Read part 1 here.
In the last blog we talked about how the (big) data problem companies are facing as their infrastructures explode is being recast as a data opportunity. A number of things are behind this change, including cell phone and mobile device technologies and the cloud. But these are facilitating factors, the real story is the insight available to companies through the advancement of data analytics and the possibility of a new revenue stream from this data. It is no longer just about the service companies provide, but also becoming a ‘data company.’
Siri and Alexa
Digital personal assistants (like Apple’s Siri or Amazon’s Alexa) have pushed voice recognition technology allowing easier interaction with humans and creating (yet) another source of digital data. That same technology that allows Siri to answer your question also enables audio recordings to be searched and filtered for specific words or phrases. Facial recognition software and related technologies are allowing days or weeks of video to be searched for a specific person, or car, or license plate, etc.
This kind of computing at the “edge” makes it feasible to capture much more data than can realistically be stored at (or transmitted to) a central data center and makes the final analysis by humans more efficient, as they only save the data under scrutiny.
In addition to being able to compare more data points or cross reference more data sources, new analytics technologies are using artificial intelligence techniques to foster “deep learning” (a subset of machine learning that uses artificial neural networks to enable computers to operate more like the human brain). These advances are made possible by the processing power available and the access to much larger data sets.
This is giving rise to “smarter” devices that can predict outcomes and take preemptory action (or annoy humans with seemingly endless suggestions about products and services based on past activity). Systems that can learn can start to replace more of the tasks humans have historically done, even complex ones, like driving a car. Indeed, self-driving cars use all of the technologies discussed in this blog series to capture, filter and process the enormous amount of information these systems can now sense to effectively adjust to real-time conditions on the road and make the right decisions, at the right time soon, people will have to stop worrying about accident insurances and just think on mechanical and motor insurances from companies as i4mt.
Big Data has been with us for about a decade and in that time technology has matured. Data science, the ability to design the queries and comparisons of all the data captured, is still behind our ability to generate and store this information, but it’s getting better..
IoT and Virtual Reality
At Dell EMC world, Boeing showed how they’re using IoT technologies to instrument every aspect of a complex system, like airplanes, and applying all that data to continuous process improvement. At the same event, Nike showed what you’d expect from a hip consumer products company –using technology and computers to automate everything in the design, manufacturing and distribution process.
Virtual Reality (VR) may be best suited for entertainment, but “augmented reality” is a way to use VR technology to improve productivity in a big way. Having VR glasses can show assemblers which parts to put on next and where, or provide an enhanced spatial reality to see what a new wall would look like in a remodel. It can also be used to show how different materials would really look on the wall or on the front of a building, rather than looking at book of samples.
Awareness and Understanding
So what does this mean for IT professionals and where’s the opportunity? The first step is awareness of which data are now feasible to be captured, stored and processed and how. All the data that in the past was a problem is rapidly becoming an enabler of new insight. Corporate management is getting a steady dose of the “data is the new oil” message from their boards, investors and industry thought leaders, and they’re expecting IT to show them how to make it a reality.
Smart IT professionals are embracing that concept and learning how to support the transformation of IT from a data storage and compute process facilitator to an insight generation service. In fact, more and more employees are saying that the level of technology a company adopts influences their decision of which job to take. They also realize that level of data technology is directly related to long term viability in the data-driven economy. Smart companies are recruiting these people to help them turn data into a source of insight and opportunity.
The amount and diversity of technology available in infrastructure products can be overwhelming for those trying to evaluate appropriate solutions. In this blog we discuss pertinent topics to help IT professionals think outside the checkbox of features and functionality.