It seems like only a few years ago that managing data growth was a high-priority objective for most users. What happened? The short answer is the same thing that happened to oil. Data is the new oil.
Before we learned how to use petroleum-based products effectively, oil was little more than a pollutant. It killed crops, poisoned animals and made land worthless. Then we learned how to lower the cost of extracting, refining it into kerosene for lighting, gasoline for cars and planes, and lubricants. Costs dropped, a competitive market evolved, and oil became the new gold.
That paradigm is now repeating itself with data. We have lowered the cost of storing, managing, and using data to the point where economics and the size of the problems we are tackling have made it an indispensable tool. Today simulations/modeling save engineering firms time and money by enabling engineers to explore innovative alternatives, optimize designs, and lower costs before committing a design to prototyping or manufacturing. For example, changing the logic in an ASIC is expensive in time and money because the chip layout has to change, new masks have to be created, and then the semiconductor vendor has to build and package the new ASICS. This can take months and can result in schedule slippages and cost overruns. Simulating an ASIC’s logic typically takes hours and doesn’t involve any other expenses except simulator time.
This disruptive lowering of costs has lowered the cost of building fraud detection systems that work in near real time. Stated differently, lowering the cost of fraud detection has lowered the threshold of acceptable fraud levels. Ditto the use of analytics and AI to build better decision support systems. In insurance the democratization of data has enabled AI to improve claim approvals and identify new market opportunities. Auto insurance companies are now offering lower rates to customers that allow them to monitor their driving patterns….