So gelingt die digitale Transformation naturwissenschaftlichen Unternehmen

Digitale Transformation kann als Prozess zur Gewinnung neuer Fähigkeiten in Unternehmen betrachtet werden. Die Voraussetzungen dafür, sich reibungslos selbst zu organisieren, nahtlos zusammenzuarbeiten, systematisch zu experimentieren und schnell markt- und kundenorientierte Entscheidungen zu treffen, sind für den Erfolg in einem sich schnell verändernden digitalen Umfeld unerlässlich. Die richtigen Instrumente und Daten sind wichtige Voraussetzungen für den Aufbau dieser digitalen Kompetenzen.

Erfahren Sie mehr zu diesem Thema im folgenden Blog Post, der zuerst im Blog des Copyright Clearance Centers erschien.

The right tools and data are key enablers for building digital dexterity.  In particular, through previous research at MIT, we’ve found that access to current (timely) and integrated data is correlated with greater digital dexterity.  Access to effective communication, collaboration, and coordination tools is also correlated with greater digital dexterity.

Technical and scientific industries, like the life sciences, are extremely data-intensive businesses, especially in the R&D function.  As research and development efforts target increasingly complex problems, collaboration is a crucial component of the R&D process.  Thus, tools that support seamless access to necessary data and effective collaboration can be particularly fruitful for these companies in their digital transformation efforts.

Effective tools for the R&D intensive industries should consider at least three kinds of stakeholders:

1. Researchers
2. Publishers
3. Enterprises

Researchers focus on the characteristics of their research problem on a day-to-day basis. They don’t want to struggle to access relevant sources, nor worry about if and how they might share those sources with their collaborators.  Nevertheless, these issues are of concern to both publishers of content and the enterprises themselves that must validate and assure their research processes and outputs.

Copyright licensing has addressed these concerns, enabling enterprises that subscribe or otherwise legally acquire content to also share, store and make copies of that content in line with their intrinsically collaborative research processes.  Within enterprises, however, there is still the challenge of finding the right content relevant for a particular research stream, at the right time, and ensuring that content sharing is appropriately limited to partners and collaborators according to the license. Relatedly, there is the challenge of incorporating new information such as research findings (or failures), documenting its sourced content, and retaining it over the lifecycle of a research effort, for use in downstream activities.

Two categories of tools are emerging that support the data-intensive and collaborative activities prevalent in the life sciences:

Content management tools

These tools, which are configurable to the enterprise organizational structure and incorporate decision engines, can overlay licensing arrangements and ensure that, within an enterprise, content is both limited but also easily visible and accessible to authorized researchers and other professionals.

For an individual researcher, the content management interface must be transparent, giving them flexible access to all the relevant input sources available through their enterprise license. Effective content management tools give researchers the ability to seamlessly search, annotate, print or share content with collaborators within the natural flow of their research activities.

At the same time, content management tools offer enterprises and associated publishers the assurance that research is being conducted thoroughly and systematically while referencing content appropriately within the bounds of the relevant licenses.

Related Reading: What to Look for in a Discovery Tool: 3 Questions to Ask When Developing a Data Strategy for Life Sciences Information

Workflow support tools

These tools also address the data and collaboration needs of the life sciences by contextualizing content used in characteristic activities such as grant applications, FDA submissions, and article publications.

For example, researchers increasingly need to share their datasets as part of the publication process. Enterprises need to provide detailed documentation about clinical trials, which might extend over many years, as part of an FDA application. Workflow tools incorporate templates to help ensure that, for these routine, but complex, activities, essential information sources are included, decisions are fully documented and approved by authorized decision-makers, and all stakeholders are fluidly kept informed of progress.

What’s the impact?

For life sciences organizations to undergo successful digital transformation, they need tools that facilitate fluid access to relevant data. But don’t forget – alongside these tools, organizations need to integrate expertise from several disciplines, such as molecular biology and organic chemistry, statistics and machine learning, computer science, and systems engineering.

The bottom line? Tools are important – but they’ll only go so far if your organization lacks overall digital dexterity. Organizations that will be successful in digitally transforming need to rely on a strong combination of people, processes, and technology.

Explore Copyright Clearance Center’s solutions for life sciences data and information strategies. 

This blog was originally written by Dr. Deborah Soule.

Dr. Deborah Soule conducts research on the interaction between technology and organizations, with particular attention to the dynamics of learning, collaboration and change. She has over 15 years of experience leading research and development projects in both industrial and academic settings, including MIT and Harvard, plus ten years of client-facing responsibility as an organizational and technical subject matter expert. Earlier in her career, she worked on product development programs for a large chemical company in Europe.