Data Value at the Edge

The Edge, while frequently discussed as something new, is in fact another technical turn of the crank. Fueled by an abundance of smart devices and IoT sensors, worldwide data creation has been growing exponentially, driving our customers and partners to innovate. For example, between 2016 and 2018, there was an 878% growth in healthcare and life science data resulting in over 8 petabytes of data managed by providers per annum. Dell Technologies has been at the forefront of this data revolution enabling our customers and partners to leverage these new sources of data to drive business. The process of data creation, transformation and consumption has taken on new meaning as devices have become more integrated in our everyday lives. How this data lifecycle adds value to our customers and partners is the subject of our post today.

Data Creation

“Data is fuel.” We’ve heard this spoken time and time again. While that’s true – it doesn’t convey the process the data undergoes, before it become something useful. “Data is fuel” is the net result of this process, not the genesis.

So, how do we get to this final, consumptive state with data? Data Creation is a constantly evolving mechanism driven by innovation, both in technology as well as in society. For example, the idea of remote patient monitoring has evolved, enabled by complementary technologies like 5G networks and IoT sensors. The ability for health care providers to securely retrieve data from smart watches, pacemakers, blood pressure cuffs, temperature sensors, electrocardiograms and insulin pumps (to name just a few) has driven a new paradigm of patient care and engagement. This wouldn’t have been possible a few decades in the past and, due to innovative approaches in networks, data management, and sensors, it represents one of many unique applications of the data creation process. Once this data is created, however, it must be transformed to be useful.

Data Transformation

Using the example of remote patient monitoring, the data generated by various sensors is unique. It has no intrinsic value as a “raw” data stream. Binary bits of encoded data provide no context, no perspective on what is happening with a patient. To fully understand, contextualize and derive useful consumptive value, it must be transformed. This transformation process extracts information, correlates and curates it through applications like artificial intelligence and analytics and provides it back in a human and machine-readable format. 1’s and 0’s become more than their sum and now, as transformed data, they’re ready to be consumed. Continuing with the patient monitoring example, the doctor receiving this information is then able to correlate and analyze these data feeds from a variety of sensors and sources and view them with an eye toward application. Recently, a Dell Technologies customer was able to increase their analyst-to-support staff ratio by greater than 100:1, enabling them leverage this data transformation to achieve better performance. As we’ve now seen, data is now one step closer to being fuel.

Data Consumption

By now, data has been created in various modalities, transformed by analytics and artificial intelligence and is ready to be consumed. Consuming data is more than just visualizing an output; it is the action. Our doctor has received remote patient data, securely viewed the correlated results and is now ready to provide diagnosis. The diagnosis is the net result of this generative (reproductive) model. Rather than being static or one-time-use, data consumption has taken on new meaning. Broadening this example, doctors use data to predict how to better counteract and treat disease. Machine learning models consume training data to learn to take future action and to create and transform the outputs into new capabilities. Manufacturers view vehicle data in extended reality (XR), peeling apart systems to be able to experience the real-time interactions between components. This generative cycle continues to evolve as technology advances, making the most of data’s kinetic energy.