What is a semantic data layer ONTOFORCE DISQOVER


The competitive advantages of connecting data with a semantic layer

Managing the use of external and internal data can be a challenge for organizations without the proper infrastructure in place. We're sharing why organizations need to be leveraging a semantic data layer in order to make better use of the two. 

1 March 2024 5 minutes

There’s no doubt that 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. However, keeping up with external data in addition to internal data can be a challenge for organizations without the proper infrastructure in place.  

In this blog post we’ll look into the challenges associated with integrating internal data sources, the power of public data, and why organizations need to be leveraging a semantic data layer in order to make better use of the two. We’ll discuss why connecting internal and external data through an integrated semantic data layer is essential for life sciences organizations looking to gain a competitive advantage in the industry.  

Challenges with internal data 

Life sciences companies have teams and departments spanning the entire drug development timeline, each producing data that not only brings value for their specific function but can also bring significant value for other entities across the organization. For example, data on adverse events from a clinical trial can be reused in early identification of potential safety issues for new, similar drugs. However, enabling data reuse is where many companies fall short. This is due to a number of factors:  

  • Data is not integrated and is stored in places that are inaccessible to other entities within the organization, making it difficult for others to know such data exists in the first place. 
  • Data lacks context or is formatted in a way that renders it unusable to non-technical users, thus requiring time-intensive effort to transform the data into a useable and understandable format.  
  • Even if data is accessible and understandable, it might require too much manual effort to retrieve said data, taking away valuable time for other important necessary tasks. 

The power of public data 

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.  

ClinicalTrials.gov is another publicly available resource that’s key for life science companies. Data from this site enables organizations to monitor ongoing and completed clinical trials across the globe, which is essential for identifying gaps in research, keeping an eye on competition, and providing invaluable insights into trial designs to aid in the optimization of their own clinical trials and regulatory strategies.  

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. Additionally, to really reap the benefits of public data, it should be fully integrated with an organization’s internal data. 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 organizational data and external data

Integrating internal data within a life science organization is crucial for maximizing the value of the data collected from activities spanning the entire drug development life cycle. It enables a holistic analysis of research, findings, and metrics, facilitating operational efficiencies and enhancing decision-making processes and strategic planning.  

In addition to this, internal data needs to be integrated with external data, such as publicly available sources and licensed sources. 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.  

Building such a comprehensive data ecosystem accelerates and optimizes various process across drug development, such as target and biomarker identification, clinical trial design, and market intelligence and competitive analysis capabilities. It enables companies to stay abreast of the latest scientific advancements, regulatory changes, and market trends, facilitating more informed decision-making and strategic planning.   

Data integration often requires technical profiles who 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.  

What 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. 

Integrating data through a semantic data layer allows life sciences organizations to harness the full potential of both proprietary and publicly available information. By leveraging a semantic layer, organizations can unify disparate data sources to create a cohesive, context-rich view of data. The semantic layer acts as a bridge, translating complex datasets into a common language, thereby streamlining data analysis, accelerating research processes, and fostering innovation.  

What are the competitive advantages of integrating data with a semantic layer? 

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. More specifically:  

  • Increased efficiency and reduced costs: by streamlining data management and reducing the need for manual data integration and cleansing, a semantic layer can significantly reduce operational costs and increase efficiency. This allows organizations to allocate more resources to core research and development activities. 
  • Accelerated time to market: by providing a common framework for describing data, a semantic layer makes it easier for researchers and decision-makers to discover, access, and utilize the data they need. This can fast-track research processes, from hypothesis generation to clinical trials and regulatory submissions. Teams and departments can get more done, faster, ultimately accelerating  drug development. 
  • True data-driven decisions: the semantic layer 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.  
  • Improved data quality for AI applications: 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. 
  • Scalability and future-proofing: as organizations grow and the volume of data increases, a semantic layer provides a scalable framework for managing data. It also ensures that the organization can adapt to future technological advancements and data standards, protecting its long-term investment in data infrastructure. 

The risks of siloed data  

Not investing in a semantic layer, or semantic technology, poses risks to an organization’s ability to remain competitive in an industry that is known for technological data innovation that rapidly evolves. By implementing a semantic layer, organizations can ensure that data from various sources is integrated and interpreted within a unified framework, to not only break down the barriers between different data silos but also enhance data quality and consistency, enabling a more holistic view of information. As a result, organizations can leverage their collective data assets more effectively, driving insights and decisions that are informed by a comprehensive understanding of available information.