Why UX is The Key to Unlocking the Value of Big Data

If you want to understand the world around you, it’s best to learn the history of what happen up to this point. The greatest technological advancement in human or pre-human history is debatably the wheel. It’s worth recognizing that the wheel is a circle — humans didn’t “invent” the circle, we just made it usable and branded it the wheel. Booyah: innovation!

Fast forward a few hundred thousand laps around our solar system and we had a young Einstein, who wrote theoretical papers on light and radiation, but it took decades for the ideas to come to life as the first functioning laser. Several decades later we have laser scanners, printers and corrective eye surgery. Lasers are now useful for the rest of us, and it only took a lifetime of effort!

There’s a pattern to many of the great technological advancements of our time: a theory is posed, proven and applied to practical situations. Once these real world solutions are created the true value of the theory is realized. It’s no longer just an advancement of science — it’s innovation. Brilliant people can do great things with sophisticated technology, but the world only cares when solutions are identified and offer benefits for everyone else.

This is illustrated undeniably well by the advent of computers themselves. For years, computers were the domain of the academics, the scientists, the elite thinkers of our world. These people would schedule a time to visit a building housing a computer, there they could input their work, wait, and wait and eventually study the output. Companies popped up to first try to make computers smaller and more convenient, faster and more powerful — they were working on making them usable. In 1984, the world was presented with Steve Jobs‘ best-stolen idea: the point and click interface. That is when the user experience of computer technology entered the realm of possibility for the rest of us to use.

This same pattern can be seen with the advent of networked computers and data packet transfers. All fun and games for the very smart people who worked on these challenges to eventually build the internet. Give them a decade or two and TCP/IP leads the way for email, instant messaging the world wide web: the real usable layer of the internet. Now that the internet is something usable for the rest of us we get Google, eBay, Amazon and Facebook.

With everyone using the internet for practically everything, we’ve been creating data at an exponential rate. Advancements in computer hardware and cloud technology have given rise the ability to store and access massive amounts of data collected. It is collected from just about every piece of online communication, electronic interaction, e-commerce transaction, social media tweet and status update from almost half the world’s population. That’s an overwhelming amount of data. It’s easy to think that this amount of data is useful to have, like having all the books in the library of congress. But simply having all the books in a library doesn’t make you smarter — what you should be after is all the knowledge contained in the library.

Machine learning applied to big data will help gain that collective knowledge. Applying data modelling techniques further to understand the data will help to realize that value. These endeavours are being perused every day by data scientists, machine learning experts and engineers. It takes a vision to understand that we’re at the beginning of another technology innovation path — one that will be taken for granted years from now but for the time being the trail is still being blazed. The industry is ripe for forward thinking companies and clients to put their combined efforts into creating a user experience that allows real practical solutions to be developed utilizing these new big data efforts.

Companies like Rubikloud are bringing together these skillsets with retail experts, business analysts and user experience designers to bridge the gap from big data to real life problems in the retail space. We’re applying our collective knowledge to create products and platforms that make big data usable for the rest of us.


Originally published at rubikloud.com on October 16, 2015.