Collaborative Scientific Intelligence for the Big Data Era
The Challenges of Data Exploration
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.
The Need to Ask Complex Questions
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:
- Database programming is not a common skill set among researchers
- There is often no shared mechanism or language to support collaboration
- The lack of access to data, while maintaining regulatory compliance
Deep Collaboration Empowers Multidisciplinary Research
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.
Why Choose Qiagram For Your Data Exploration Needs?
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:
- Easy Access to Dynamic Data: With Qiagram, there is no need to understand the underlying data model or database concepts, as it guides the user through an exploratory process to get to the answers. Qiagram can also quickly connect with new data sources in order to support questions being asked.
- Intuitive Tools: Qiagram replaces programming and other form-based query builders with a visual query “language”. Users simply “draw” their question on a web-browser canvas, building it up step-by-step. As each step is drawn, the users can preview the answers to their questions interactively, allowing rapid refinement of the query. No knowledge of programming is required.
- A secure, holistic view of data: Qiagram allows the user to query across multiple domains in a simple, secure interface. All the stakeholders in the research ecosystem can determine what data will be shared and with whom.
- Collaboration: All queries developed using Qiagram can be shared with any of the researchers and informaticians involved in a project. This visual environment provides unprecedented transparency of the research group’s thought processes. Once the query is ready for use, it can be built into a dashboard and consumed by anyone with appropriate security access.
Who Uses Qiagram?
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.