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

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 ML 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.

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.

They 5 key takeaways:

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”

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. 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.

2. Specialist firms are refining and connecting data

Since 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.

3. Most non-tech companies are lagging, but new tools can get them in the race

Adapting to an era of more data-driven or even automated decision making is not always a simple proposition for people or organizations. The companies that have been fastest out of the gate already have data science chops. But according to Devaki Raj, CEO of CrowdAI, most non-tech Fortune 500 companies are stuck in pilot purgatory when it comes to sophisticated uses of systems such as computer vision and AI. “It starts with a lack of understanding of where all of their data is.”

4. It takes domain experts to extract the real value from data

Data science teams can build models with miraculous capabilities, but it’s unlikely that they can solve highly specific business problems on their own. Data engineers and scientists may not understand the subtleties of what to look for—and that’s why it’s critical to pair them with domain experts who do.

5. Companies need to build in privacy safeguards and AI ethics from the start

The utility of data versus the right to personal privacy is one of the biggest balancing acts facing society. There is enormous value in using personal data such as health indicators or geolocation tracking for understanding trends. But people have a legitimate desire to not be tracked. Companies that work with data typically promise that it is anonymized and aggregated, but not all of them have the same standards and cybersecurity protections.

Technology itself can address this issue, noting recent advances in areas such as differential privacy, homomorphic encryption, and synthetic data. These technologies could conceivably enable the ability to connect individual-level data, analyze it, and then use it in a way that doesn’t give away any individual-level information. It’s going to yield an incredible amount of innovation. Over the next few years, we’ll be able to have our cake and eat it, too.

Thanks

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Excerpts borrowed from Mckinsey research