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 but in a fast-changing competitive environment, experience of the past is less and less valuable. Foresight, not hindsight, is of value.

Foresight is a result of understanding, through structured and unstructured data, the unfolding of competitive dynamics. There is value in identifying new patterns of relationships, predicting the
behavior and evolution of systems, and mitigating risk. In an N = 1 world, the behavior of individual consumers as well as broad patterns of change must be understood. In R = G, the capabilities of each vendor in the ecosystem in terms of costs, time, and quality levels must be understood and matched with the specific demands of a single consumer at a point in time. Furthermore, given the complexity of the entire ecosystem, the impact of change in any single variable, such as order entry, will have a ripple effect on other related subsystems such as inventory, spare parts, and manufacturing lead times.

In a fast-changing competitive environment, experience of the past is less and less valuable while foresight, not hindsight, is of value.Foresight is a result of understanding, through structured and unstructured data, the unfolding of competitive dynamics

A “small change” in order entry could trigger multiple changes in the totality of business processes. Managing the systemwide impacts of changes cannot be left to the gut feeling of managers. However, individual managers can, based on their experiences, interpret the signals differently (especially in a rapidly evolving system). Hence, foresight based on the real-time analyses of both structured and unstructured data is indispensable. Intuition and gut feeling are still useful, but not as a substitute for analytics.

Keeping business processes current and compliant with all changes and at the same time gleaning insights about the evolving behavior of consumers and the supply network require a commitment to analytics. Consider, for example, the Indian IRS. It is known that not everyone in India pays his or her taxes adequately. The IRS can safely start with the assumption that there is significant tax evasion in the country. In order to deal with widespread tax evasion, India’s tax agency is building a database of declared income and consumption patterns, such as travel, purchases of bigticket items such as cars, plasma TVs, deposits and withdrawals from banks, stock market activity, and the like, to spot patterns of tax evasion. The focus is on identifying individual taxpayers (N = 1) for further investigation. This project calls for deriving insights based on data from multiple sources.

In an N = 1 world, the behavior of individual consumers as well as broad patterns of change must be understood. In R = G, the capabilities of each vendor in the ecosystem in terms of costs, time, and quality levels must be understood and matched with the specific demands of a single consumer at a point in time. Furthermore, the impact of change in any single variable will have a ripple effect on other related subsystems such as inventory, spare parts, and manufacturing lead times.

A similar initiative is in place at the IRS in the United States as well. In the United States, the cost of tax avoidance is estimated at $350 billion. Tax evasion, around the world, is a moving target. In order to predict these behavior patterns, complex analytic models have to be developed. Data from a wide variety of sources must be pulled together to see the emerging patterns. Microsoft recently announced its purchase of a small start-up health search engine called Medstory, Inc., that applies advanced analytics to structured and unstructured medical and health information in journals, government documents, and the Internet to present an enhanced customer experience in access to health information. The desired result is a personalized information search based on one customer’s family history, prior medication, age, and gender.

Analytics must be driven by strategy. For example, in order to price health insurance for each diabetic consumer (patient), we need analytics, which in real time monitors behaviors (compliance on predetermined routines) but can also forecast likely behaviors. Analytics can also show where to allocate resources and how to optimize the “resource network.” Should a call from an irate and important customer in New Zealand be routed to India or Australia? This is a real-time decision, one of thousands, to which the firm must respond creatively. Insights also result from consumer concerns and comments. Understanding and researching blogs and chat rooms is another important source of insights. The capability to use analytical modeling tools is critical in every aspect of value creation, from understanding customer preferences and behaviors to supply chain management, global resource reconfiguration, skill management, and risk mitigation. We (i.e. CK Prahalad and MS Krishnan) will illustrate the power of analytical tools with applications focused on leveraging global resources to serve individual customers in global markets first (R = G), followed by illustration of such tools in moving toward N = 1.

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

 Print This Post