Data Science (AI/ML), quite literally, is projected to be the next ‘big’ thing.
We’re all about Data Science (AI/ML) right now. Data Science (AI/ML) is the ocean of data now available to businesses via the social web, machine data and transactional data.
Question
Creative
Optimizing Spend
Simple
The Data Science (AI/ML) varies in terms of volume
Big’ misconception: Big data is not for the small guy
According to a BMO Capital Markets report, $50 Billion is what marketers are spending on Big Data and advanced analytics in the hopes of improving marketing’s impact on the business.
Core Business Areas
With the help of Data Science (AI/ML), businesses strive to offer improved customer services, which can help boost profit. Enhanced customer experience is the principal goal of most companies.
Banking
Banks and retail tradesmen use big data for sentiment analysis and high-frequency trading, among others. The area also relies on big data for risk analytics and monitoring financial business activity
Insurance
The BFSI area largely implements big data and analytics to become more effective, customer-centric, and, therefore, more effective.
Financial Services
Financial companies use big data analytics to reduce overlapping, redundant operations as well as providing tools for easier access to data.
Advertising
Use of big data helps in reducing fraud and allows the timely analysis of the record.
Marketing
marketing industry Use of big data helps in reducing fraud and allows the timely analysis of the record.
Retail
The retail industry gather a large amount of data through RFID, POS scanners, customer loyalty programs, and so on.
Features of Data Science (AI/ML) Required for Business Data Collection
Collecting user data is the first step in the development of Data Science (AI/ML) systems. There are many ways to collect user data, and most of them are performed without specific participation, or even experience, of users. While customers may carefully provide some personal data via surveys or feedback forms
Data Visualization:
Visualization may be utilized for both classified data collected in the database. And the analysis results obtained using this information as a source.
Data Storage:
Big Data solutions have greatly high demands for information storage capabilities as they have to store both collected raw data and processed data
Data Analysis:
In order to make use of gathered data, it must be subjected to comprehensive processing that utilizes important computational resources.
