Converging Text and BI: The Case for a Content Mining Platform

Converging Text and BI:
The Case for a Content Mining Platform Although enterprises commonly utilize business intelligence (BI) tools against structured data for analysis and decision making, leading organizations recognize that they must take a more holistic view of their information assets and find ways to creatively analyze the exponentially growing universe of unstructured content - contracts, press releases, filings, forms, call center notes, medical records, insurance claims, web content, emails, etc. This white paper describes how the Clarabridge Content Mining PlatformT avoids the pitfalls of previous approaches to unstructured analysis, and capitalizes on lessons learned from solving similar problems in the structured domain. A platform approach enables enterprises to efficiently and effectively source, transform, store, and analyze unstructured data alongside structured data - in a way that is easy to manage. The result is broader business understanding, the ability to leverage existing resources, and the freedom to rapidly apply the most appropriate decision support interface. WHITE PAPERMarch 6, 2006 Converging Text and BI: the Case for an Unstructured Intelligence Platform
Executive Summary It is becoming ever more important for today's agile enterprise to use the best available data to drive strategic and operational business decisions. Although most companies deploy business intelligence (BI) tools against structured data to answer a wide variety of questions, leading organizations are increasingly recognizing that they must take a more holistic view of their information assets. They find creative ways to analyze the exponentially growing universe of unstructured content - contracts, press releases, research papers, filings, call center notes, medical records, insurance claims, web content, emails, etc. This content when understood and analyzed alongside structured data provides business insight that enables organizations to better serve customers, control cost and risk, compete effectively, and drive profitability. Text processing technologies are rapidly maturing to enable concept/entity extraction, relationship Unstructured Data Search BI, OLAP, Reporting Structured Datatagging, and other paradigms to allow more structure to be applied to unstructured data. Knowledge WorkerSearch technologies are evolving to provide end Currently end users have separate interfaces for Structured and users with better ways to retrieve text, but provide Unstructured data: Search for Unstructured, and BI for Structured .limited to no analytic insight, which makes the determination of precise answers to questions . How can we improve satisfaction? .time consuming, tedious, and increasingly more Call center notes Customer demographicsdifficult. Even more advanced implementations of What is root cause of problem?text processing technologies require complex .Warranty repair notes .Service ticket & outcomeprogramming work and are, like search engine technologies, totally disconnected from time- .Clinical Notes How do symptoms change over time? .Patient recordstested analysis approaches used in the BI world.
So how can enterprises better enable users to Figure 1 - Currently structured and unstructured analysis are done in spend their days making informed decisions different ways and with different tools. versus gathering data? Fortunately, there is much to be learned from two decades of struggling with similar problems in the structured data world. We now know as needs change and evolve, organizations will require the flexibility to integrate the most appropriate text processing technologies to extract desired information. They must enable users to apply time-tested analytical approaches that can be modified or expanded upon as understanding of issues and opportunities emerges from the data itself. For example, a call center should be able to apply a multi-dimensional analysis (i.e., "slice and dice") to call center logs and email text for assessing trends, root causes, and relationships between issues, people, time to resolution, etc. Organizations should have the infrastructure, storage, and user interfaces to process and efficiently explore large volumes of data. And they need to easily leverage their existing BI and data warehousing (DW) tools presently used only for structured data analyses, to analyze unstructured data alongside structured data. As organizations adopt ana... [download for more]