Five years ago, McKinsey had taken stock of the big data revolution in five areas: location-based services, retail, manufacturing, healthcare, and the public sector. Revisiting these sectors, McKinsey finds the fastest adoption in location-based services and retail. Manufacturing, healthcare, and the public sector, however, have captured less than a third of the potential value from big data, finds the study.

What’s more, the gap between the leaders and laggards is growing wider as new opportunities emerge.

Organizational culture, mindset, and structure are the main stumbling blocks. This has prevented companies from using data analytics effectively to create new value, improve performance, and become more competitive.

“The biggest barriers companies face in extracting value from data is adapting core processes and building new capabilities at scale,” says Nicolaus Henke, global leader of McKinsey Analytics. “Embracing analytics is not about adopting a new tactic. It’s about changing your business model and the fundamental way you make decisions.”

Another challenge is finding the right talent – and retaining it. The demand for data scientists is significantly higher than the supply, and this will continue until academic and training programs grow and catch up with requirements.

But McKinsey finds an even more critical area of talent shortage – what it calls “business translators.” These are people who can link data analytics with practical business questions. They are data savvy and have industry or domain expertise.

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Editing by Malavika Velayanikal and Judith Balea