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What the vaccine rollout can teach us about big data and AI


I have spent my entire career looking at data and the world through the lens of science. Perhaps that is why, while observing vaccine launches, we have found interesting links between the challenges overcome by governments and scientists and those faced by most companies.

Looking at this comparison, he next discovers four technology takeaways for companies looking to innovate with data-driven big data and AI.

Train your employees to trust science

Given the massive campaigns and mass education that have helped build trust in the science behind vaccines, the same must be happening with the company. Employees tend to reject AI based on anecdotal evidence. They tend to follow their own biases rather than let AI and predictive modeling do the work. This is often the case with salespeople as well. They realize their AI technology was off once or twice and abandon science altogether. Unfortunately, this can hinder or negate a company’s go-to-market strategy. Therefore, companies should train their employees to use technology and data, not against it.

Employees can take a scientific look at AI, analyze its risk-return effectiveness against benchmarks and metrics across the pipeline, and see how AI is impacting the organization as a whole rather than individuals.

Must learn to check AI can be powerful even in a world with limited sample sizes

Similar to vaccine development, large companies are also limited to small datasets and short time periods. They can’t afford to run multiple tests or conduct years of research and experimentation. Unfortunately, neither COVID-19 nor business customers have that patience.

I work in his B2B world and the amount of data in this industry is a fraction of his B2C. If a company only has a few dozen customers, it wants to use AI to find more customers. AI can be equally powerful on small datasets and short-term processing if you use the right methods, such as choosing the right benchmarks, accurate A/B testing, and ingesting additional data from outside your organization. Become.

Be proactive with your schedule

Every company I’ve worked with, regardless of industry, considered their goals to be “high risk”. Although the risks are not as high as vaccine development, these companies still risk millions of dollars and solve high-risk business problems. That’s why I encourage them to be proactive.

During the pandemic, I have worked closely with one company whose demand has increased tenfold due to the nature of their business and the current needs of the world. Prior to the pandemic, we used manual solutions, but given the ‘high stakes’ and huge opportunities, we couldn’t afford to give up on deploying advanced AI technologies.

Not only did they make the switch quickly, they acted aggressively. Every minute counted for the sales team, so for the most part they followed his 80/20 rule of thumb. In other words, if 80% of your problems are resolved after using AI, you’re ready to go live.

This is the final point.

AI is not 100% guaranteed

AI is never 100% accurate. This means that you should start with low-risk, high-reward ones and continue to monitor your performance for potential risks. We saw this when we first vaccinated people on the front lines. In business, we do this by putting them at the forefront by focusing on the people who need AI most to help them make decisions (usually sales and marketing). From there, apply AI to the remaining departments that can benefit.

As a data technologist, it makes sense for me to ‘trust science’. I take all this information, whether it’s vaccine-related or corporate data, and I’m translating it into statistics and forecasts while navigating uncertainty. The introduction of vaccines created a moment when scientific perspectives permeated the world. And as the company joins this new wave of scientific thinking, the impact of AI, big data and technology on business will be profound.

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