Big Data is a term one hears quite often these days. Look at the Big Data trend on Google to validate just how much it’s trending:
So it comes as no surprise when I say that many people and vendors are experimenting with Big Data. I was recently reading an interesting article by David Linthicum titled, Big data in the cloud: It’s time to experiment. David gives various Big Data examples that include a manufacturing company amongst others. The following passage is the most interesting since it defines the core of the problem: consolidating multiple data sources.
The art of big data is that it consolidates many types of data resources with different structures and data models, all in a massive, distributed storage system. Big data systems may not enforce a structure, though structure can be layered into the data after the migration. But there are trade-offs in going this route, including migrating unneeded and redundant data that takes up space in the big data system.
Inforbix is all about data; it’s in our DNA. The consolidation of multiple data sources together was the first challenge we decided to tackle. Engineering and manufacturing data (the data that is located in “zillons” of CAD files, excels and enterprise database) presents Big Data related challenges due to two factors: rich semantics and high level diversity. Inforbix addresses these two challenges by applying semantic technology combined with noSQL data storage and a cloud infrastructure delivery mechanism.
And there’s one more way Inforbix address Big Data challenges. We provide simple yet effective tools which we call product data apps that can be used by anyone (no special skill or training is required) in the company to research, search, expose, and slice & dice data in meaningful and useful ways. Here’s a screen shot of Tables and Charts:
Conclusion. Inforbix is experimenting with Big Data approaches that help people in manufacturing companies re-think the way they work with data. Exposing and making data accessible, we believe, will open new possibilities which lead to improved efficiency and leaner product development. Has your company started thinking how to tackle Big Data?