Companies need to be more focused on proprietary data — data that is unique to a company and can be used to create a sustainable competitive advantage. The need for proprietary data strategies is increasing with new data types and the growth of artificial intelligence (AI). Most commercial AI involves machine learning, and if your company has the same data as everyone else, it will have the same models informing these machines, and thus no competitive advantage. A company’s proprietary data strategy should address the full lifecycle of such data — from what might be done with it, to how to get it, to the ethical considerations that might result from it. Beyond simply appreciating the need for such data, a strategy effort can answer key questions about how proprietary data fits into the strategy and business models of an Organization.
What’s the most overlooked piece of your company’s data strategy? If you’re like many companies, it’s probably proprietary data — data that is unique to a company and can be used to create a sustainable competitive advantage. This is not to mean trade secrets and intellectual property (which is often proprietary but seldom really data), but rather, data where the company is the only Organization that has it, or it has added enough value to make it a unique business asset. Proprietary data can be big or small, structured or unstructured, raw or refined. What’s important is that it is not easily replicated by another entity. That’s what makes it a powerful means of achieving offensive value from data management.
We and others have been writing and talking about the value of proprietary data for many years. But we still see few organizations with strategies for how to acquire, develop, and leverage it. Most companies focus only on their internal data, which is proprietary in a sense, but may not be a valuable asset unless further developed. If, for example, your internal data sheds light on an issue that other organizations face (payments data for a credit card firm, for example), or if you can combine it with external data in a way that makes it useful to other firms, it could be a proprietary asset.
The need for proprietary data strategies is increasing with new data types and the growth of artificial intelligence (AI). There are many new types of data emerging across industries — sensor data, mobile data, new types of payment data, and more. Most commercial AI involves machine learning, and if your company has the same data as everyone else, it will end up with the same models informing these machines, too — and thus no competitive advantage. Organizations need to think about their proprietary data strategy and put it into action now.
Some companies and industries are already pointing the way to effective proprietary data strategy. Alphabet’s Waymo and GM’s Cruise Automation, for example, are assiduously gathering maps and sensor data from billions of miles of simulated and on-road driving. Firms focused on medical imaging for AI-assisted radiology or pathology are acquiring or partnering for image data.
