Unstructured Content and Text Analytics
For most organization’s unstructured content is abundant and overflowing but not exploited to derive the most value to improve decision-making, drive profitability, and competitive advantages.
Under the Big Data umbrella, falls structured data, semi-structured, or unstructured data. Unfortunately, unstructured content does not fit neatly into database analytics and must be solved with proven technologies that can extract concepts from content and classify to taxonomies to gain business value from content.
Only when unstructured content is proactively managed can insights be attained to achieve business advantages. Organizations can then find the descriptive needles in the haystack to increase business agility.
The business imperatives driving this issue:
- Unstructured content is surpassing relational data and must be proactively managed
- Only evaluating structured data does not provide the nuances, sentiment, or knowledge found within unstructured and semi-structured content
- Lack of effective decision making as all pertinent information can’t be found or extracted
- Inability to respond more quickly to market changes
Concept Searching product platforms analyze and extract highly correlated concepts from very large document collections. Before analysis, the products can be used to narrow the data set to include highly granular information, remove irrelevant content, and noise.
After creating a manageable data set, organizations can attain an ecosystem of semantics that delivers understandable results. The valuable insight gained can be used to identify competitive advantages, customer perception, regional trends, and, perhaps more importantly, identify internal knowledge capital that exists but is rarely used because it cannot be found.
For more information about this topic, please read our ‘ Big Data Solution Overview‘.