Chief Data Officers: How to Achieve Innovation, Resilience and Growth

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Learn what is expected of data directors who are increasingly committed to business innovation, resilience and growth.
Data management and governance for data-first companies

Companies that make their strategic decisions with the support of chief data officers ( CDOs ) place greater emphasis on customer experience and driving revenue growth. CDOs can achieve sustainable business growth in their role. These executives increasingly see themselves as business leaders rather than just technology leaders. Therefore, in addition to outlining a data management strategy and implementing a data-centric technology landscape , they must focus on value creation “and making the organization use information as an asset that impacts results” and drives the business .


The role of chief data and analytics officers (CDAOs) architect database to grow and evolve, while turnover and stability remain challenges in what is still a new role. The percentage of organizations that have appointed a chief data and analytics officer (CDAO) continues to rise, from just 12% in 2012 to 74% in 2022. However, challenges remain, with nearly 60% of companies indicating that the role is nascent, evolving, or has high turnover.

On the other hand, the role of the CDAO is increasingly focused on business growth and analytical results.



HubSpot BLOG

In recent years the role has evolved to encompass analytics: 44% of CDAOs now come from a data analytics or data science background, compared to 29% who have a data management or data governance background.

Source: NewVantage Partners Survey





Trends 2022
To operate successfully in the field of data and analytics, it is essential to stay on top of trends. This way, data managers can anticipate changes and manage uncertainty .

As Gartner points out , among the trends to be observed in 2022, the use of artificial intelligence (AI) systems that adapt to changes stands out. In addition, there has been much talk about the concept of data-driven artificial intelligence (which takes into account data management problems specific to AI and addresses the issues of bias, diversity and data labeling in a more systematic way).