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fectively. By definition, in a rapidly changing competitive
environment, business processes cannot be static. The dy-
namics of an industry dictate the rate of change in business
models and strategy. Business processes must keep pace
with this rate of change in the strategy of the firm. More
important, business process capability may suggest new
ways of competing.
Competitiveness favors those who spot new trends and
act on them expeditiously. Therefore, managers must de-
velop insights about new opportunities by amplifying weak
signals. These weak signals emerge from insights derived
through a deep understanding and interpretation of a wide
variety of information. For example, recognizing that SMS
(text) messaging using a cell phone will be an important
method for settling small payments is critical for the long-
term success of Visa and MasterCard.
Spotting new trends requires comprehension of con-
sumer expectations and behaviors and technological changes,
as well as the nature of the supply chain and opportunities
for its improvement. How does one spot trends early? Can
n the last chapter, we identified business processes as the
enabler of an innovative culture through their impact
on both social and technical architecture. As a critical
intermediate step between strategy and operations, the
quality of business processes (granularity, flexibility, and
clarity) determines the capability of firms to compete ef-
a firm develop tools that aid in building insights? The new com-
petitive landscape requires continuous analysis of data for insight.
Analysis that is only episodic and ad hoc (as when a senior manager
commissions a specific study, say, to assess the impact of oil prices
on shopping patterns) or periodic (such as actual sales compared
to forecasts) will not suffice. Traditional analytical approaches are
often asynchronous with business changes. Hence, delays in rec-
ognizing, interpreting, and acting on the trends are emerging as
critical impediments to competitiveness.
Every firm accumulates a voluminous amount of transaction
data (for example, sales transactions) and equally large volumes of
unstructured data (for example, video clips and advertisements).
Managers need a mechanism to understand the accumulated in-
formation and extract valuable insights. Real-time analytics seize
the opportunities and mitigate the risks in seeking to have global
resources serving single customers.
We use the terms analytics and analytical models to describe
a class of mathematical applications that permits businesses to
crunch everything from picking stocks in trading rooms rapidly (in
less than a millionth of a second) to identifying specific advertis-
ing messages based on your search at any time in Google. Some
recent trends are helping firms build this capacity. Algorithms and
quantitative methods used in analytics are evolving to help man-
agers derive insights, often combining structured transaction data
(numbers) and unstructured data as in documents, images, and
video. Digitization of business processes, the Internet, and evolv-
ing ICT architecture enable real-time predictive modeling. These
capabilities, as we will demonstrate in this chapter, are at the heart
of effective management in an N = 1 and R = G world.
The link between data, analytics, and insights is shown in Fig-
ure 3.1. As you can see, the quality of insight depends on both the
quality of data and the quality of analytics. Models that are not
built specifically to inform on strategic priorities are of little value
to line managers. More important, insights that are not available
when decisions have to be made are of little value. In this chapter,
we will assume the availability of high-quality data that capture the
millions of transactions in a company—be they sales, warranty
claims, orders placed, or payments to suppliers. (We recognize that
the quality of data is a major concern in many firms. Data collec-
tion often is not standardized across the firm. Increasingly, data are
also collected in a highly decentralized fashion, for example, by
delivery agents with handheld devices. Rather than engage in a de-
tailed technical discussion on how to “clean up databases,” in this
chapter we will assume that the data quality is acceptable to per-
form analytics.) We will explore a range of analytics, with exam-
ples, that can help you recognize the usefulness of these tools in
migrating to an N = 1 and R = G world of innovation.
Traditionally, managers depended on experience and intuition to
develop insights—“gut feel,” if you will. Most often a gut feel is
based on past experiences. Gut feel and intuition are important,
Rich Transaction Data
Consistent, transparent
Unstructured Data
Weak signals
Analytics Engine
Focus on strategic
priorities of N = 1, R = G
Actionable Insights
Focus on cocreation
Business Insights

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