Take Academic data analysis & interpretation to new heights
Corporate-Academic collaborations are increasingly viewed as a key to unlocking insights and spurring innovation with big data. What’s behind the trend? Such partnerships are a win-win: Companies gain the advantage of top data science minds across multiple disciplines conducting data analysis and interpretation on their behalf, while universities tap critical funding sources at a time when government support of data analysis in research is declining.
Delivering relevant big data into trend analysis tools
Many universities have extraordinary computing power, brilliant academic minds and passionate data scientists at their disposal. What’s missing is relevant machine learning datasets. Nexis® Data as a Service provides much-needed fuel for big data analytics, including:
- Massive amounts of historical news articles and legal publications for training machine learning algorithms to identify trends in media discourse across locations and time periods.
- Current print, broadcast and online news articles, plus social commentary, to use for various data analysis methods that lead to insights into consumer trends and brand strength.
- Industry sources, patents data and legal information to support sector-focused collaborations for statistical experimentation, data analytics and innovative product development.
- Supporting natural language analysis with hedge fund news, company financials, hierarchies and M&A information, and other alternative data for applying predictive analytics to market behaviors and inform investment decisions.
By choosing an experienced data as a service provider like Nexis DaaS, universities benefit from data integrations with flexible APIs that deliver normalized big data in a semi-structured XML format. This enables universities to add bespoke tags and other enrichments to meet the unique requirements of the research being undertaken. Additionally, when retrieving data, searchers can take advantage of our own robust topical classifications to identify and retrieve highly-relevant result datasets, ensuring a faster time to insight.
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