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The article discusses steps that managers can take to measure the quality of their data which include assembling the last 100 data records used by the group, setting a meeting with people having knowledge of the data, and checking and recording errors in the data.

The article discusses the roles of data provocateurs in driving data quality across an entire company and recommends provocateurs to keep bosses informed, accept help from others, and share credit for success.

The article discusses business models aimed to compete with data which include cost reduction through improved data quality, data-driven innovation, providing content and becoming data-driven.

The article examines the various types of data quality problems that companies regularly encounter, which include incomplete data, misinterpreted data and unreliable data, and discusses how they can impact business planning and decision making.

The article discusses what members of the C-suite should know to get the quality of the data and start to put high quality data to function. It states that organizations adopt the strategy of finding out and determining data errors and preventing the data errors at their sources. It notes that high quality data are considered as assets while bad data are liabilities. Moreover, senior management must distinguish its most costly data liabilities and place a quality program to make it less complex.