Modern research has thrived with digitization, yet data engineering and business-led digital innovation often lag behind. Advancing research with AI requires more than digital tools—it demands an environment where researchers can create and manage their digital assets freely.
This article explores the challenges and opportunities of driving digital innovation in research.
Digital innovation in the enterprise often conjures images of cutting-edge technologies like digital twins and quantum computing. These advancements promise to revolutionize industries by providing unprecedented insights and capabilities. Digital twins, for instance, allow businesses to create virtual replicas of physical assets, enabling real-time monitoring and predictive maintenance. Quantum computing, with its potential to solve complex problems exponentially faster than classical computers, holds the promise of breakthroughs in fields ranging from cryptography to drug discovery.
Digital transformation is not a destination; it is a foundation.
While these high-tech innovations capture headlines and drive strategic initiatives, the democratization of digital tools for everyday business functions can often have a more immediate and profound impact on day-to-day work. This democratization involves making advanced digital tools accessible to all employees, not just IT specialists or data scientists.
While the enterprise view of digital innovation focuses on groundbreaking technologies that can redefine industries, the democratization of digital tools addresses the practical needs of the workforce. It’s this accessibility and ease of use that often drive the most significant changes in day-to-day operations. Many of the digital transformation projects have focused on the tech-side. They have built cloud-journeys, infrastructure, or platforms but have missed the real transformative power of organizations; the people that make business processes work.
True digital innovation requires a balanced approach. High-tech advancements like digital twins and quantum computing hold immense potential for future transformations. However, the democratization of digital tools is equally crucial, offering immediate benefits and empowering employees to innovate in their daily tasks. Organizations that recognize and invest in both aspects—strategic technological advancements and practical tool accessibility—will be best positioned to thrive when we want to implement the advanced technologies of the future.
Based on our hands-on work in implementing these tools across various organizations, we have gathered valuable insights and practical advice. In the following sections, we share concrete strategies and actionable steps on how to support practical digital innovation in your organization.
Research data is inherently diverse, varying significantly across disciplines and projects. Supporting digital innovation requires systems that can handle this heterogeneity, providing flexible data management solutions that cater to different types of data—whether structured, unstructured, qualitative, or quantitative. Tools that facilitate easy integration, storage, and retrieval of diverse data types are essential for fostering a productive research environment.
Each research project and department has its own unique needs and workflows. Digital tools and platforms must be adaptable to these idiomatic requirements, allowing researchers to customize their environments according to their specific needs. By providing configurable solutions, institutions can ensure that researchers are not constrained by rigid systems but are empowered to innovate and experiment freely.
Research data is inherently diverse, varying significantly across disciplines and projects. Supporting digital innovation requires systems that can handle this heterogeneity, providing flexible data management solutions that cater to different types of data—whether structured, unstructured, qualitative, or quantitative. Tools that facilitate easy integration, storage, and retrieval of diverse data types are essential for fostering a productive research environment.
Digital assets play a pivotal role in modern research. Providing tools that facilitate the creation, management, and sharing of these assets is essential. Whether it’s building predictive models, developing interactive applications, creating data products, or managing offline databases, researchers need robust and user-friendly platforms that support their innovative endeavours.
Traditional IT project management processes can often slow down the creation and deployment of digital assets in research. By removing these dependencies and enabling researchers to independently manage their digital projects, institutions can significantly enhance productivity. Researchers should be able to create and deploy digital assets such as models, notebooks, scripts, Streamlit apps, data products, pipelines, and offline databases without being hindered by bureaucratic processes.
Digital innovation cannot be achieved by merely purchasing an IT system. Success lies in fostering a collaborative community where researchers can share knowledge, tools, and best practices. Community-building efforts, such as workshops, forums, and collaborative projects, are vital for creating an environment where digital innovation can thrive. Encouraging researchers to contribute to and benefit from a collective pool of resources enhances the overall research ecosystem.
Where data tools and teams unite.
