In previous entries excerpted from Chapter 2: Business Process as an Enabler of Innovation, CK Prahalad and MS Krishnan have 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 effectively. By definition, in a rapidly changing competitive environment, business processes cannot be static. The dynamics 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 develop 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 longterm success of Visa and MasterCard.

Spotting new trends requires comprehension of consumer expectations and behaviors and technological changes, as well as the nature of the supply chain and opportunities for its improvement….

Spotting new trends requires comprehension of consumer 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 a firm develop tools that aid in building insights? The new competitive 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 recognizing, 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 information and extract valuable insights. Real-time analytics seize the opportunities and mitigate the risks in seeking to have global resources serving single customers.

The new competitive landscape requires continuous analysis of data for insight. While traditional analytical approaches are often asynchronous with business changes, real-time analytics seize the opportunities and mitigate the risks in seeking to have global resources serving single customers.

The terms analytics and analytical models are used 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 advertising 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 managers derive insights, often combining structured transaction data (numbers) and unstructured data as in documents, images, and video. Digitization of business processes, the Internet, and evolving ICT architecture enable real-time predictive modeling. These capabilities are at the heart of effective management in an N = 1 and R = G world In the subsequent entries, this will be demonstrated).

The link between data, analytics, and insights is shown in the figure above. 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 of 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. (The quality of data is a major concern in many firms. Data collection 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 detailed technical discussion on how to “clean up databases,” therefore, it is assumed that the data quality is acceptable to perform analytics.)

by C.K. Prahalad and M.S. Krishnan, Via The New Age of Innovation: Driving Cocreated Value Through Global Networks (2008)

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