The universe of digital data is expanding rapidly, creating more information by the minute about online users from a variety of courses, including social media, mobile devices, online transactions, etc. This vast new body of information, known collectively as big data, contains important insights into users’ thinking and behavior patterns. New advanced analytical tools can help businesses and government agencies to access this information and use it to make informed decisions. While many people may not be aware what is name matching or text mining, these processes are central to the useful application of big data.
Why analyze big data?
The size of digital big data is increasing rapidly. The International Digital Corporation or IDC estimates that about 1.7 mb of data is created for every individual, every second. The size of the digital universe is expected to reach 40 Zb by the year 2020. However, only a small percentage of this data is ever analyzed for the useful information it contains.
In fact, the IDC estimates that only about 1% if big data is ever analyzed. One of the reasons for this is that traditional analytical tools like databases cannot be used for big data. This is because big data exists in multiple formats, such as unstructured, semi-structured and unstructured text, in different sizes. The high volume and high velocity of Big Data also put it beyond the capacities of traditional analytical tools.
Tools for big data analysis
New analytical tools and software can handle high volumes of data in diverse formats. Entity extraction and data matching software can search large data sets to resolve people, names, places, events etc. The usefulness of these techniques is attested by the fact that the text analytics market is already worth $3 billion and is projected to double by 2020 to $6 billion.
Text mining processes include information retrieval, natural language processing, information extraction and data mining. It can be used by businesses to gain insights from information drawn from a wide range of sources and datasets. It can help them in risk detection, fraud, and compliance. And by using natural language processing of large sets of unstructured data, it offers greater customer involvement, helping businesss to understand customer thinking and behavior patterns.
What is name matching?
Entity resolution tasks are deduplication, record linkage, and canonicalization. These find, resolve and link mentions of the same entity across different and even inconsistent data sets. The entities resolved could be people, places, events, organizations, or dates.
This can be used for meeting compliance screening of customers and employees by banks and financial institutions. Name matching can also be used in anti-fraud, government intelligence, law enforcement, and identity verification.
Big data analysis tools help businesses and government agencies to extract useful information from large and varied data sets. As more organizations learn about what is name matching and its applications for their own operations, their commitment to text analytics is likely to increase.