How AI is Revolutionizing Big Data Analytics
Artificial Technology (AI) and Big Data are two technologies that are changing the way businesses operate around the world. By using Deep Learning, businesses are able to feed AI algorithms with immense amounts of data generated on the internet, giving them powerful new insights on marketing strategies, product lines, and more.
AI makes it possible for the large amount of information created online to be usable by humans, processing information at a rate faster and creating key insights into customer habits or predicting their behaviors. So, how exactly does Big Data incorporate AI technology?
Understanding Big Data
Big Data refers to the large amount of data that is generated and collected through online and digital sources including social networks, smart devices, cameras and microphones, website traffic, and more. This information is then split up into four ways:
Volume: The sheer size of data generated online and collected is growing at an exponential rate because of increased reliance on the internet.
Variety: Not all data is the same. Information can be categorized as structured, semi-structured, or unstructured.
Velocity: The speed at which data is generated and how quickly it moves from one location to another is being increased by real-time data feeds.
Veracity: Accuracy and reliability are measured to determine how usable collected data can be.
Understanding AI and Machine Learning
Both AI and Machine Learning play significant roles in data processing and are especially useful for the large quantities of information being generated online. These systems can automate data analysis, processing information at a constant rate that is faster than any human.
By using Neural Networks, industries and platforms are able to crunch extreme numbers to create meaningful insights that show additional complexities. This can aid fields like manufacturing or marketing and e-commerce where Predictive Analytics can be used to estimate upcoming trends and patterns in production.
The Convergence of AI and Big Data
Due to consistent advances in AI, there are numerous technologies that can be used to modify big data in more processed and usable materials:
Prescriptive Analytics: Similar to predictive analytics, this technique maps out detailed expectations of every decision. This helps businesses consider the best plans to optimize efficiency.
Natural Language Processing: NLP is used to identify, extract, and interpret human language which can improve an algorithm’s processing capabilities.
Convolutional Neural Networks: CNNs can help analyze images and videos through image recognition and object detection.
Real-world Applications of AI in Big Data
Big Data integration with AI has helped businesses explore new opportunities across numerous fields including:
Autonomous Vehicles: Companies like Waymo, a subsidiary of Alphabet Inc. (a.k.a. Google), use big data to teach its self-driving cars how to analyze road conditions and signs. This data is also helping with infrastructure, providing cities with new information on how to optimize the flow of traffic with updated street light systems.
Travel: Businesses like Hopper are using machine learning to predict flight prices based on historical patterns and seasonal surges. Analyzing origins and destinations can help algorithms spot unique patterns in travel that would be overlooked otherwise.
Marketing: Netflix is using Deep Learning to analyze viewer trends, identifying traits from successful shows and movies to help develop more targeted content like Stranger Things which has received widespread success.
Future Trends in AI and Big Data Analytics
As AI becomes more refined, innovative techniques such as Automated Machine Learning (AutoML) can help repair the countless amounts of unstructured data being captured online that store even more unique insights on consumer habits.
Until then, developers will need to focus on expanding their efforts in Explainable AI as more user data gets processed through these machines. While insights are helpful for businesses, gathering them is not always ethical, and feeding them through stronger AI models could create unwanted privacy concerns.
