Translational research, biomarker discovery, clinical studies and even biobanking have become increasingly data intensive. Generating scientific insights from such disparate “big” data sources across multiple domains is a challenge for both researchers and the informaticians that support them. While data has always been the lifeblood of the scientific method, today’s researchers have access to big data sets from data sources scattered around a complex research ecosystem of internal and external stakeholders. It is a challenge to fully realize the value from such data. While there are many ongoing efforts to develop powerful data mining tools and large-scale databases and analytics, a key step of “data exploration” is often being overlooked.
By data exploration we mean the stage before in-depth statistical analysis and quantitative data mining; it is a stage of hypothesis generation and decision making during which time the researcher takes a high level, probing view of the available data. In this stage researchers may be asking complex questions that involve multiple data sources and a number of different domain experts.
Asking this kind of question is challenging for the following reasons:
In order to make data smarter, data exploration tools need to be placed in the hands of not only the IT and informatics experts, but also the scientists and domain experts that understand and can act on the data, independent of knowing how to write programming code, the structure of data, or even where it is located.
The Qiagram deep collaborative environment provides just such tools, allowing researchers to explore their own data in a collaborative, transparent, and effective manner.
Qiagram is a web-based tool powered by a new type of business intelligence (BI) technology, which replaces programming and other form- based query builders with a visual query “language”.
Qiagram offers the following key benefits for researchers exploring today’s big data sets:
Qiagram’s award-winning technology is domain neutral, and is being applied today in multiple business verticals that need to support collaborative data exploration of big data. Qiagram technology empowers researchers to quickly discover insights from large and complex data sets, which cannot be accomplished by traditional visual analytics tools. Qiagram is proving valuable in next generation biobanking, translational research, biomarker research, and real-world-evidence.