As organizations pursue digitization, it can be all too easy for technical systems to become fragmented and incapable of interoperation. This can impede innovation and impact the flow of the knowledge, data and resources that are essential for driving transformation and maintaining a competitive edge.
There are two aspects to this: technical and business. Focusing on one without the other will make collaboration ineffective. SMEs from different platforms hold deep knowledge on their respective areas, while business stakeholders can provide the foundation for new collaboration and interoperability. Having a disciplined cadence with executive presence will provide opportunities for innovation. -
Go for shared data formats and interoperability frameworks. Focus on identifying common business processes and workflows that can be mapped across various tech platforms. This creates a shared understanding of how information should flow and improves interoperability. Do proofs of concept often to allow for early technology adoption, but before scaling, benchmark the results. -
Ongoing education is crucial for scaling, innovation and meeting consumer needs across diverse platforms. Practices such as establishing guilds and pair programming for engineers can help unite distributed teams, promote knowledge exchange and encourage joint problem-solving. - Allowing small teams to solve a subset of a legacy problem enhances the chances of dramatic innovation. Imposing the same problems and constraints on everyone stifles that innovation. It’s applying the concepts of the “innovator’s dilemma” to your own innovations. The subset approach may not be deployable immediately, but it increases your speed and will result in more at-bats in the game of change.
In the life sciences, data is the key asset, and the increasing volume and velocity of data make interoperability critical. No singular system can solve every problem along the clinical data value chain. Data sharing must be part of your overall data strategy. At the same time, organizations need to prioritize having a central source of truth to increase the availability and usability of data. -