In my career, I’ve spent more than 25 years helping companies with their data and data quality programs. Beginning in 2018, I undertook a broad-based research effort to understand why, so many years into the digital revolution, progress in the data space is so slow. This article synthesizes a review of my clients’ and others’ successes and failures, discussions with dozens of experts in data and analytics, and study groups that dived deeply into various aspects of the topic.
The headline result: Today’s organizations are unfit for data. Until companies address the underlying issues, progress will remain halting and uncertain.
To be clear, the many successes I’ve observed — in data science, analytics, artificial intelligence, data quality, and other ways to put data to work — confirm that the approaches, methods, and technologies around data work just fine. But most companies still struggle. Organizational structures, people issues, a lack of accountability, and other traps get in the way. This conclusion has far-reaching consequences, and leaders should not expect faster progress until they make some real changes. To do so, they must first understand and address the underlying issues that hinder progress.
To better understand both driving forces (those pushing progress forward) and restraining forces (those holding back progress) that are impacting data science progress, I’ve used force field analysis (FFA), a powerful visual tool derived from Kurt Lewin’s change management model. A companion toolkit provides information on how to build out your own FFA. (See “Visualizing Change With Force Field Analysis.”)