Life sciences companies are overwhelmed with the amount of data available in their organization. These vast amounts mean that valuable data is often overlooked during decision-marking or is not reused properly, impacting resources and efficiencies. On top of this, more and more publicly available and licensed data is becoming available. This data can be extremely beneficial in solving different use cases across the stages of drug development.
Keeping up with external data in addition to internal data can be a challenge for organizations without the proper infrastructure in place. Moreover, life sciences companies have teams and departments spanning the entire drug development timeline, each producing data that not only brings value to their specific function but can also bring significant value to other entities across the organization. For example, data on adverse events from a clinical trial can be reused in the early identification of potential safety issues for new, similar drugs.
In addition, publicly available data sources are crucial assets for life sciences companies, offering a wide range of benefits that span the entire drug development lifecycle and beyond. A comprehensive resource, such as PubMed for example, helps to facilitate literature-based discovery, enabling researchers to uncover potential drug targets, understand disease mechanisms, and identify biomarkers.
Despite the benefits public data provides for an organization, accessing and utilizing this data can remain a challenge ultimately impeding use. Without the proper tools or systems in place, time and effort will need to be made by individuals or dedicated teams to access and curate this data manually. Integrating internal organizational data with external public data allows a life sciences organization to significantly level up its data management strategies to derive deeper insights and facilitate better analysis.
Integrating internal data within a life science organization enables a holistic analysis of research, findings, and metrics, facilitating operational efficiencies and enhancing decision-making processes and strategic planning. This integration combines proprietary insights from internal datasets, such as experimental results and clinical trial data, with the vast expanse of knowledge available in public databases, including genomic sequences, biomedical research, and epidemiological data.
Data integration often requires technical profiles that are in high demand. When these individuals have large workloads, projects that rely on this integrated data can be delayed. Further, data integration at scale is practically impossible without appropriate technical infrastructure. A stand-out solution to enable efficient data integration for all types of internal data, along with internal data and external data, is a semantic layer.
A semantic layer is an abstraction layer that provides a unified and consistent representation of data across various sources and systems by using common formats and vocabularies. This layer can sit above the physical storage of data (such as databases, data lakes, or APIs) and allows applications and users to interact with data in a more meaningful and context-aware manner. In all, a semantic layer is the glue connecting all data with the business context it represents.
Organizations investing in a semantic layer can expect to see a return on their investment by way of improved research capabilities and operational efficiencies, and a sharpened competitive advantage. It enables more sophisticated analytics by understanding the context and relationships within the data. This can lead to deeper insights, predictive modeling, and better decision-making that’s based on a complete picture of accurate data.
Also, a semantic layer improves the standardization and enrichment of data across diverse sources, providing the necessary context and consistency for AI algorithms to operate effectively. This improved data quality facilitates more accurate predictions and advanced analytics, potentially elevating the reliability and insights generated by AI models.
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