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Picking up signals

Information Management

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 otenconnected in ways that cannot be ully understood

without extensive analysis. Events that occur during the

course o business operations can be thought o as

signals that are — or should be — recorded in some way

by organizational inormation systems.

Signals come rom business or inormation domains such

as customer, nance, risk, supply chain, workorce, and

product and protability management. These domains are

interdependent, much like environmental ecosystems, and

signals oten span domains, creating a ripple eect

throughout the business environment. How eectively

companies can detect these signals and determine their

signicance to the business is a key actor in managing

business perormance.

It’s relatively easy to detect signals rom internal systems

such as transaction processing systems, ERP systems and

other back-oce operational systems. It’s also a airly

straightorward process to detect signals that are

aggregated by decision-support systems such as data

warehouses or unctional data marts. This data is typically

structured content that is collected, organized and

disseminated or analysis and decision-making on a

regular schedule.

However, the signal detection process is complicated by

the act that an ever-growing amount o data is in the

orm o unstructured content such as emails, scanned

documents, online conversations, customer interaction

logs, video and audio les, etc. This content is

unstructured because it doesn’t t into traditional

database structures that are typically used to organize

data or 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 o signal

inormation to help companies better understand,

manage and predict perormance. For example, rich

content can be mined rom social Web analytics. Social

Web analytics is the application o search, indexing,

semantic analysis and business intelligence technologies

to identiy, 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 o inormationabout market trends, perceptions and timing.

Internal systems and back oce operations provide a wealth o unused,valuable inormation

Published in Information Management Magazine, July/August 2011

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Inormation gathered by these means can be used to

analyze and quantiy each conversation’s sentiment and

infuence how it shapes — and will shape — market

trends and preerences.

Online prediction markets are another eective way to

detect signals rom business events — especially vis-a-vis

adverse events that may aect mission-critical projects.

The term “prediction markets” describes the knowledge

that is aggregated across multiple participants in a

project or business. It exemplies the theory that crowds

carry more wisdom than individuals. Prediction markets

acilitate the breakdown o social barriers inherent in

complex projects, especially when these projects span

unctional and geographic boundaries.

In prediction markets, participants share knowledge

anonymously, in real time. The ability to tap the

predictive powers o participants’ collective wisdom and

gather inormation about what’s going to happen —

both in the short term and in the long term — can be

leveraged to enhance business perormance. Forexample, i 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

oresight. As an illustration, consider the eect o trader

knowledge on stock prices. In this example, prediction

markets build on the principle that the stock market

serves to aggregate the belies o multiple traders to

generate a orecast — the stock price. For example, at

any given time, a stock price is refective o traders’collective belies about the company’s expected uture

earnings, allocated to each share o stock.

Like the stock market serves to assign a price to the

uture estimated earnings o a company, prediction

markets assign a value to collective belies about the

uture, or predictions o events to come. They can be

used as the basis or quantied scenario analysis o

possible events to support assigning values to potential

outcomes and using those values — along with other

inormation — as a oundation or decision-making.

Internal inormation systems, social Web analytics and

prediction markets are just a ew o the sources o

signals that inundate most organizations on a daily basis

These signals are oten conusing and dicult to

decipher. Making the eort to detect and put them into

some type o rame o reerence is essential. It’s not

everything, though. Signals are o no use unless they can

be eectively aggregated and analyzed to understand

and improve perormance.

My next column will continue on the topic o signal

detection with a discussion about how to apply analytics

to signal detection in order to provide deeper insight intoperormance and acilitate more sophisticated oresight

into possible uture events. Stay tuned.

Jane Grin is a Deloitte Consulting LLP partner.

Grin has designed and built business intelligence

solutions and data warehouses or clients in numerous

industries. She may be reached via e-mail at

 janegrin@deloitte.com.

As used in this document, “Deloitte” means De loitte Consulting LLP., a subsidiary o Deloitte LLP. Please see www.deloitte.com/us/about or a detailed description o

the legal structure o Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations o public accounting.

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