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The Impact of Scientific Monitoring on Innovation

Scientific monitoring is essential in sectors like pharmaceuticals, medicine, food, energy, and automotive. Companies analyze scientific publications to assess technological advancements, initiate R&D projects, and identify potential innovation partners.

Despite its significance, scientific monitoring is time-consuming due to the increasing volume of information and the extensive analysis required. The challenge lies in efficiently managing this data to extract relevant insights.

This article will explore the impact and value of scientific monitoring for innovation, focusing on how companies can optimize this crucial process to remain competitive and foster growth in a rapidly evolving technological landscape.

Time for scientific intelligence

The value of scientific intelligence for innovation

Best practices in business intelligence

Ia for scientific intelligence

1. Time Dedicated to Scientific Monitoring

In a context where scientific monitoring is vital for innovation, companies dedicate between 1 to 20 hours per week. However, professionals face several challenges:

Time Consumption

The process is lengthy, and the value generated may not meet expectations.

Lack of Time

Monitoring is often rushed or outsourced to focus on other tasks.

Information Overload

The sheer volume of data is overwhelming.

Fear of Omissions

Inability to access all relevant literature raises concerns.

These issues, mainly due to time or resource constraints, limit the added value of scientific monitoring in innovation projects. To optimize this process, it’s essential to:

Identify and access relevant data sources.

Structure the analysis of collected information.

Enhance knowledge sharing within the organization.

By overcoming these obstacles, companies can maximize the impact of their scientific monitoring and drive innovation more effectively.

2. The Value of Scientific Monitoring for Innovation

Typically, scientific monitoring occurs at the project’s outset, helping to define the innovation project’s scope by:

Starting points for the innovation project.

Through reviews, experts can assess the technological maturity of a topic to avoid duplicating previous work.

An understanding of the subject experts

to contact them or analyze the work of a specific expert.

Evaluate the technological maturity level of a scientific topic

to determine if the planned experiments have already been conducted or if the innovation project has already been realized.

Justify a technological or scientific decision.

In an innovation project, an approach may be chosen for various reasons. It is generally essential to justify this approach with prior publications and scientific research that have reached the same conclusion.  

In these scenarios, pertinent information is primarily found in scientific documentation, highlighting the true added value of scientific monitoring.

3. Best Practices for Scientific Monitoring in Companies

In many organizations, scientific monitoring is often disorganized or poorly shared, hindering the effective use of scientific information in innovation processes.

 

To ensure effective scientific monitoring, companies should:

Identify and track relevant information sources, such as academic publications, patents, and conferences.

Use automated monitoring tools to efficiently collect and filter information.

Involve internal experts to analyze and contextualize the gathered information.

Establish a regular dissemination process for monitoring results to relevant stakeholders.

Regularly assess the effectiveness of monitoring and adjust processes as needed

Integrate scientific monitoring into decision-making and innovation processes.

4. AI for Scientific Monitoring

AI is transforming all organizational departments, but R&D, scientific, and innovation sectors lack suitable tools. Public solutions often fall short due to confidentiality, comprehensiveness, and data sourcing requirements.

 

New AI tools, like Opscidia, are emerging to support these experts without replacing them. These solutions aim to optimize their time and efficiency by offering features tailored to their needs while respecting their field-specific constraints.

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