R&D 2.0: The Right R&D Informatics System
Posted June 18, 2013 & filed under Job Search
Is the R&D process too disjointed? Some might say yes. In a recent whitepaper from IDC Manufacturing about 56% of companies deemed their R&D efforts to be marginally effective at best with only 25% achieving an actual market release. When researchers try to analyze petabytes of data cached across incompatible systems, low percentages aren’t quite so hard to believe. New releases are changing the way data is organized, accessed, and analyzed, so the question is do we finally have the right R&D informatics systems solutions?
To start, let’s look at the root cause of this R&D problem.
Mountains of Data
Research & experimentation are reliant upon the ultimate amount of collected data. With promethean jumps in storage size, entire mountain ranges of information can be stored on hand for a single laboratory. One database in a modern laboratory can easily contain petabytes of information. Now imagine that you have 5 to 15 incongruous database systems of that size (or greater) and you have the endemic problem facing R&D departments.
In an article for Plastics Today, Senior Director Ted Pawela of Accelrys further discusses this problem in depth. He says when R&D departments are lacking a unified informatics system, there is no single person who fully comprehends the big picture. Parsing through this information across different systems wastes time and diminishes the scientific return. Worst case scenario, he says, “is that they repeat experiments that they ran before.” So, needless to say, an informatics systems that can better organize the process would do wonders for efficiency.
The Right Big Data Tool
As storage size increases, more big data tools are released to corral stubborn data sets into tidy, cohesive pens. Hadoop, NoSQL, and other tools can help ease the complexity of analyzing data but Accelyrs’ Experiment Knowledge Base may fully provide the R&D industry what it needs. Designed for the R&D industry, the Experiment Knowledge Base (EKB) can transform massive amounts of data faster than traditional programs, ensuring more efficient product innovation.
Regardless of the database source, the EKB can run the data through flawless ETL processes. It can extract information from dense, incompatible data sources, translate them into effable concepts, and load them into a uniform platform where trends can be analyzed and conclusions can come to light.
Early adapters are already beginning to see success. R&D informatics systems like the EKB will help eliminate rehashed experiments and any sort of redundancy across the board. As these systems begin to pick up momentum, even more alternatives will hit the market, adding the operational capabilities that will take R&D projects into more & more fruitful endeavors.
by James Walsh
