AI and Big Data are two of the most popular and useful technologies of this era. Artificial Intelligence has been there for more than a decade, while Big Data came into existence just a few years ago. Computers can be used to store millions of records and data, but the power to analyze this data is provided by Big Data.
AI’s ability to work well with data analytics is the primary reason why AI and Big Data are now seemingly inseparable.
We can say that together Big Data and AI are a set of two amazing modern technologies that empower machine learning, continuously reiterate and update the data banks, and take the help of human intervention and recursive experiments for the same. Today, we have brought this blog to provide informational usage of AI and Big Data together to resolve all possible issues related to the data.
Machine learning is considered as an advanced version of AI through which various machines can send or receive data and learn new concepts by analyzing the data.
Every corporate system and every business department has piles of data that have been gathered but that people know nothing about. By using machine learning and combining its power with algorithms that address how to manage and categorize different types of emails, documents, images, etc., stored on servers, AI can go to work on this unplumbed data and pre-sort it for you.
AI can objectively recognize data that is rarely or never used and recommend that you throw it away, but it doesn’t have the same refinement abilities that employees do. This saves an employee time hunting down this potentially outdated or used data because now all they need to do is to determine whether there is any reason to keep it.
AI Technologies that Are Being Used With Big Data
Various AI technologies that are used with Big Data are:
- Anomaly Detection
For any dataset, if an anomaly is not detected then Big Data analytics can be used. Here fault detection, sensor network, ecosystem distribution system health can be detected with big data technologies.
- Bayes Theorem
Bayes theorem is used to identify the probability of an event based on the pre-known conditions. Even the future of any event can also be predicted based on the previous event. For Big Data analysis this theorem is of best use and can provide a likelihood of any customer interest in the product by using the past or historical data pattern.
- Pattern Recognition
Pattern recognition is a technique of machine learning and is used to identify the patterns in a certain amount of data. With the help of training data, the patterns can be identified and are known as supervised learning.
- Graph Theory
Graph theory is based on graph study that uses various vertices and edges. The data pattern and relationship can be identified through node relationships. This pattern can be useful and help big data analysts in pattern identification. This study can be important and useful for any business.
AI and Big Data use many methods and techniques, but they can be used in an integrated manner and provide a result to be used by the organizations to analyze customer interests and offer them the best-optimized services. The technologies can be blended to provide a seamless experience to customers.
Frequently Asked Questions
- Choose a partner that cares about its clients.
- Never compromise on technology experience and domain expertise.
- Check out your development partners’ portfolios, customer testimonials, and references.
- Observe how they approach communication and how much they pay attention to your vision.
- Ask the right questions to help you choose easily.
- The average outsourcing charges in India are $18 – $40, which is way more affordable than in developed countries like the USA, $38 – $63.
- India has a large pool of native-English speakers who’re highly proficient in their work.
- With an Indian outsourcing partner, you can access 24×7 support and specialized IT talent.