AI Case Study
AstraZeneca improves internal management of its global data sources through organisation, search and information extraction using an AI platform
AstraZeneca implemented an AI-powered platform to extract information and automatically classify both its internal and external knowledge resources in order to aid information search and research and development efficiency within the company.
Pharmaceuticals And Biotech
"AstraZeneca's goal was to dramatically impact the time it took scientists to research, aggregate, and understand relevant information related to the company's various biopharmaceutical projects. AstraZeneca realized the need to provide a platform that allowed rapid and easy creation of business applications, in research as well as other departments throughout the organization. The overall requirement for this group was to work with all of the stakeholders to develop and implement the next generation of information discovery processes and systems at AstraZeneca and to do so in a timely manner while delivering ROI and value within the budgeted parameters provided to the group by senior management. These systems needed to handle a wide range of information and document types from a large number of sources such as SharePoint, Office 365, department file shares, EMC Documentum, eRoom, and AstraZeneca's own R&D wiki as well as several sources of large-value external data.
AstraZeneca implemented Sinequa's real-time search engine for AstraZeneca's global R&D. This focused on all scientific information and core internal repositories and supported the creation of multiple search-based applications. Sinequa is a cognitive search and analytics platform that provides relevant insights about information to users in their work environments. This platform discovers new information and hidden relationships and insights and analyzes user behavior and preferences to learn about their work context. Sinequa integrates cognitive capabilities and machine learning to provide real-time, relevant results from unstructured and structured internal and external data."
"AstraZeneca's app store has led to a better functional information discovery system and cross- departmental acceptance and use of better information discovery methods, which has improved its science, R&D productivity, and time to market. Targeted role-based search now enables AstraZeneca to socialize key findings from news and documents but also chatter, applications, people, and scientific tags, helping to connect people together."
"Sinequa's platform supports a wide range of machine learning algorithms including natural language processing, statistical and semantic analysis and a set of scalable machine learning libraries".
"As a large biopharmaceutical company, AstraZeneca faced the challenge of making information discovery an agile process for scientists in the R&D department to find the right information in a timely manner. Accuracy, efficiency, and real-time results and insights were priorities. AstraZeneca understood the need for comprehensive information collection, aggregation, and discovery to accelerate research and innovation."
Wide variety of corporate data and intelligence documents; structured and unstructured data