Competitive-Intelligence-Teams besseren Zugang zu wissenschaftlicher Literatur ermöglichen

Das manuelle Durchsuchen riesiger Mengen wissenschaftlicher Literatur und Daten ist eine Herausforderung für fast jeden Bereich einer forschungs- und entwicklungsintensiven Organisation. Entgangene relevante Informationen können extrem kostspielige Auswirkungen haben, insbesondere für Competitive-Intelligence-Teams, die für das Verständnis der Marktumgebung zuständig sind, da die Markteinführungsstrategie des Unternehmens unmittelbar dadurch bestimmt wird.

Wenn Sie über Ihre Strategie für eine kluge Wettbewerbs- und Konkurrenzanalyse nachdenken, sollten Sie die folgenden Fragen berücksichtigen:

Weiter geht es hier mit dem englischen Originalbeitrag, der zuerst im Blog unseres Mutterunternehmens Copyright Clearence Center veröffentlicht wurde.

Do we have a way to monitor competitive intel in a collaborative way? 

When articles, press releases, journals and other data are stored in different systems and locations, you can’t be sure everyone charged with monitoring competitive intel has access and rights to share that content. We often hear from organizations where employees are storing content on their individual hard drives, making it very difficult to collaborate on new materials coming in.

Using one convenient workflow to purchase, organize, and collaborate on content can provide competitive intelligence teams the tool they need to collaborate and share information. With a shared library tool, users can create libraries for approved team members where all information related to a specific product, therapeutic, or topic of research can not only be accessed, but also annotated and highlighted. Automated alerts can also be set up to help individuals or teams receive the most relevant information about their focus area without having to search for it.

Do we have a platform to search across data sources in aggregate?  

The ability to access data such as clinical trial information, drug and device patents, sales forecasts, scholarly articles, poster presentations, and more, is critical to gain a well-rounded perspective on the market.

When employees must consult multiple sources to find all the data they need, it’s a massive manual undertaking.  By bringing together data from publicly available sources with licensed content and internal proprietary data into a single interface, connecting the dots within competitive intelligence information becomes an intuitive process instead of a manual one.

This is also an area where knowledge graphs can play a significant role. An excerpt from How Knowledge Graphs Solve a Diverse Set of Business Problems provides the following example:

“A group working in business intelligence might be interested in which drugs are being developed to treat a particular disease. This could require linking across scientific and medical literature as well as clinical trials databases. Going further, a full business intelligence landscape might draw from many more sources. Drug pipeline and clinical trials data can tell a team much about what competitors are investing in. Finding links between those pipelines and mergers and acquisitions from SEC filings, industry news, and patent filings can give further clues. It’s not hard to see how a complex graph of disparate information sources with information coming in multiple shapes and sizes can be cross-referenced to gain extraordinary insight.”-referenced to gain extraordinary insight.”

Do we have the tools to synthesize large amounts of data? 

Bringing together multiple internal and external data sources can potentially mean information overload. But it doesn’t have to be overwhelming with advanced capabilities like natural language processing and semantic enrichment tools that help synthesize and make sense of that data.

Text mining can enable competitive intelligence teams to automatically analyze massive amounts of information quickly to extract data, assertions, and facts from unstructured text sources specific to a particular research topic.

For example, our partners at Linguamatics describe how one top pharma company utilized text mining to extract, normalize, and visualize data to inform their commercial strategy:

“The company used structured data from scientific journals, abstracts, and conferences to generate a comprehensive understanding of the market ‘gaps’ they could address. Focusing on a particular therapeutic area of immunological diseases, the organization was able to develop precise searches with increased recall across these different data sources.”

And when it comes to semantic enrichment, consider competitor patent filings. They can be explored alongside non-patent literature (NPL), enriched using the same vocabularies, to provide a fuller picture of competitor strategy, claims, and prior art for patent landscaping or other purposes.