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Data driven organizations have the potential to increase the profit

A data driven organization can be defined as an organization that maximizes the value of its data (of all day-to-day data they are dealing with e.g. customer, clinical, research data). Nevertheless, by now just 26.5% of organizations worldwide have established a data-driven organization (Harvard Business Review, 2022).

One of the branches that particularly faces increasing competition is the life science industry. Life Science companies are forced to regularly launch new drugs to stay competitive. Therefore, their Research & Development department (R&D) plays an essential role in helping to stay ahead of the competition. Life science companies spend up to 25 % of their investments just on R & D. The integration of new, big and correct data can be a perfect addition in the fighting of cost explosions. (Forbes, 2022)

Due to this high relevancy, this article focuses on setting up a data driven organization within the R & D departments of Life Science companies.

Cognizant defines Organizational Design (amongst others) as the review and redefinition of an operating model based on requirements. This redefinition also leads to changes in responsibilities and job roles.  

A first step to become a data driven organization would be to structure people, systems, and applications in a way to:

  • Ensure access to the right data for the right people (Brown, 2020)
  • Support the data exchange between employees
  • Foster data analysis

This helps avoiding losing valuable insights by having data dumped somewhere unused (Brown, 2020).

Through analyzing the current areas of improvement in Organizational design, the Research & Development operating models in pharma will be assessed.

Figure 1 illustrates an exemplary high-level operating model of the pharmaceutical R&vD process.  

Figure 1: Drug development process

Once the R & D process is done, the newly developed drug requires a permission from a regulatory authority for example from the Food and Drug Administration (FDA) in the USA.

The above illustration indicates that data plays an integral role within an R & D organization. For example, during clinical trials 3.6 million data points are generated in average (Forbes, 2022). Also, data is considered within the drug approval process. The FDA reviews data on drug effects to decide whether the benefits of the newly developed drugs outweigh any risks to provide approval (FDA, n. D.)

Until today scientists generally are using data in silos for their specific purposes (Entellect, 2019). Nowadays, emerging technologies allow scientists to gain new insights beyond their specific analysis (Entellect, 2019). This could solve new (medical) problems by gaining further insights (Entellect, 2019). The Information Technology & Innovation Foundation estimates that data could bring in annual cost saving potentials of 54 billion USD for pharma R & D (Forbes, 2022). The usage of data in R & D is going to be extended in the future.

Further use cases will be added such as analyzes beyond a specific medical problem (Entellect, 2019). To accomplish this, a data driven organizational design is required. A data driven organizational design should foster data sharing through the below example.

Organizations need to design a structure that enables data sharing and data analysis especially in their R&D departments to integrate the use of data to remain competitive. Based on identified areas of improvements, the following adjustments to an existing operating model can be suggested:

Figure 2: Data driven operating model grafik
Figure 2: Data driven operating model

Outdated technologies leading to incorrect and insufficient data were identified as one reason for having data hidden in silos (Brown, 2020). Therefore, as a first step, a platform like Power BI, Tableau or Qlik with access to harmonized and updated data should be introduced. Cognizant Technology Solutions has been working on this topic for a long and can offer a broad range of experience. As an example, we have introduced a harmonized platform for clinical trial data at Roche in the year 2022. Such a new platform helps to ensure standardization and consistency of clinical trial data for study teams and other impacted stakeholders. With this a project could reduce their average questionnaire time by 36 %.

Such a newly generated platform needs to be supported by a proven governance structure. A governance structure provides guidance about who is having access to which data and ensures legal compliance. It is essential that those employees who need the data have access and can work with it.

For companies newly implementing such data bases it could be beneficial to involve data science experts to improve current low data competences. Additionally, those experts can accompany the introduction of data usage and data literacy within an organization.

After implementation of a data platform and governance structures, user adoption must be ensured. A study found out that only 20 % of the data and analytics solutions deliver business outcomes (Jain, 2022) due to lack of adoption.

To prevent this, it is highly recommended to work on a culture of data usage (especially beyond data analyzes) and data sharing across all workstreams. This can be achieved by leveraging experts in organizational change management.

A structured change strategy & plan supported by communication measures, tailored trainings, a change agent network and leadership alignment ensure a successful adoption across all workstreams. All new tools and ways of working need to be communicated within all available communication channels. R & D workers need to be trained how to use those new tools. A dedicated change agent network could support the interdisciplinary collaboration. Leadership alignment is essential, as the management must support the overall initiative and needs to provide a clear visual commitment for a data driven organization.

To ensure regular data exchange across all R & D functions communication channels such as SharePoint groups or regular exchange meetings could be established. Possible discussion topics within those communication channels could be the use of clinical trial data for further insights into the discovery of other drugs. The overall communication for example can be moderated and fostered by a dedicated and newly nominated data officer.

The overall goal of this is to adopt data sharing and data usage to everybody’s daily ways of working. Such new ways of increased working with data will lead to changes in the role profiles of the R & D workers. Therefore, Organizational Change Management (OCM) teams play a crucial part in assisting these changes. OCM helps to identify the job profile changes with a Change Impact Analysis. Based on that they are going to conduct communication measures and trainings to onboard all employees. It is important that all employees understand the benefits of the data structure. Goal is to make employees see the change as an upskilling of their role.

Cognizant has broad experience in extending use cases for clinical data. As an example, for one of our customers we have applied artificial intelligence techniques to clinical trial data to gain further insights regarding the optimal dosage for cancer drugs. This led to cost savings between 8% and 10 % per patient for our customer. In another project we developed a text mining algorithm to gain critical insights within 200’000 patient data. Those use cases proved the benefit of sharing data and using data beyond the already known ways of including data into our day-to-day work.

Dominik Lefèbvre

Business Analyst, Organizational Change Management, Cognizant

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Dominik Lefèbvre has broad experience in the Life Science, Insurance, Automotive and Manufacturing industry in various functions. He currently advises life science clients in transformation and organizational change management topics and has a strong interest in data literacy topics.

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