In order to create digital value, we have to understand how data is structured and how it moves from system to system. Otherwise we can't figure out if it can or can't be monetized, and if it's ethical.
The first stage is data acquisition—data is collected from sensors, systems, and humans.
The next stage of the data supply chain is storage—recording data to a trusted location, which is both secure and easily accessible for further manipulation. Storage is often some version of the cloud, or perhaps a specific server. Sometimes it’s a flash drive or local memory on a sensor. But wherever we store data, we need to make sure that we understand how that's going to connect to other systems.
When we aggregate data, we combine disparate data sets to create a larger data set that's greater than the sum of its parts (often referred to as Big Data). This is where exponential possibilities begin, but so does exponential complexity.
Once data is collected, stored, and aggregated it’s time to analyze it. We examine data to extract information and discover new insights. We might append this analysis to the original data, and/or use it to discard extraneous data.
Apply the insights gained from data analysis and use it to make better decisions, which effect change or otherwise help you deliver a product or service. This stage may also involve changing or adding to the data.
During the share or sell stage we send data—or insights gleaned from data—back to its source or to third parties. There are significant ethical considerations to take into account in this stage.
Disposal of data is an important consideration at the end of the data supply chain. It’s never the most exciting, and it doesn’t generate any immediate value, so it’s often overlooked. However, regulatory requirements and common decency both demand that we think through how data will be disposed of when it’s no longer useful.
