Maximising the value of your data function is crucial for a company's success. However, many companies are still lagging behind. In 2020, just 38% of businesses claimed that they had managed to create a data-driven organisation, and yet 64% claim that they use data to drive innovation. So, what can leaders do to get the most from their data teams and truly maximise the value of their organisation’s data function? 

Define and communicate your data strategy.

One of the key ways to maximise the value of a data function is to clearly define the data strategy, align it with the overall business strategy, and effectively communicate the business impact to the teams involved in processing and applying that data.

For example, if the goal is to develop new treatments for patients and bring them to market quickly, your data strategy could be focusing on collecting data from clinical trials, electronic health records, and other sources to gain a deeper understanding of disease mechanisms and identify potential drug targets, and as a leader you should focus on.  

Some of the main types of users who will be vital with the implementation and enforcement of your data strategy are data engineers, data scientists, and data analysts. Focus on building out and engaging the team driving your data strategy and you will be in a better position to align the data function with your overall business goals.

Illumina, a biotech that specialises in genetic sequencing and array-based technologies, are an example of a company that has successfully maximised the value of their data function through a clearly defined and rolled-out data strategy. They use next-generation sequencing technology which allows for the rapid and cost-effective sequencing of large amounts of DNA, generating vast amounts of data. To manage it efficiently, they have created informatics solutions such as BaseSpace Sequence Hub, a cloud-based platform that allows data scientists to store, analyse, and share sequencing data easily. They have also developed data visualisation and analysis tools to help researchers make sense of their data and extract valuable insights. Their strategy and processes are clear, and this has helped maintain their 3% year-over-year revenue growth, positioning them well in the market.

Establish a data governance structure.

A robust data governance structure is essential for maximising the full potential of a data function. Data governance refers to the processes and policies in place to ensure the accuracy, completeness, consistency, and security of an your data. Having a data governance structure in place means that leaders can be confident that the data they are using is high quality, which can help to prevent errors in R&D and ultimately lead to more effective and efficient processes. 

With increasing regulatory scrutiny in the life science industry, a data governance structure can help to ensure compliance with industry standards and regulations set by the FDA, EMA, and other regulators. This can help to mitigate the risk of non-compliance and costly penalties and protect your reputation.

Having the right team in place will be crucial in establishing your data governance structure. Ahead of implementing the processes, ensure you have a chief data officer (CDO) or other data lead. They will be responsible for implementing the framework, executing the strategy that comes with it, and leading your data team to success.

Establishing a data governance structure is more than a one-person job. It would be beneficial to also hire a data owner and a data steward to support the CDO. The team will help train employees and ensure protocols are followed. These people will be the backbone of your data governance structure and begin the processes necessary for your organisation. 

Roche Diagnostics are a great example of a life science company who has focused on their data governance to maximise the value of their data function. They use a centralised data management system that allows for the secure storage and sharing of data across different departments and locations. The system is encrypted and has advanced access controls to ensure that only authorised individuals can access and manipulate the data. Their data governance teams also work closely with other data experts across the business to ensure that the data is used ethically and responsibly. Their strict policies and procedures ensure the integrity, security, and accessibility of their data.

Encourage a data-driven culture.

It’s reported that only 15% of healthcare organisations worldwide are fully equipped to make quick data-driven decisions. Yet nearly 50% of companies have already invested in big data and more than 70% of those businesses plan to invest again. It is becoming more and more evident that for businesses to remain competitive and progress it essential that they have a data-driven culture. 

In the life science industry, data plays a crucial role in everything from R&D, to clinical trials, to regulatory compliance. By having a strong data function in place, leaders can gain valuable insights into their operations and processes, which can help them identify areas for improvement and drive innovation. Additionally, by using data to inform decision-making, you can ensure that your company is operating in a compliant and cost-effective manner. By creating a culture that values data and encourages its use, you’ll be able to foster a more productive and efficient workforce – even beyond your data function.

Making sure that you have the right team in place is essential to create and maintaining a data-driven culture. It will be down to your CIO’s and CTO’s to drive this culture, so when it comes to hiring you need to look at more than just the technical skills, soft-skills such as communication are going to instrumental in making sure the shift to a data-driven culture is a success. 

Biogen, a biopharmaceutical company focused on developing treatments for neurological and neurodegenerative diseases, has a strong data-driven culture. They use data to inform every aspect of their research and development process, from identifying new drug targets to conducting clinical trials and evaluating the effectiveness of their treatments. Biogen uses advanced analytics and machine learning techniques to analyse large amounts of data and identify new insights that can inform drug development. For example, they’re currently using patient-oriented AI solutions to transform care for neurodegenerative disease. Their data-driven approach is helping them to develop innovative treatments for diseases with high unmet medical needs giving them more notability in this niche market.

Drive cross-departmental collaboration.

Encourage collaboration and the sharing of data across the organisation. While sharing data might sound like a big “no-no”, if done correctly, when different departments and teams have access to the same data and are able to share insights, it can lead to a more holistic understanding of the business and better decision-making, and highlight the importance of data in the bigger picture of your company’s vision to those responsible for processing it.

However, to facilitate this type of collaboration, it is important to establish the clear data governance structure previously mentioned, and also ensure that data is accurate and reliable. Additionally, it can be helpful to provide training and resources for employees to help them understand how to work with data and extract insights from it. 

Qiagen is a great example of a life science company who are maximising their data function through cross-organisation collaboration. 

• Data-driven R&D: they use data analysis and machine learning techniques to improve the development of new products and services.
• Supply Chain Optimisation: Qiagen uses data analytics to optimize its supply chain operations, such as inventory management and logistics, to ensure that products are delivered to customers on time and at the lowest possible cost.
• Digitalisation of laboratory workflows: they use data analytics to digitise laboratory workflows to increase efficiency and reduce errors, which enables scientists and researchers to collaborate more effectively.
• Customer Relationship Management: Qiagen uses data analytics to improve its customer relationship management, such as by using data to identify customer needs and preferences, which helps to improve collaboration with customers.

By fostering a culture of data collaboration, organisations can unlock the full potential of their data function by driving innovation and growth, enhancing insights, improving innovation, accelerating delivery speeds, and increasing team engagement. 

Conclusion.

While implementing these practices will in theory maximise the value of your data function, those in leadership positions must continuously measure and evaluate the performance of each aspect of their data function and ensure that they’re providing maximum value to the organisation. Regularly monitoring and assessing the performance of a data function can help identify areas for improvement, such as a lack of data quality or a lack of alignment with business goals. It can also help to identify areas of success, like successful data-driven decision-making processes or increases in revenue. By regularly measuring and evaluating the performance of your data function, you’ll ensure that you are getting the most value out of their data.

Having a solid team with the right people, good leadership, and proper training is key to making sure you have all the pieces in place to make your data function run smoothly. If you need support for your next data-based hire, reach out to us today.