In 2018, Thomas C. Redman, president of the consultancy Data Quality Solutions, conducted research to understand why, in his words, “so many years into the digital revolution, progress in the data space is so slow.”
He identified five key areas of critical importance. “Failure to deliver in any of these areas can scuttle an otherwise terrific data program,” he stated in an article for MIT Sloan Management Review.
• Data quality: Poor-quality data adds incredible cost and friction.
• Putting data to work: Unless companies put data to work in ways that return value, there is little business benefit. Ways to do so include data science (including AI and machine learning), exploiting proprietary data, creating a data-driven culture, monetizing data by selling it or building it into products and services, and treating data as an asset.
• Organizational capability: This refers to the people, structure and culture within the organization that support data programs. For example, silos can get in the way of data sharing.
• Technology: Technological infrastructure will be different for each company, but it will be difficult for companies to scale their data programs without the right tools and technologies in place.
• Defense: This category encompasses all the organizational tasks related to minimizing risk, including security, privacy and ethics.
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