Dotmatics for Data Intelligence

Data intelligence from Dotmatics

As R&D teams are pressed to accelerate the pace of innovation while facing stagnant or shrinking budgets, the promise of data-centric R&D models seem irrefutable. Whether organizations are aiming to increase the reuse of existing knowledge, implement data-driven decision making, or are working towards new R&D models based on artificial intelligence (AI) / machine learning (ML), two prerequisites are often neglected: Data needs to be readily available and the context of these data needs to be understood.

Data together with context is data intelligence, and therefore should be viewed as foundational for any data-centric R&D effort.

Data intelligence diagram b
Example workflow configuration for chemicals & materials R&D. 

Data Contextualization via Workflows and Roles

Why is it so difficult to gain value from R&D data? After all, many organizations in life sciences and chemicals/materials research have invested in digital infrastructure designed to capture and manage R&D data. However when it comes to retrieving data out, the situation is often less than ideal, because the wider experimental context is lost.

Dotmatics resolves this challenge with a unified, data-centric platform approach that integrates the different data types which make up the experimental fabric. This capability also allows tailoring of permissions, based on roles For example, lab technicians may only see the data relevant to a specific test, while scientists can analyze the totality of data that makes up a specific experiment.

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Data Queries for Complex Experiments

Given access to all data connected to a given experiment, scientists can explore data in ways that they otherwise couldn’t. Dotmatics experimental workflows are configured to include all relevant data in one place without overburdening the user with data that aren’t relevant to their analysis.

Whether experiments are highly standardized, such as antibody screening workflows, or highly diverse such as in materials science applications, Dotmatics provides a flexible framework to give a holistic view onto your research data, allowing you to cross reference chemistry, biology, formulation, process and analytical/physical characterization data.

advanced knowledge extraction

Advanced Knowledge Extraction

While it is incredibly powerful being able to access all relevant data for an experiment in one place, Dotmatics can take advanced knowledge extraction one step further. Oftentimes, researchers need their analyses to span series of experiments, whether to analyze a multi-step reaction with all their intermediates, or to understand how a series of experiments resulted in a successful outcome. Or, when project requirements are close to prior work, relevant experiments need to be aggregated to create the best possible starting point. Without Dotmatics’ data intelligence solutions these efforts often become insurmountable, resulting in the duplication of costly experimentation.

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Data Visualization and Decision Support

Data visualization is a key activity within data intelligence: the process of turning data into knowledge. Unlike others who see data visualization as an endpoint, Dotmatics’ Vortex is an integral part of the data lifecycle, allowing scientists to feed their insights back into the platform. This enables decision support or creates the basis of new projects. Or, selections of relevant experiments within the design space may be fed into other applications, creating an open environment for innovation.

Data intelligence diagram b

Integration Flexibility and Open Platform

The Dotmatics unified platform can be deployed either standalone or as a solution to integrate various transactional systems (e.g. LIMS and/or ELN) to help you turn your research data into valuable assets to accelerate innovation. Integrations can be implemented to tie various data silos together or to span a variety of disciplines (e.g. chemical synthesis, formulations, process and analytical/physical characterization). Other integrations at the application level allow for the streamlining of workflows (e.g. for instrument data acquisition and preprocessing).

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