The explosion of data and content is not limited to social media and represents a top of mind issue for many companies.  The opportunity exists to create unprecedented business value – but there are significant hurdles like greater risk exposure, more complicated risk management, and difficulty extracting relevant insight from large volumes of data. 

As volume grows, automation is critical. For example, social media monitoring is a common practice today, one that becomes increasingly ineffective and costly as the social web expands. Monitoring tools that enable the analysis of dialog on social networks like LinkedIn, FaceBook and Twitter provide a basic level of insight. But a deeper level of insight still requires a manual process, where irrelevant content is filtered before finding meaningful insight. Information management is therefore a growing challenge.  

Although there are many aspects of an effective information management strategy, I view two as critical success factors: automation and knowledge. Manual efforts won’t scale, as we’ve only scratched the information explosion surface. An instrumented and interconnected world creates significant volumes of data from various sources – some of which we never envisioned. As we navigate this world, analytic automation has to go beyond scheduled reports and alerts to the automated extraction of insight based on knowledge. This knowledge comes from people, data and content. I believe that capturing this knowledge and automating its application is the fastest path to actionable insight. Don’t get me wrong, people will always be part of the analytical equation. But people focused on using their instincts and experience to act on presented insight is far better than people focused on finding insight through manual review. 

This application of knowledge is part of a comprehensive strategy that utilizes technology to address the critical steps in the information management process. The first step, finding and collecting all relevant data and content, is enabled by more intelligence in the search process. The objective is to reduce the noise that slows the analysis process and eliminate content that provides no value. Software that finds contextual meaning in content, determines the relevance of that content. This effectively automates the first step in the information management process. 

In the analysis step, software enables the capture of knowledge from people, data, and content and applies that knowledge to large volumes of data. Detecting fraud in the Insurance Industry is a good example. A claim examiner with years of experience knows what suspicion looks like. Medical reports, email, police reports, adjuster notes, and traditional data like name and phone number are manually reviewed. Software that mines data and content to identify the characteristics of fraud is sometimes leveraged to find suspicion in open claims. By leveraging these sources of knowledge in an automated fashion, suspicious claims are automatically flagged and resources can focus on investigation, not manual review. 

Understanding presented insight and taking appropriate action represents the final step in the information management process. Interpreting and digesting results from the analysis of large volumes of data can be difficult. Visualization techniques are leveraged to accelerate time to insight. Alerting, workflow and business process management capabilities are used to enable action. 

It’s clear that companies face many information management challenges across multiple departments, and use one-off approaches to address specific pain points. Whether it’s monitoring social media to stay ahead of brand and reputation risk, or automating the analysis of customer survey verbatims, or tracking what employees are saying in various forums, companies pursue multiple solutions in isolation. But from my perspective, information management challenges have to be viewed holistically to create real business value. 

What we need is a holistic listening platform that extracts insight from a collected set of relevant data. The platform addresses both the risk and opportunity aspects of the information explosion. Each business need is addressed by the application of captured knowledge to its relevant set of data. When viewed holistically, data, content and information management capabilities are leveraged cost effectively and productively across multiple business initiatives.                         

I truly embrace the notion that we have access to a new kind of intelligence fueled by growing instrumentation and interconnectedness.  The intelligent enterprise will have advanced analytics as a core competency. They will automate advanced analytic processes wherever possible and capture and apply knowledge to solve business problems. They will be flexible in their approach and embrace the opportunities presented by this new kind of intelligence.