Text Analysis for Unstructured Data
It matters how structure is added to unstructured content.
- Leading analysts suggest that more than 80% of data is unstructured in nature.
- Yet structure is necessary to analysis of any kind.
- Gaining insight from unstructured content starts with adding structure, and it matters a lot how you go about this task.
STRUCTURING THE UNSTRUCTURED THROUGH TEXT ANALYTICS
Text analysis is an inexact science:
IBM cites "Veracity" as one of the "Four V's of Big Data." Veracity refers to uncertainty and unknown quality within unstructured content. However, unless users of analytical systems have confidence in the underlying data, they will be unable to use t any insights that they gain in a meaningful way.
A focus on data quality is obviously important, but so too is transparency. When an analysis leads to new ideas, users like to be able to check back to source content, and reassure themselves of the validity of their findings. Where the analysis is based on structured, machine-generated data, veracity is not an issue. But where unstructured content is involved, this ability to easily check back to original source examples is necessary to support actionable insight.
SEARCH COMPLEMENTS BIG DATA
At Search Technologies, we believe in creating systems that provide not just insightful analysis capabilities, but an environment in which source data is easily available through search (taking into account any necessary security restrictions), so that decisions can be made in confidence.
Analytical solutions are created by combining appropriate technologies with proven processes, and expertise, all of which are available through our search and big data consulting services.
Contact us for an informal discussion of your requirements for exploiting text analytics.