The importance of human-curated data enrichment for big data analysis
Big data analytics is playing an increasingly important role in the advancement of chemical science. With the world’s scientific data increasingly stored in digital format, and data collection accelerating, big data is only going to get bigger. According to IBM Marketing Cloud, 90% of the world’s data was created in the last two years alone.
That’s good news for business and research, particularly in the chemical sciences, where there’s already a well-established framework for publishing and sharing scientific data. With more data comes further real-world intelligence with which to make better decisions, improve outcomes and enrich lives. Of course, to turn this raw data into information, and this information into insight, it’s essential that scientific data is organized, refined and enriched in the right way.
What is big data enrichment?
Data enrichment is all about associating, enhancing and refining the quality and utility of raw data. Effective enrichment goes beyond simply minimizing errors and improving data accuracy. It involves organizing, curating, associating and extrapolating highly complex information repositories, turning vast ‘data lakes’ into organized reservoirs composed of ‘pipelines’ and associated knowledge graphs with underlying ontologies ready for sampling. Ultimately, the goal of enrichment is to drive the discovery of associated clusters, relationships and optimally semantic ontologies within these collections, revealing the new insights necessary to draw conclusions and make truly strategic decisions and potentially informed predictions.
Enriched big data analysis is delivering new insights (and even predicting the future)
The analysis of enriched big data and associated knowledge graphs is helping researchers, entrepreneurs and business leaders make sense of the vast collection of published chemical science data to generate new insights and achieve better outcomes. From papers and patents, to chemical structures and competitor strategies, big data analysis enables users to connect the dots, revealing what’s trending and where the next opportunity might lie.
These tools aren’t simply helping to deliver insights faster – they’re helping to predict the future. The analysis of enriched big data allows entrepreneurs and business innovators to get the inside track on the competitive landscape, evaluate a company’s strengths and weaknesses and inform business strategy. Big data could also let you find the paths to successful commercialization of research earlier than ever before. Equally, it may be possible to identify when the commercial opportunities associated with a particular innovation will peak – based on what’s known today.
The biotech sector is a thriving technology transfer space where the analysis of enriched big data is set to play a key role. In this rapidly expanding field, big data analysis is helping to cluster patent and publication data around categories of biologics, targets, therapeutic indications and manufacturers to understand the competitor landscape and link treatments to opportunities. In turn, this is helping to track where the field is moving, uncover novel research opportunities and help researchers identify the best route to success.
Getting big data enrichment right in scientific fields still requires human intelligence
Enrichment is essential for getting the most out of big data. However, ensuring that the insight is reliable and high-quality has become a challenge considering the sheer volume of scientific data now available.
Scientific data is unique in its complexity. Chemical structures and names, ranged values, and graphs and charts are just a few of the elements of scientific information that make algorithmic structuring and extraction difficult. The quality of the relationships derived from big data repositories ultimately comes down to the robustness of the analytical models used to create them. Today, computational algorithms and statistical analyses are widely used to enrich big data. But despite their importance for data enrichment, neural networks, deep learning models and machine learning tools can only take us so far. The analytical models necessary to obtain useful insight from scientific data are complex and nuanced – and they must be supported by expert insight.
When it comes to interpreting complex studies and finding innovative connections between disparate chemical data, human intellect is still a critical component. Experienced chemists, biochemists and data scientists can analyze data and offer insights that no artificial intelligence system can.
At CAS, hundreds of experts across the chemical science fields carefully curate and enrich scientific information by identifying and collecting key ideas, substances, reactions, properties and much more in published data. These ‘scientists serving science’ read the literature daily, amassing a wealth of knowledge that assists them in uncovering insights and trends not found by technology alone. The resulting high-quality, enriched ‘data lake’, when combined with advanced data analytics tools, plays an increasingly important role in driving business strategy and the commercialization of scientific innovation.
Learn more about how CAS scientists enrich chemical big data and how we can deliver insights to help drive your business forward.
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