An adage says that nothing happens in a vacuum, and this is especially true when it comes to the complex environment that most businesses operate in. Virtually no event is random. Seemingly unrelated events are often connected in ways that cannot be fully understood without extensive analysis. Events that occur during the course of business operations can be thought of as signals that are – or should be – recorded in some way by organizational information systems.

Signals come from business or information domains such as customer, finance, risk, supply chain, workforce, and product and profitability management. These domains are interdependent, much like environmental ecosystems, and signals often span domains, creating a ripple effect throughout the business environment. How effectively companies can detect these signals and determine their significance to the business is a key factor in managing business performance.

It's relatively easy to detect signals from internal systems such as transaction processing systems, ERP systems and other back-office operational systems. It's also a fairly straightforward process to detect signals that are aggregated by decision-support systems such as data warehouses or functional data marts. This data is typically structured content that is collected, organized and disseminated for analysis and decision-making on a regular schedule.

However, the signal detection process is complicated by the fact that an ever-growing amount of data is in the form of unstructured content such as emails, scanned documents, online conversations, customer interaction logs, video and audio files, etc. This content is unstructured because it doesn't fit into traditional database structures that are typically used to organize data for analysis and reporting. To say unstructured content is nontraditional does not imply that it has no value – quite the opposite.

Unstructured content can provide a wealth of signal information to help companies better understand, manage and predict performance. For example, rich content can be mined from social Web analytics. Social Web analytics is the application of search, indexing, semantic analysis and business intelligence technologies to identify, track, listen to and participate in distributed conversations about a particular brand, product or issue.

These distributed conversations can exist in traditional media, social media, advertising and customer interactions. They can be a valuable source of information about market trends, perceptions and timing.

Information gathered by these means can be used to analyze and quantify each conversation's sentiment and influence how it shapes – and will shape – market trends and preferences.

Online prediction markets are another effective way to detect signals from business events – especially vis-a-vis adverse events that may affect mission-critical projects. The term "prediction markets" describes the knowledge that is aggregated across multiple participants in a project or business. It exemplifies the theory that crowds carry more wisdom than individuals. Prediction markets facilitate the breakdown of social barriers inherent in complex projects, especially when these projects span functional and geographic boundaries.

In prediction markets, participants share knowledge anonymously, in real time. The ability to tap the predictive powers of participants' collective wisdom and gather information about what's going to happen – both in the short term and in the long term – can be leveraged to enhance business performance. For example, if project leaders receive early indicators that timelines have slipped, they can make immediate and proactive decisions, thereby reducing risks, shortening delays and saving costs long term.

Companies can also tap prediction markets to gain foresight. As an illustration, consider the effect of trader knowledge on stock prices. In this example, prediction markets build on the principle that the stock market serves to aggregate the beliefs of multiple traders to generate a forecast - the stock price. For example, at any given time, a stock price is reflective of traders' collective beliefs about the company's expected future earnings, allocated to each share of stock.

Like the stock market serves to assign a price to the future estimated earnings of a company, prediction markets assign a value to collective beliefs about the future, or predictions of events to come. They can be used as the basis for quantified scenario analysis of possible events to support assigning values to potential outcomes and using those values – along with other information – as a foundation for decision-making.

Internal information systems, social Web analytics and prediction markets are just a few of the sources of signals that inundate most organizations on a daily basis. These signals are often confusing and difficult to decipher. Making the effort to detect and put them into some type of frame of reference is essential. It's not everything, though. Signals are of no use unless they can be effectively aggregated and analyzed to understand and improve performance.

My next column will continue on the topic of signal detection with a discussion about how to apply analytics to signal detection in order to provide deeper insight into performance and facilitate more sophisticated foresight into possible future events. Stay tuned.

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