20 Apr Harnessing a World Full of Data
There is data everywhere. Have you sat in a conference room and heard someone say, “yeah, we have the data, but what do we do with it?” We are in the midst of a data revolution. What goes into turning a world full of data into a data-driven world?
Are you making decisions about sales tactics, marketing strategies or branding colors? Harnessing customer and market data fast is key to meeting expectations and achieving your goals.
It used to be you could rely on instincts and experience. Now, machine learning and artificial intelligence (AI) systems can boost analysis and insights to give you actionable information in near real-time. A recent McKinsey article summarizes the top five takeaways from leaders in data analysis and AI. Read on for a perspective from four CEOs about the subject.
1st: New forms of data are giving organizations unprecedented speed and transparency.
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.
However, there are some issues with unstructured or subjective data, especially in the form of images and video. They remain challenging for organizations to utilize due to the complexity of building and maintaining cutting-edge algorithms.
2nd: The increase in specialist firms with expertise in 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.
“We’re just an ingredient in any one solution,” says 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.”
3rd: Most non-tech companies are lagging, but new tools can get them up to speed.
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 expertise. 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.”
Now a growing range of available tools and platforms can help them catch up. The number of companies working with data today is sharply higher than it was even five years ago. Back then, it took a world-class engineer to extract value from that information, and non-tech companies had difficulty attracting the few people with skills at the cutting edge of data science. But new platforms and analytics tools are leveling the playing field – as is the vast array of data that is free, open, or available at relatively low cost.
4th: 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. “To be effective, automation needs to be informed by those closest to the problem,” says CrowdAI’s Devaki Raj.
On-the-ground business knowledge is especially important when it comes to interpreting data from other countries. “As a transactional data provider for emerging markets, we cover places like Southeast Asia, Brazil, and Greater China,” says Measurable AI’s Heatherm Huang. “You need to adopt different languages and compliance standards in different regions. You need to know that people in China don’t use email that much, for instance, or credit card adoption in Indonesia is still pretty low at this moment.”
5th: 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.