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www.privateairny.com Private Air | Spring 2021 42 BUSINESS "To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain. ere are a few key ways to implement and elevate machine intelligence so that it's far smarter, faster, and more accountable than protocols past," said Microsoft alum Irfan Khan, founder, and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization. Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability. Manifesting New Market Opportunities Today's machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze images, spoken, or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. is generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus. Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course, patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. is includes determining that a certain drug is only effective on women between certain ages—critical insights for pharmaceutical developers and physicians. Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, resulting in the resulting solutions, discoveries, and profit- producing results. Reconcile Emotions with Actualities Data generates value, which leads to the generation of money. It's that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. ere existed very rudimentary tools like regression and correlation, but today's analytics call for gaining a proper understanding of what extracted data means. How do you convert data into a story you can tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking while also prompting them to ask new questions. So, garnering meaning out of a company's data provides tremendous advantages. "Human nature is such that unless we can see it, touch it feel it, it's hard to understand it," Khan says. "We as data scientists haven't done a really great job of explaining AI- driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies." Scale statistical models for actionable models We often separate our data as factual, asserting "this is what happened." Neural networks connect the "human decision-making process" to those factual—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors, and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike. Technology is now allowing us to understand trends on a factual level and then project outward. Some companies use this key learning to project whether or not a person is likely to suffer a specific affliction in the health realm. It's also allowing for far more efficacious "if this then what?" scenarios. If a diabetic person takes insulin, controls their diet, then treatment protocol will change. is is enabling highly personalized medicine. But the same processes, principles, and benefits hold in non-health categories as well— encompassing all industries across the board. Future-proof, Anti-fragile Data Supply Chains From data connectors to pipelines, data lakes to statistical models, AI to Quantum, visual storyboards to data-driven automation, ML to NLP to Neural Networks, and more, there are highly effective methods for future-proofing your data value chain. e data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights.

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