Modern research has benefited immensely from digitisation efforts, e.g. through the big data wave, and digital tools have become ubiquitous in the scientific workflows of knowledge driven organizations. And while data science has made its impact, both data engineering and digital innovation in the business functions have taken a backseat position. But our ambition of advancing research through machine learning and AI requires more than just digitizing processes; it demands a shift towards business led digital innovation. This transition involves not only the use of digital tools but also fostering an environment where researchers can freely create and manage their digital assets.
This article delves into the key aspects of supporting digital innovation in research settings, focusing on the unique challenges and opportunities that come with this transformation.
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.
Empowering researchers with self-service digital tools and environments is crucial for fostering innovation. Platforms that allow researchers to quickly set up and manage their own computing resources, datasets, and analytical tools enable them to focus on their research without waiting for IT support. This autonomy accelerates the research process and encourages a culture of experimentation and innovation.
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 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.
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.
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Modern research has benefited immensely from digitization efforts, e.g. through the big data wave, and digital tools have become ubiquitous in the scientific workflows of knowledge driven organizations. And while data science has made its impact, both data engineering and digital innovation in the business functions have taken a backseat position. But our ambition of advancing research through machine learning and AI requires more than just digitizing processes; it demands a shift towards business led digital innovation. This transition involves not only the use of digital tools but also fostering an environment where researchers can freely create and manage their digital assets.
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