Sponsored by:
Produced by:
Claus Andersen
Sr. Dir. Data Science in Data Orchestration as Data Lead
The real challenge lies in bringing machine learning to a production-level stage, where it can effectively transform data into actionable insights.
Johnson & Johnson's enterprise has highlighted the role of data, cloud computing, and edge computing in enabling innovation and scalability. What specific technology trends do you observe in your industry, particularly related to machine learning?
Machine learning is gaining significant momentum in the healthcare industry. It is becoming a key driver of innovation and transformation. Organisations are recognising the power of machine learning to extract valuable insights from vast amounts of data and make accurate predictions. However, it's important to note that we are still in the early stages of fully harnessing the potential of machine learning. The real challenge lies in bringing machine learning to a production-level stage, where it can effectively transform data into actionable insights. Additionally, cloud computing and related technologies are essential tools that enable scalability and accessibility to data and computational resources. While these technologies are necessary, the true differentiator lies in how organisations leverage them, invest wisely, assemble the right talent, and focus on the end goal.
Dr. Lee Harland emphasises the importance of making data machine-readable and standardising data sources to improve accuracy and reliability. What steps can data science leaders like yourself take to achieve this outcome?
Achieving accuracy and reliability in data management requires a methodical approach. It begins with building robust and reliable processes for data collection, storage, and analysis. It's crucial to ensure that data is in a usable format and accessible for analysis. Standardising data sources is essential to achieve consistency and comparability across different datasets. This involves defining clear data standards, establishing data governance frameworks, and leveraging technologies like ontologies to organise and structure data effectively. However, it's important to strike a balance and avoid investing excessive time and effort in standardisation if the value derived from it does not align with the organisation's goals. Taking an agile approach, gathering feedback, and continuously refining data management practices are key steps to improve accuracy, reliability, and overall data literacy within the organisation.
The combination of automation and real-world data can drive efficiency, increase ROI, and foster innovation in the life science industry.
Life science research data is expensive to produce and interrogate, and there has been a significant increase in the average cost of developing assets. What should be the key objectives for data management to improve efficiency, increase ROI, and speed up innovation in the future?
Two key objectives can significantly enhance data management in the life science industry. The first objective is to leverage automation and technologies like machine learning to expedite processes and achieve significant improvements in efficiency. By automating various tasks and leveraging machine learning algorithms, organisations can streamline data collection, analysis, and interpretation. This can lead to faster insights, reduced costs, and increased ROI. The second objective is to embrace the concept of "point of care" and leverage real-world patient data for analysis and insights. By analysing data from a larger and more diverse pool of patients, even if it is not as controlled as traditional clinical trials, organisations can uncover valuable insights and accelerate innovation. The combination of automation and real-world data can drive efficiency, increase ROI, and foster innovation in the life science industry.
Could you elaborate on how your organisation approaches data governance and ensures data quality?
We have a robust framework for data governance. We define clear data governance policies and procedures that align with industry standards. We have dedicated teams and data stewards responsible for the implementation of these policies. To ensure data quality, we have a series of data quality checks and validation processes in place. Regular audits and reviews are conducted to monitor data governance practices and identify areas for improvement. Data governance is a continuous effort that requires active involvement from all stakeholders, and we strive to maintain high standards in this area.
Many organisations have vast amounts of historical data that need to be validated and checked for consistency. Automating this process can save a considerable amount of time and resources while ensuring the accuracy and reliability of the data used in analytics and machine learning.
Could you provide examples of how automation can enhance data analytics and machine learning?
Certainly! One area where automation can have a significant impact is in dealing with documentation. In many organisations, there is a wealth of unstructured data in the form of word files, PDFs, and presentations. Automating the process of extracting relevant information from these documents can significantly accelerate the addressing of various queries and provide a speedup of 100 or even 1,000 times faster than manual efforts. This can greatly enhance the efficiency and effectiveness of data analytics. On the data side, automating the verification of legacy data is another area with untapped potential. Many organisations have vast amounts of historical data that need to be validated and checked for consistency. Automating this process can save a considerable amount of time and resources while ensuring the accuracy and reliability of the data used in analytics and machine learning.
Claus Andersen is a Senior Director of Data Sciences, overseeing the Data Orchestration function. He collaborates with global project teams, integrating data science into project and product development. Claus orchestrates the data science team's efforts, facilitates effective communication, and ensures the utilization of expertise and crucial discussions. His role plays a vital part in driving successful data-driven outcomes.
Sponsored by
We are a global player in life-science R&D informatics, providing customers with cutting-edge multi-omics data management and integration software. We develop innovative off-the-shelf and custom solutions for clients in pharma, biotech, agriscience, consumer goods, and research centers. Omics Data Manager (ODM), our flagship product, helps organisations create a FAIR catalogue of multi-omics investigations (studies, samples, omics data), with powerful tools for curating rich and standardised metadata in bulk, as well as optimised RESTful APIs for scalable cross-study, cross-omics integrative search.
Up next: Survey Poll Results
