• uniquely enabling value
    based data navigation


e[discover] objectively and statistically measures the value of data assets as defined by their usefulness and relevance. The approach is based on decision theory and exploits quantitative and probabilistic techniques to enable a unique conversational/question driven approach, allowing scientists to explore scientific value across diverse data sets as never previously possible.

Winning Innovation

e[discover] won the prestigious Best of Show Award at Bio-IT World Conference & Expo 2016. Find out more by watching this award video. This features an interview with Eagle Genomics’ CEO, Abel Ureta-Vidal and VP Raminderpal Singh.

How it works

e[discover] is a first-generation tool capable of objectively assessing data value (as opposed to “quality” or monetary value) of scientific datasets using Decision Theory techniques. A hierarchical model is used to match datasets (value components) to the question posed by the data scientist. Once modelling is complete, e[discover] allows the user to apply value to the catalogue, discover relationships between components contributing to the value, and to further refine the model.

Business Benefits

Guides information management strategies

By providing users with prioritized data based on scientific value, e[discover] (working in conjunction with e[catalog]) improves data selection, encourages data reuse and informs experimental programs.

Breaks the loop of data-analysis paralysis

e[discover] addresses the challenge where too much time is spent on data wrangling to the detriment of scientific interpretation thus breaking the loop of data-paralysis.

Capture tacit knowledge

The ability to capture and use tacit knowledge in a scientific or business decision context increases ROI on acquired knowledge across multiple stages of product development

Closes the Design of Experiments Loop

Valuation of data within a internally coherent framework is bound to support improved experimental design. Low value (as opposed to only low quality) experiments can be easily flagged and drive leaner more informative experiments.

Module Features

Unique question-driven approach »  allows scientists to explore datasets using validated scientific queries.

Objective guide to data curation and enhancement »

Data selection by prioritization (scoring) » not simply down-selection (filtering).

Hierarchical search & discovery visualization »

Enterprise integration with proprietary or public data sources » 

Translation of tacit knowledge to a business experimental process » subject to PDSA learning cycle

Conversational environment (human-machine & human-human) » for refining data value model

Our Smart Data Management Platform

e[discover] is part of our proprietary software suite that bridges the entire process from the medical data through to insight.