Why does progress in the data space continue to be so slow? Based on a review of organizations' successes and failures, discussions with experts in data and analytics, and study groups that dived deeply into the topic, the author identifies the underlying factors impeding organizations' data science efforts and points to nine key areas they must address in order to advance their data initiatives.

The United States has had many problems coping with the coronavirus. A critical and underappreciated problem is bad data: Without good data, planners can't plan, epidemiologists can't model, policy makers can't make policy, and citizens don't trust what they're told. The U.S. needs a robust program, with professional management of the data supply chain, to develop trustworthy data about pandemics and other public health crises.

Fifty years after the expression "garbage in, garbage out" was coined, we still struggle with data quality. Studies show that knowledge workers waste a significant amount of time looking for data, identifying and correcting errors, and seeking confirmatory sources for data they do not trust. When data are unreliable, managers quickly lose faith in them and fall back on their intuition to make decisions, steer their companies, and implement strategy. They're also much more apt to reject important, counterintuitive implications that emerge from big data analyses. The good news is that improving data quality is often not as hard as you might think, the author asserts. The solution is not better technology: It's better communication between the creators of data and the data users; a focus on getting the process right going forward rather than on cleaning up existing bad data; and, above all, the shifting of responsibility for data quality away from IT folks, who don't own the business processes that create the data, and into the hands of managers, who are highly invested in getting the data right.

Most companies underutilize their data assets, but sometimes they figure out how to leverage them to satisfy marketplace demands. The author outlines nine ways to create new value from your data.