Unlocking data insights through semantic normalization

The cost of fragmented data

The exponential growth of data is a critical challenge to modern research and development, especially in heavily R&D-reliant industries such as pharmaceuticals, life sciences, and chemistry.

As public research becomes increasingly available, your ability to combine it with proprietary data can drive immense value, from accelerating discovery and reducing research costs to informing your future strategy.

However, for that value to be realised, it requires you to be able to connect and reliably interpret data held across multiple different formats, platforms, geographical locations, languages and countless other silos.

Every organization has to deal with this kind of data fragmentation to some extent, in fact 80% of companies store more than half their data in hybrid and multi-cloud infrastructures. 

But left unaddressed, fragmented data becomes the antithesis of a strategic asset, risking business continuity as employees leave the business, taking personal drives and access with them. 

It doesn’t just make data management and analysis more difficult, expensive and time consuming, it can also make it more difficult to adhere to regulatory compliance. 

The commercial implications are also clear: When information is stored in disparate locations, systems and applications with different terminology and access levels, R&D teams cannot integrate it effectively, meaning work is repeated and value is lost.

Why semantic normalization is essential

To turn such a massive volume and variety of data into something useful, you need a process capable of combining, structuring and standardizing it in such a way that enables both machines and humans to interpret it with a common understanding. Semantic normalization applies to unstructured and fragmented data, as well as differently structured, separate data sources. The process transforms them into unified, meaningful formats, enabling you to deliver actionable insights based on every bit of information accessible to your organization. Ontologies play a pivotal role here, connecting disparate sources and formalizing the description of the data held within.

Refining big data into targeted intelligence

Semantic normalization is a key stage in OntoChem’s process of integrating your structured and unstructured data with scientific and industrial literature across the life science industry. Once the fragmented knowledge is combined and homogenized, our platform enables all users to access insights centrally while combining the world’s largest chemical ontologies. 

R&D teams can also tailor their approach from end-to-end, using any combination of internal or publicly available ontologies. Searches can be expanded or refined by ontology-based text, compounds or reactions, helping to locate any associated documents, synonyms, identifiers, properties and parent classes. 

The number of hours saved on searching and interpreting results can run into the hundreds of thousands, improving productivity, enhancing analysis and accelerating time to discovery. As an example, one multinational pharmaceutical organization was able to save more than 800,000 working hours over the course of a year.