@EREBUS, @DOCDIVAR: I agree with your statements that the validation is still lacking. To me, we are only at the beginning of the Big Data era and until it fulfills its promises, it will take a while. Looking back into my career, the technologies mentioned in the article referenced by docdivacar were already a topic of discussion and partly in action in the telecom business 15 years ago. The level of big data adoption really depends on the industry you are looking at.
I am currently working for IT solutions in semiconductor (and alike) R&D. Looking there even the collection step is still in its infancy in many organizations. Data is collected in all kinds of data silos (file / MS Sharepoint servers, MS Excel, single purpose databases, …) and cleaning, arranging and analysis is pretty much down to the research engineers. Most of the time even the relations of the data points are not properly archived or data pieces are missing, therefore gaining information from R&D experiments is a challenge. Although solutions for the first steps in your mentioned action sequence are available, people are oftentimes hesitant to adopt and stick with their 25+ years old methodologies.
To me big data does depend as much on the willingness to walk the first steps as on the technology. Without early adopters there is no progress in technology. So beside solving the technological challenge we need to educate people to overcome the inertia of adopting new ways of working and thinking while at the same time not to blindly trust the new technologies alone!
@EREBUS, I agree one needs to understand, analyze, generate usable metrics, provide decision making and prognostics from big data. Much of the noise in big data has been in the collection, storage, access and (some) analytics but the rest are lacking, pitifully!
Readers may be interested in the link on the topic:
Big Data for Smaller Providers –Part 1
For big data to work, you need to understand the data collected, learn to process the data for information, validate the data transformation and then use the data to accurately predict a future event.
I have watched the progress of the technology for the last three decades and I am not convinced that any of the factors I highlighted are ready for real use.
Yes, we are collecting a lot of data, but there is such a rush to show progress that the validation of results is being ignored.
Before Big Data becomes useful, it has to develop a level of trust in its results.
I do not see that happening any time soon.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.