Businesspeople everywhere grasp that something sudden and dramatic is happening. We are undergoing a deeply disorienting re-acceleration of technological change despite the global recession and to cope with this degree of fluidity and uncertainty, the strategist needs to return to first principles. We cannot assume that traditional bases of competitive advantage will last. We cannot presume that hard-earned “excellence,” built within the current business model, is the right skill-set for the future. We do not know who our future competitors will be. Indeed the boundaries of the business and the industry cannot be taken for granted. We need to step back and rethink the connection between technology and business strategy.
Two large phenomena, both driven by information technology, are reshaping internal organization, business strategy and the structures of industries. The first is deconstruction of value chains: the break-up of vertically integrated businesses, as standards and interoperability replace managed interfaces. The second is polarization of the economies of mass, meaning that in some activities, economies of scale and experience are evaporating, while in others they are intensifying. “Negative” polarization, where economies of scale and experience have weakened, leads to the fragmentation of activities. “Positive” polarization leads to the concentration of activities. The combined consequence of these trends is to substitute “horizontal” organization for “vertical,” both within the corporation and across industries. The transposition of the industrial matrix.
This does not render the traditional corporation obsolete, but it does often mean that corporations need to redefine their role and reshape their business definitions. They need to establish collaborative relationships with communities, especially user communities, where individuals or small proprietorships are more flexible, better informed about end-use, or can innovate more cheaply. Conversely, they need to establish collaborative relations with other institutions, perhaps competitors, to achieve economies of scale and experience that would otherwise be inaccessible. On both sides, strategy becomes a matter of collaboration as well as competition.
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Internally corporations need to do much the same thing. Innovation and small-scale experimentation are best done in loose groups where individuals and small teams enjoy a high measure of autonomy. On the other hand, scale- and experience- sensitive functions need to be centralized across businesses, driving the overall organization to a more functional structure. The internal architecture of the corporation becomes a set of platforms, each supporting activities at smaller scale and with faster cycle times. One platform can be stacked on top of another. And the architecture of an “industry” can be exactly the same, some companies serving as platforms for others, some serving as platforms for end-user communities. The pattern is fractal.
These trends are quite general, and account for numerous industry disruptions. But they apply in particular to Big Data. “Big Data” means much more than vastly larger data sets and exotic software. It requires treating data as infrastructure: centralized, secure, massively scaled, built as a general resource not for any specific end-use. It also requires treating the processes of inference as “super-structure”: iterative, tactical, granular, modular, decentralized. Put the two together internally and you are replacing product- or market-based organization with a functional one. Put the two together externally and you have a fundamental challenge to many traditional business models.
Thus, Big Data is not an isolated or unique phenomenon: it is an exemplar of a wider and deeper set of trends reshaping the business world. Achieving the potential of Big Data is a challenge not only to process and capabilities, but also to organization and strategy.
As Big Data reshapes business, it will transform two fundamental aspects: internal organization, and industry architecture. Organizationally, Big Data impels corporations to consolidate databases in order to achieve internal economies of mass. The implications of Big Data for industry architecture are all about tapping the superior capabilities of other players. It may require outsourcing innovation to small contributors, especially customers, by exposing APIs and proprietary databases. It may require outsourcing processing and facilities management to a cloud provider that enjoys superior economies of scale and experience. It might involve investing in data partnerships to achieve critical mass collectively that would be infeasible severally. In every case the definition of the business is being changed to accommodate the evolution of competitive advantage beyond the bounds of the traditional business model.
There is one final issue whose importance cannot be over-emphasized: data rights. It is profoundly ambiguous in most business contexts who “owns” personal data and what rights they have to use it. If the terms of data exchange were tightened, as some policymakers have proposed, then the properly open-ended nature of Big Data exploration would be stymied. It is unlikely that these legal and perceptual ambiguities will be cleanly resolved in the next few years. In the interim, corporate (and governmental) use of personal data will depend critically on the context in which the data is gathered and used, and on the degree of trust enjoyed by the data-using organization. Establishing that context, and building that trust, will be fundamental challenges. Ultimately, the legitimacy with which corporations use their data, in the eyes of their customers and the eyes of society, will constrain the rate at which the Big Data revolution transforms our world.
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