With the gradual development of big data and more and more data, data analysis has become particularly important. For enterprises, big data analysis can help them grasp customer information and further promote transactions. So, what are the types of big data analysis data?
To understand what data to analyze, there are four main categories of data types to be analyzed in big data:
TRANSACTION DATA
Big data platforms can acquire structured transaction data with larger time spans and larger amounts, so that a wider range of transaction data types can be analyzed, including not only POS or e-commerce shopping data, but also behavioral transaction data, such as web servers Log of recorded Internet clickstream data.
HUMAN-GENERATED DATA
Unstructured data is widely found in emails, documents, images, audio, and video, as well as data streams generated through blogs, wikis, and especially social media. This data provides a rich source of data for analysis using text analytics capabilities.
MOBILE DATA
Smartphones and tablets with Internet access are increasingly common. Apps on these mobile devices are capable of tracking and communicating countless events, from in-app transaction data (such as recording a search for a product) to profile or status reporting events (such as a location change reporting a new geocode).
MACHINE AND SENSOR DATA
This includes data created or generated by functional devices such as smart meters, smart temperature controllers, factory machines and internet-connected home appliances. These devices can be configured to communicate with other nodes in the internetwork and can also automatically transmit data to a central server so the data can be analyzed. Machine and sensor data are prime examples arising from the emerging Internet of Things (IoT). Data from the IoT can be used to build analytical models, continuously monitor predictive behavior (such as identifying when sensor values indicate a problem), and provide prescribed instructions (such as alerting technicians to inspect equipment before an actual problem occurs).
The Internet itself has digital and interactive characteristics, which have brought revolutionary breakthroughs to data collection, organization, and research. In the past, data analysts in the "atomic world" had to spend higher costs (funds, resources and time) to obtain data to support research and analysis. The richness, comprehensiveness, continuity and timeliness of data were much worse than in the Internet era.