07-09-2014, 10:29 PM
At the present time, the way in which we manage data depends on its structural features. In this report we propose a logical model which represents a step further in the process of bridging the gap between different data modeling approaches. In particular, the focus is on Unstructured and semistructured data warehouse . Today, 80% of business is carried out on unstructured data documents, call center logs, blogs, wikis, tweets, and surveys. Neglecting to analyze such data leads to ignored risks, uninformed decisions, and missed opportunities. Financial services firms are increasingly analyzing unstructured data to understand customer needs, prevent frauds and expand the customer base. Analytics plays a key role in analyzing unstructured data and transforming it into actionable intelligence. The rapid adoption of social media by the financial services industry has resulted in an even higher percentage of unstructured data being generated. This has prompted firms to increasingly look at social analytics to derive structured insights out of social media. As unstructured and structured data analytics are converging, financial institutions are looking for analytic vendors to come up with products that blend unstructured analytics with structured analytics. This article analyzes unstructured data, the various analytics vendors in the space, and applications in the financial services industry.