What was once unknowable now can be discovered quickly 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 artificial intelligence (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 such as 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 the chief executive officers 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,” said Orbital Insight founder James Crawford.
Data and AI not only are finding answers faster but also are 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.
Data and AI are not only finding answers faster but creating transparency around issues that have always been murky.
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,” said 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 startup, 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% of reported earnings, compared with an industry consensus that was off by more than 10%.
Takeaway 2
Specialist firms are refining and connecting data.
Because the universe of data is so broad, service providers are carving out specialized niches in which they refine a variety of complex and even messy raw sources, feeding the data into machine learning– or AI-powered tools for analysis.
Consider SafeGraph, a startup focused exclusively on geospatial data. It specializes in gathering, cleaning, and updating data on points of interest, building footprints, and foot traffic to make it quickly usable by apps and analytics teams. Further, to get around the issue of the many quirky permutations in the way addresses are assigned around the globe, the company has introduced Placekey, a free and open universal identifier that gives every physical location a standard ID. This enables everyone to use a recognizable string when they interact—a step that will ease the merging of data sets. In the first 6 months after its rollout in October, more than 1,000 organizations began using and contributing to the initiative.
“We’re just an ingredient in any one solution,” said SafeGraph CEO Auren Hoffman. “It’s like selling high-quality butter to pastry chefs. The end consumer of the croissant may not even know that there’s butter in the pastry. And they certainly don’t know it’s SafeGraph butter. But the chef knows how important the ingredient is.”
Another example is Orbital Insight’s compilation of data from satellites, mobile devices, connected cars, aerial imagery, and tracking of ships at sea. All of this information feeds into an integrated platform, giving users the ability to pull out whatever is in satellite imagery and even count objects of interest automatically and connect it with other data on the platform. “We can deliver counts so you don’t have to look at every cornfield in Iowa or every road in China to figure out what the agricultural harvest is going to look like or whether people are back on the road after COVID,” said founder James Crawford.