Scientific Data is Different
Scientific innovation starts with data, and research labs are sitting on an untapped goldmine. Data need to be in the hands of scientists to allow for rapid iteration and discovery. However R&D data is different, making unlocking its value difficult.
In R&D, working with data is much more labor-intensive than other parts of the business because the data are complex, multimodal, and from many different sources. Scientific data includes images, time series, tables, machine-generated binary files, electronic lab notes, and more. It is continuously being generated in the laboratory by instrument machines that are used in experiments. Scientists run computational workflows that process raw data and generate new data. Some data is under management and some is not. Before performing any analysis, the right pieces of data need to be curated in one place.
What is Analysis-Ready Data?
Analysis-ready data is highly curated and highly available data that support specific analytical use cases. Starting with analysis-ready data accelerates the users by significantly reducing the time that they otherwise had to spend on preparing data for analysis.
In labs lacking the technical infrastructure to readily make their data analysis-ready, scientists and engineers are unnecessarily burdened with spending too much time wrangling their data instead of using it. They spend the majority of their time on trying to access data that are locked in data warehouses, data lakes, or other data management systems; setting up their computational environment; waiting for computing resources to be approved or become available; and figuring out how to share their work with colleagues for feedback or get into production.
Data Tools Purpose-Built for Scientific Research
Many science-driven companies focus purely on the organizational structure of their R&D data. Most find however that data stored and newly organized does not mean it’s more usable to accomplish their research goals. Enthought Edge is a cloud-native platform purpose-built for iterative scientific research, where scientists can easily access their data in analysis-ready form—in and from one environment, secure behind the organization's firewall.
Enthought Edge saves scientists up to 80% of their time by automating data compilation, accelerating data analysis by 10x, and optimizing workflows to achieve a 3x ROI within 2 years.
Edge empowers scientists to take control of their data and accelerate their research.
Unlock the Value of R&D Data with Enthought Edge
While the value of R&D data is clear, finding a way to sort through it can be daunting given the special handling required to extract its value. In fact, 75 percent of surveyed R&D executives believe advanced analytics techniques would play a pivotal role in their future R&D activities, but only 25 percent state that their R&D organizations were actually using these analytics.
General-purpose data management solutions are ill-equipped to confront the challenges of R&D data, leaving researchers to manage time-intensive and/or manual processes that may or may not yield results. That’s where Enthought Edge steps in.
Have a question? Check out our Knowledge Base for FAQs, announcements and more resources.
The Enthought Tool Suite is a collection of open-source components developed by Enthought, our partners and the scientific Python community, which we use every day to construct custom scientific applications.
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Vice President, Digital Transformation Solutions
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