Still, even with improving connections between BI and unstructured data stores, the challenge with today’s business intelligence deployments is that they only enable quantitative analysis of a fraction of an enterprises’ information assets. That’s because the majority of information available to an enterprise is unstructured content held in documents, e-mail messages, collaboration forums, and on the Web. Enterprises now realize that to have a complete, 360-degree view of their operations, they need to analyze that unstructured data. That analysis involves both qualitative assessments as well as quantitative analytics. The challenge of BI isn’t storing the unstructured data; it is the significant back-end development work needed to gather and quantify unstructured information sources.
Missing from an enterprise’s portfolio of BI tools are search and semantic processing technology, which can efficiently process unstructured data into gists and metrics, plus handle large volumes of data from widely dispersed sources.
No further than yesterday, I was writing on two separate posts that:
- the value of BigData resides both in its volume and the possibilities to enhance it with metadata and link it with other data sets
- bringing together both structure and unstructure data is the future
Original title and link: Business Intelligence for Big Data: What Is Missing? ( ©myNoSQL)