R&D in life science, material science, and chemistry is burdened by fragmented and unstructured data (“spreadsheet hell”), and ambiguous or unrecorded methodological data. This leads to low-confidence decisions, wasteful rework, missed observations, and delayed or failed technology transfer across sites or scales of development.

“Data science” and “big data” tools are often hailed as a solution to these problems.  But statisticians and data scientists spend more time gathering, cleaning, and linking data than actually learning from it. The ability to “clean-up” data is impaired by the dynamic nature of R&D processes, the heterogeneity of data sources, and the complexity of modern R&D organizations.

Riffyn’s cloud-based software platform uses visual process design and integrated data analytics to solve these problems. Riffyn collects data from people, instruments and databases, and uses the process design to contextualize the data the moment it is generated. It then uses the process design to automatically summarize and join all that data into standard tables for rich statistical analysis, visualization and machine learning — allowing users to instantly answer questions of cause-and-effect across traditional silos of team, place and discipline.  Thus by simply creating a process and experiment design with Riffyn, scientists and engineers have effectively architected their own vertically-integrated data system (front end, database, & ETL) in less time than it would have taken to communicate their data requirements to IT.

With built-in versioning, Riffyn is also continuously adapting — so that laboratory methods, data and, analysis are always in sync with ever-changing R&D processes.  This frees each organization’s IT team to attack the backlog of high-value analytical and business intelligence projects that are usually sidelined by the heavy burden of core system maintenance. No longer will database development or data integration be a drain of precious IT resources, nor a bottleneck to scientists and engineers who are trying to improve their R&D processes.

The result is a new level of transparency and confidence in R&D decisions, a culture of continuous improvement, and corresponding gains in R&D effectiveness.