Offline to Online Data Systems
Data is stored in systems that should facilitate our process.The way we conduct work is completely different from what we were used to not ten but five years ago. Data shapes the future of our products.
Customer engagement and time spent interacting with products has become the metrics for success in any online business. I have always been fueled by building efficient systems, as I set out a goal to make people’s lives easier and minimize time spent online.
Prior to working in technology, I was a policy researcher at an immigration law firm for seven years, and the majority of our work was based on paper documents and withstanding long waiting times like telecommunication and network problems with immigration services. Information that is crucial to humanity and yet remains paper-based is a huge problem.
In our current times, keeping up with paper-based structures is nearly impossible. This results in properties getting lost in the process and on a massive scale.
Migrating from an offline to an online infrastructure requires a shift of tools and resources, it can be done, but it takes a ton of human will-power to transform information from an offline to an online base.
I personally thrive on migrating paper to digital systems and figuring out back-end structures and foundations to ensure accuracy and maintain relatability.
One of my favorite books of all time on technology and emerging industries is The Industries of the Future by Alec Ross, who I quote a lot in this blog. Ross explains what goes behind data aggregation and making sense of it all. I like this quote from page 164 on how to deal with data in the future,
As powerful as big data is, there are some things that it does not do well and for which there is little change of meaningful improvement in the foreseeable future. I don’t see any developments in big data that will change the old truism that machines are adept at things humans find difficult (such as working 24 hours straight or quickly solving a complex math problem) and humans are adept at things that machines find difficult (such as creativity or understanding social and cultural context).
New York Times columnist David Brooks has pointed out that data has failed to analyze the social aspects of interaction or to recognize context: “People are really good at telling stories that weave together multiple causes. Data analysis is pretty bad at narrative and emergent thinking, and it cannot match the explanatory suppleness of even a mediocre novel.”
It is also the case that while analyzing ever-larger data sets will produce a larger number of spurious correlations. The larger and more expansive the data sets, the more correlations there are, both spurious and legitimate.
Please take every data set into consideration factoring in the human element that data can’t translate on its own.