Can the U.S. maintain its lead in data management and analytics?
After spending much of the past year traveling throughout Asia and working with companies in various industries to advance their data, analytics, and artificial intelligence capabilities, it’s become clear to me that U.S. businesses have their work cut out for them in the coming years.
Traditional measures put the U.S. 10-15 years ahead of the APAC region in terms of technical sophistication and operational deployment of data and related technologies. But that timeline is far from certain. If anything, it’s only accurate insofar as APAC countries follow the same roadmap as the US, which is unlikely, to say the least.
What’s far more plausible is that APAC businesses across industries will skip entire generations of technology and leap past their US competitors. Just as we saw with the rise of smartphones and digital payments in the region over the past decade, leapfrogging can change the game entirely. Today, a look at patent filings around AI and deep learning in the region, especially in China, suggests the process is already underway.
How can U.S. companies maintain their edge on data, analytics and AI, and by extension their edge in the global business landscape? By radically reinventing their approach.
Specifically, they need to spend less time on science experiments and pushing the limits of technology, and far more time applying it to existing problems. In other words, revolutionizing the way the people inside their organization actually use data. This is where the greatest value lies for companies – upwards of $5.8 trillion annually, in fact, according to the latest research from McKinsey.
In particular, there are three areas where the US needs to focus:
Data sharing and accessibility
Traditional use cases for data and analytics revolve around measuring various aspects of a business’ operations followed by analyzing the data to find ways to optimize performance. The data used is almost entirely first-party – data produced by the organization directly – and typically hoarded like gold.
But with the rise of AI in recent years, the limits of this approach are becoming increasingly clear. To get the most out of AI and ML, companies need to feed their systems as much data as possible. Generally speaking, the more data going into the system, the more intelligent it becomes.
Today, U.S. companies are often hamstrung in their AI efforts by a lack of data. Sure, they are capturing more data internally than ever before, but the “data as gold” mentality and other technical limitations are keeping them from making it available to others.
In Asia, meanwhile, there is a distinctly different mindset. Companies there understand the value of data in AI/ML contexts and can be more strategic in making it more open and accessible beyond the walls of their organizations, avoiding the fragmentation and data silo traps that persist in the U.S. Asian organizations are better able to grasp the “spread the wealth” benefits that accompany mass data sharing, in no small part because AI is actively promoted by many governments in the region.
Until recently, the ability to draw meaningful insights out of data was available only to data scientists and others who could use complex, cumbersome tools. But as these technologies mature, the user interfaces are becoming more powerful and intuitive. It’s a common theme in technology and it’s one that promises to make advanced analytics and AI more widely accessible to non-technical users.
In the U.S., however, the urgency to democratize analytics and AI is sometimes difficult to pin down. To a great extent, the problem stems from the fact that American companies have invested heavily in advanced technology over the years, while simultaneously being able to employ specialists who manage these technologies with relative ease. As the thinking often goes, why sacrifice power for accessibility?
In the new world, however, companies need technologists working on higher order, strategic projects if they don't want to get overtaken. That means moving away from traditional gatekeeper models when it comes to tools and data.
APAC businesses, meanwhile, are becoming more open to data democratization. They have obstacles to overcome for sure – especially social friction in more conservative cultures like Japan – but there is also far less organizational baggage slowing them down, in great part because investments in current analytics technology are lower. What’s more, there is a realization that they have fallen behind, which makes data democratization an even greater priority.
Today in the U.S., we are facing an existential crisis of trust, especially when it comes to our relationship with technology. People are growing wary of technology after seeing the negative impacts of dominant technology platforms and the spread of misinformation on social networks, which erodes trust and discourages future technology use.
In many ways, I think this is a consequence of the bottlenecks noted above. In the US, data and the technology we use to interact with it have always been in the purview of experts, which – historically at least – has made it easy to trust. (That doesn’t necessarily mean the technology is perfect or the data is right – just that it’s easier to trust when “experts” are the gatekeepers.) But the only way to truly foster trust is by understanding how the tools work. That means making them more accessible to non-technical members of the team.
I expect that trust will play a considerable role in Asia’s next leapfrog moment. There, the technology adoption curve itself is much steeper because they are skipping generations of technology across multiple categories all at once. By skipping the gatekeeper model and making tools more widely accessible from the get-go, trust in the tools and the data they produce will come faster, further extending their potential lead.