Text Analytics for Unstructured Data
It matters how structure is added to unstructured content.
THE GROWTH OF UNSTRUCTURED, BUSINESS-CRITICAL DATA
- Leading analysts suggest that more than 80% of data is unstructured in nature (consider social media, emails, phone conversations, support queries, customer reviews, etc.)
- 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.
UNSTRUCTURED TEXT ANALYTICS USE CASE EXAMPLES
Using unstructured text analytics tools, techniques, and approaches, we've helped customers improve operations through multiple use cases, such as:
- Extracting fielded data and relationships – analyzing doctor’s patient notes, legal contracts, vendor agreements, etc.
- Enhancing semantic search – connecting employees to business data, charts, information, and resources
- Chatbots and customer support – answering questions, directing users to manuals or products, and automatic answering / rerouting of requests
- E-commerce – improving sentiment analysis, search relevancy, targeted responses, and personalized results based on query intent
STRUCTURING THE UNSTRUCTURED THROUGH TEXT ANALYTICS
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 any insights that they gain in a meaningful way.
We bring our expertise in text analytics to help you with a full range of tasks required to extract insights from unstructured and semi-structured data, including:
- Data acquisition
- Data cleansing, formatting, and enrichment
- Text extraction, text mining, and natural language processing (NLP)
- End-user application development
- Data quality analysis
A focus on data quality and transparency is key. 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.
TECHNOLOGY ASSETS TO SUPPORT TEXT ANALYTICS FROM UNSTRUCTURED DATA
Developed through hundreds of client projects, our technology assets can help organizations acquire and search across unstructured data in the most efficient and impactful way.
- Aspire content processing for semi-structured and unstructured data
- Saga Natural Language Understanding framework for scalable NLP/NLU applications
- Connectors for a range of unstructured and structured content sources
- Query processing language for advanced query processing
These technology assets are search engine agnostic and work with a wide range of open source and commercial platforms, including Elasticsearch, Solr, SharePoint, Azure Search, etc.
UNSTRUCTURED DATA SEARCH COMPLEMENTS BIG DATA ANALYTICS
At Search Technologies, we believe in creating text analytics 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.