Scientific organizations of all sizes, scopes and specialties produce and manage copious amounts of scientific data — but without the ability to effectively draw insight from this information, its true value cannot be unlocked.
Establishing a structured data management strategy is an essential step in any successful digital transformation. A successful data strategy will seamlessly support current technology while also offering the flexibility and scalability needed as new applications emerge. To unlock the power of artificial intelligence, machine learning and cognitive analytics, scientific organizations need a strong data foundation and a high level of staff expertise. After all, the success of these technologies relies wholly on the inputs they receive: if garbage goes in, then garbage comes out.
Organizations often overlook the strength and value of their data assets. Data management is not a new concept. However, it remains a complicated and challenging one, especially for scientific data. As you set out to implement and manage a scientific data strategy, here are three must-haves to ensure long-term success.
1. Executive alignment around business goals
Ensure that all stakeholders understand and support what you wish to achieve. Is the aim to drive revenue, to improve efficiency, to innovate? The desired outcomes should frame your approach to data strategy and inform where you invest. Naming a dedicated leader of your data strategy initiative will help ensure consistent focus and application of resources to achieve your business goals.
2. Talent with both scientific and data management expertise
To implement an effective data strategy in an R&D setting, it’s critical to combine data management skills with a deep understanding of the complexities of scientific and technical information. This presents a challenge to many R&D organizations, as few individuals have both scientific and data management expertise.
To overcome this challenge, find researchers who love data and give them the training they need — across data governance, stewardship, modeling and architecture — to be effective data managers. Nurturing the passion for data innovation within those who understand the scientific context of the data problems is a valuable approach for any R&D organization.
Toray Industries, a leader in innovative materials for industrial products, recognized this and sought help from CAS to guide their data management journey. Through our customized training program, Toray equipped their scientists with the strategic and technical data management know-how needed to bring order to their data and optimize their assets for innovative R&D. Training on data curation, management, integration, search and analytics shifted the perspective of Toray scientists from a piecemeal view of scientific data toward an integrated, holistic perspective.
Want to learn how Toray is unlocking new value from their data to accelerate their digital innovation strategy? Read the Case Study.
3. A sense of urgency to get started
Many organizations have decades of data they need to organize and are generating more every day. The longer you wait, the bigger the pile-up of messy data – and the more time you need to curate and organize. This ultimately results in more missed opportunities and can put you at a competitive disadvantage.
The path to a data strategy is one of continual improvement and is far from a one-and-done endeavor. Learning never ends but will pay back exponentially as you are able to accelerate innovation and unlock the true value of your rich R&D data.
Ready to shift your data strategy into high gear? Learn more about training from CAS.