Editor’s note: Diana Pohle is senior director, business analytics and insights, at biotechnology research company Myovant Sciences, Brisbane, Calif.
On the surface, the democratization of data holds wide appeal for managers of the most innovative of start-ups and the stodgiest of incumbents alike. Few would deny the obvious advantages of accelerated decision-making and a culture of innovation that a data-driven orientation can nurture.
But the optimal data-driven operating model for any organization isn’t obvious. An organization planning for organic growth within an established footprint likely has very different needs from one planning for new vertical integration or an international expansion. Your confidentiality and cybersecurity needs, for instance, are very different when you’re sprinting toward a launch than when you’re undertaking merger and acquisition due diligence, or even when the target of due diligence is primarily invested in intellectual rather than physical property.
That is why an organization’s operating model itself needs to be every bit as nimble and flexible as any market-responsive activity undertaken by the organization. It needs to be as dynamic as the organization’s markets are, open to continuous reappraisal and adjustment in response to ever-changing business conditions and new market information. The model needs to be selected based on a dynamically weighted matrix comprising architecture, audience preferences and abilities, scalability and talent flows.
Centralized, command-and-control systems are uncommon these days. Deeply siloed back offices that unify data collection, analysis and decision-making are largely limited to organizations with unusually large investments in physical assets. These systems may be justified by substantial barriers to entry, legacy investments or preoccupation with liability or confidentiality concerns.
Much more common are fe...