Solutions

Clinical Data Conversion (CDISC): Simpler, scalable conversion

Tamr’s CDISC solutions offers a dramatically simpler and scalable way to automatically convert, validate, and package clinical study data in file formats organized according to the latest CDISC standards. By understanding SAS input and output formats, in addition to controlled terminologies, Tamr combines machine learning and human guidance to automatically convert your clinical trial dataset to a specific standard.

For most pharmaceutical companies, submitting clinical study data to the FDA is an expensive and time-consuming process. A study sponsor must collect data from different sources, extract it from proprietary file formats, transform it to conform to CDISC standards, organize metadata describing these transformations, execute validation scripts to ensure data consistency, and convert it all into the file formats required by the FDA for submission.

Typically, the arduous task of aggregating, cleaning, transforming and validating this data falls to teams of contractors or employees using proprietary software and guided by complex conversion standards. When standards are updated or if a submission contains an error, the entire conversion must be restarted.

Tamr’s CDISC solutions offers a dramatically simpler and scalable way to automatically convert, validate, and package clinical study data in file formats organized according to the latest CDISC standards. By understanding SAS input and output formats, in addition to controlled terminologies, Tamr combines machine learning and human guidance to automatically convert your clinical trial dataset to a specific standard.

Benefits:

  • Automatically convert data from the most popular proprietary formats
  • Machine learning + human guidance maps study data to a target CDISC standard
  • Programmatically engage people who generated the data to answer questions

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Tamr’s CDISC solutions offers a dramatically simpler and scalable way to automatically convert, validate, and package clinical study data in file formats organized according to the latest CDISC standards.

Procurement: Fueling optimization through a simplified, unified view

Companies to make large investments in supply chain information systems, very few organizations have been able to leverage all of their valuable but often siloed data.

Procurement analysts need complete, accurate, and actionable information to identify cost-saving opportunities across the enterprise. Yet, as companies continue to make large investments in supply chain information systems, very few organizations have been able to leverage all of their valuable but often siloed data.

Tamr embraces the reality of extreme supplier data variety in the enterprise, enabling analysis of spend opportunities using data generated across all supply chain systems. Machine learning algorithms perform most of the work, unifying up to 90% of supplier, part and site entities. When human intervention is necessary, Tamr generates questions for data experts, aggregates responses, and feeds them back into the system. The result, a simpler, speedier path to analysis and optimization.

Benefits:
  • Get a single unified view of your parts, supplier and transaction data
  • Let machine learning automate 90% of data matching tasks
  • Leverage data expertise in your company to guide data matching

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Customer Data Integration (CDI): Business growth through a consolidated customer view

Customer data is among an enterprise’s most valuable assets, often holding the key to improved sales, retention, and customer service.

Unfortunately, the systems used to capture customer information are often dedicated to single functions or geographies. This creates silos of data that are difficult to integrate cleanly with other sources and a large bottleneck for downstream analytics. Traditional ‘top-down’ approaches of standardizing data quickly become insufficient when dealing with the scale of data variety found in today’s enterprise.

Tamr’s Customer Data Integration solution radically simplifies an enterprise’s construction of a comprehensive, 360-degree view of its customers. Machine learning algorithms automatically unify up to 90% of internal and external customer data. When human intervention is necessary, Tamr generates questions for data experts, aggregates responses, and feeds them back into the system. RESTful APIs then deliver a consolidated view of customer information wherever your analysts need it: from spreadsheets to business intelligence platforms and next generation analytic tools.

Benefits:
  • Use a complete, unified view of internal and external customer data and
  • Discover hidden opportunities to improve upsell / cross-sell, reduce churn, and identify key opinion leaders (KOL) via enhanced segmentation / targeting
  • Let machine learning automate 90% of data matching tasks
  • Leverage expertise in your company to guide data matching

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The systems used to capture customer information are often dedicated to single functions or geographies. This creates silos of data that are difficult to integrate cleanly with other sources and a large bottleneck for downstream analytics.

Media Analytics: Manage data proliferation and drive production decisions



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Increasingly, data analytics is driving production decisions in media, sports, and entertainment. Strategic planning requires industry leaders to develop a single, unified view of any given product or person in order to understand the variables that drive ratings and viewership. Empowered with this information, decision makers fine-tune strategies ranging from ad pricing to movie marketing, script development and talent choices.

Unfortunately, data proliferation is challenging productive analysis. It is particularly difficult for companies to harness the abundance of data within and external to their organization. Products (e.g., TV shows) and people (e.g., actors) are constantly being viewed, reviewed, followed and rated by a multitude of external sources. In addition, manual methods of preparing both external and internal data sources for analytics are slow and not scalable. Teams of data scientists must be dedicated to pulling, combining and cleaning internal data, then integrating third-party sources to enrich that information with social media data, ratings, reviews and other externally created attributes.

Tamr automates the preparation of all enterprise data sources, whether internal or external, to create a complete view of a product or person. Leveraging machine learning, Tamr easily enriches internal data with hundreds of data sources throughout the digital supply chain – from iTunes to Amazon.com, from RottenTomatoes to AllFlicks, from AMC to Fandango.

Tamr’s workflow will:

  • Dramatically enhance time-to-value in preparing data for analytics — reducing the time needed to spin up new analyses from months to merely days or weeks
  • Enable significant scaling with complete accuracy — allowing analysts to analyze all relevant data, not just a small subset, and ultimately leading to better decisions
  • Create repeatability in the analytics process – enabling analysts to answer questions continuously, even as data changes, by building a reusable data infrastructure
  • Reduce the burden on IT and empower your analysts – quickly guiding the matching process without requiring the need to write complex scripts or business logic

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