Big data is essential because its analysis unlocks information and insights that go beyond human perception and the capabilities of traditional database analysis.
For example, a person may look at a thermometer and decide whether to wear a hat. Ten years’ worth of daily temperatures can be found in a database. The data can then be used to compare hat sales with temperature data and project how many hats a retailer should order in October rather than November.
To determine which hats are trending, big data analytics can analyze selfies posted on social media and describe the types, materials, and styles of hats. It can also analyze global weather patterns and predict the likelihood of snowfall.
Benefits of using Big Data
As we mentioned in the previous point, Big Data is enormously beneficial if used correctly. Organizations can take advantage of all the information provided to them to improve decision-making, be more efficient, optimize costs, segment customers, and obtain new sources of income. Besides:
- It increases productivity and efficiency since the tools process data faster and make their work more accessible for employees.
- It gives you the opportunity to improve decision-making, as it provides us with a more informed and reliable basis.
- Reduces costs since increased productivity can lead to significant cost savings and positively impact profitability.
- It facilitates the detection of fraud and anomalies, as they detect erroneous transactions or problems in activities.
- Greater agility and speed to market.
- Improves customer service and user experience since we have more information about what they like and what they don’t.
Big Data Use Cases
Fraud Detection
Banks, credit card companies, retailers, payment processors, regulators, and others use big data analytics in real-time transaction data analysis to search for signs of fraudulent activity. Machine learning algorithms can detect suspicious patterns, lock down user accounts, and notify users about potential account breaches.
Predictive Analytics
Almost any machine, including a jet engine, factory machinery, or an automobile, can be monitored by video cameras, microphones, and other sensors, which can record information about the machine’s movement, performance, and surroundings. This unstructured data can be utilized in conjunction with AI and machine learning to recognize wear indicators early on, find problems before equipment breaks down, and, in the case of automotive safety systems, take appropriate action. Active to prevent accidents.
Spatial analysis and public safety
Machine learning is used in large public environments, such as shopping malls, stadiums, and transit facilities, to extract real-time information from security videos. These extensive data analytics systems use computer vision AI to analyze pedestrian traffic, identify traffic jams, and detect dangerous situations. The resulting information can be used to understand retailer performance, shift staff to attend high-demand areas, or alert emergency first responders if public safety is threatened. The Chicago Transit Authority uses big data and machine learning to make the public transportation experience faster, smoother, and safer.
Network Performance
The performance of telecommunications, wireless, and computing networks is an ideal use case for ample data. Every packet that travels through the network generates performance data in real time that automated systems can examine. These systems can maximize performance and speed up extra network resources. Over time, network builders can use significant data insights to prioritize investments and identify new infrastructure needs.
Feeling and Consciousness
To track trends, hot topics, and public opinion, marketers and pollsters use big data analytics to track publicly accessible online posts on social media, forums, and reviews. Of course, social media companies use even more sophisticated, extensive data analysis to produce more accurate demographic and sentiment insights.