Five insights about harnessing data and AI from leaders at the frontier

Here is an excerpt from an article written by Mohammed Aaser, Jonathan Woetzel, and Kevin Russell for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out others, learn more about the firm, and sign up for email alerts, please click here.

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Four CEOs describe what goes into turning a world of data into a data-driven world.
What was once unknowable can now be quickly discovered with a few queries. Decision makers no longer have to rely on gut instinct; today they have more extensive and precise evidence at their fingertips.New sources of data, fed into systems powered by machine learning and AI, are at the heart of this transformation.
The information flowing through the physical world and the global economy is staggering in scope. It comes from thousands of sources: sensors, satellite imagery, web traffic, digital apps, videos, and credit card transactions, just to name a few. These types of data can transform decision making.
In the past, a packaged food company, for example, might have relied on surveys and focus groups to develop new products. Now it can turn to sources like social media, transaction data, search data, and foot traffic—all of which might reveal that Americans have developed a taste for Korean barbecue, and that’s where the company should concentrate.
The potential is being borne out every day—not only in the business world but also in the realm of public health and safety, where government agencies and epidemiologists have relied on data to determine what drives the spread of COVID-19 and how to reopen economies safely.
But the sheer abundance of information and a lack of familiarity with next-generation analytics tools can be overwhelming for most organizations. That’s why the McKinsey Global Institute invited CEOs from CrowdAI, SafeGraph, Measurable AI, and Orbital Insight—four start-ups that are expanding the boundaries of data and AI innovation—to discuss what kinds of new insights are possible and how the landscape is changing. Their wide-ranging discussion yielded five important takeaways.

Takeaway 1:

New forms of data are giving organizations unprecedented speed and transparency

When a CEO wants an answer to a complex question, a team might be able to get it in a couple of months—but that may not be good enough in a world where competition is accelerating. One of the biggest advantages of an automated, data-driven AI system is the ability to answer strategic questions quickly. “We want to take that down to an hour or so when it’s about something going on in the physical world,” says Orbital Insight founder James Crawford.

Data and AI are not only finding answers faster but creating transparency around issues that have always been murky. Consider a multinational’s desire to ensure sustainability in its supply chain. An input like palm oil is produced on millions of farms in developing nations, and it goes through thousands of refineries and mills before it reaches one of that multinational’s factories. That’s a difficult supply chain to trace. But Orbital Insight has been able to use geolocation data and satellite imagery to track the physical supply chain—not based on paperwork that may not be accurate but based on real-time snapshots of where trucks are driving and where deforestation is occurring.

Unstructured data, especially in the form of images and video, remain challenging for organizations to utilize due to the complexity of building and maintaining cutting-edge algorithms. CrowdAI is unlocking the ability to extract insights from images and video. Users begin by labeling objects or pixels in raw imagery—perhaps the most time-consuming step in creating a computer vision model. “Our platform speeds up the labeling process by incorporating user-generated labels to automate further labeling, constantly iterating on that human feedback,” says CrowdAI founder and CEO Devaki Raj. In this way, firefighters can use apps on their phones to track the behavior of wildfires in real time, and vaccine manufacturers can use computer vision on their production lines to spot tiny defects in vials that human eyes might miss.

Another start-up, Measurable AI, has found a way to take some of the guesswork out of corporate financial performance. CEO Heatherm Huang explained that his company uses natural language processing and machine learning to aggregate email receipts on its own mail app, with user permission, for statistical modeling. This kind of analysis can predict reported earnings better than traditional stock analysts can. When Zoom adoption spiked in 2020, for example, Measurable AI’s algorithm was able to estimate quarterly earnings within 1 percent of reported earnings, compared to an industry consensus that was off by more than 10 percent.

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Here is a direct link to the complete article.

Mohammed Aaser is McKinsey’s chief data officer. Jonathan Woetzel is a director of the McKinsey Global Institute, where Kevin Russell is a senior fellow.

This article was edited by Lisa Renaud, an executive editor in the Southern California office.


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