Is Poor Data Management Holding Back Your Scientific Success?

Is Poor Data Management Holding Back Your Scientific Success?

Feb 22, 2024

Industry Insights

It can be costly to obtain and manage data. In a 2023 presentation, asphalt manufacturer, Ingevity, revealed that it cost $75,000 to collect emissions data from three manufacturing plants. This data enabled their staff to predict how temperature might lower emissions using machine learning.

In this and other examples of good data management, it's clear that well-managed data opens innovation. However, failing to properly obtain and manage data often spells trouble.

Think of the last time you wanted to analyze data to answer a question in your R&D and manufacturing. 

  • Was collecting data an easy task, or did you encounter obstacles such as needing to obtain funding for a sampling plan?  

  • When sourcing data from a colleague's previous work, was the data easy to obtain, or did it require a long series of emails?  

  • Was the data in proper format? Did it require cleaning and transforming?  

  • Was all the data you required available, or had some data gone missing? 

Problems in data management spell trouble for any effort to analyze that data, including machine learning and AI technologies. 

The fix? CoBaseKRM

Whether startups or mass enterprises, success starts with unifying staff to one, central method of capturing and managing scientific data. The method should also help researchers save time by automating data governance rules they would have to enforce on their own.

Our solution, CoBaseKRM, is a simple, attainable solution for anyone who wants to manage their research data.

KRM at a glance

KRM or "Knowledge Relationship Management" is a simple idea: Empower teams of all sizes capture and interconnect their data, analytical work, and staff thinking with a final innovation, solution, or piece of IP. 

Doing so, two outcomes become possible:  

  • Staff produce easily trackable knowledge assets that can be found and redeployed inside the organization for years to come  

  • Scientific employees accelerate their work and unlock innovations that may not have been possible otherwise

How does CoBaseKRM meet such aims? A few key features (among others) make it all possible: 

Export and import standardized data 

KRM allows staff to build data collection templates and export them to Excel and other spreadsheet apps. The exported spreadsheet contains a data table with all of the parameters and units you choose. What next? Assign the spreadsheet to a staff member, such as a technician and have them perform an experimental protocol, filling in the data sheet along the way. 

With standard naming, technicians can approach the lab, collect data, and reupload data into KRM. A validation step ensures data in spreadsheets matches the standards you established in CoBaseKRM. 

Because this workflow uses common tools like spreadsheets, it's available to all regardless of funding or budget.  

Advanced search

Scientists can dig through all projects for parameters of interest, enabling them to reuse learnings across initiatives with ease. Effortlessly navigate through a wealth of insights, locating essential information across projects in seconds. Get granular, searching down to a parameter or individual data point. This means less time searching and more time solving problems. 

Version control & audit 

Record data versions for every user action or change. Maintain and track distinct versions of all components of knowledge. Know when and how data was changed in your system, ensuring data integrity.  

Poor data management has nowhere to run

With simple, affordable tools that can get datasets in order, we believe that a fundamental problem in scientific research may finally meet its end.

If concerns around data quality, consistency or completeness are limiting your scientific initiatives, don’t settle for the status quo.

Try CoBaseKRM and reshape how your teams gather, organize and distribute data and knowledge.

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