Data Quality

Data Quality: A Critical Component of the CFO's Data Journey

Data quality is just one part of the CFO's data journey that should be understood to be an effective data steward.


In order for a CFO to be an effective data steward they must understand the different elements of the data journey and their role during the journey. Data quality is often cited as one of the biggest challenges facing CFOs as they wrestle with rapidly expanding datasets, ever increasing demands for insightful analysis, and pressure to adopt the latest, cool new technologies. In a 2019 Accenture study, 38% of CFOs identified difficulty in standardizing enterprise data or agreeing upon a single version of the truth as a major barrier to fully leveraging new technologies. [1]


Non-Technical Components of a CFO’s Data Journey

What does the CFO’s data journey look like and how can they play an effective leadership role?  Unfortunately, too many descriptions of how to manage data are written from an IT or systems perspective replete with terms such as replication, master data management, conversion, archival, and the like.  From a CFO’s perspective, the data journey can be described in five simple steps.

 A chart of 5 non-technical components of a CFO's data journey: 1) collect, 2) structure, 3) store, 4) use , 5) act


Using this framework allows a CFO to cut through the morass of technical jargon that so often clouds any discussion of data and technology. The effectiveness of a CFO’s data governance role is in their ability to communicate with clarity and confidence with a number of different constituencies including their C-suite colleagues, the board of directors, auditors, and regulators. Addressing data from a business perspective allows a CFO to ask the right questions and make sure that the answers satisfy business not just technical needs.  



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Data Activities CFOs Need to Be Conversant In


Data is collected or generated from many different sources many of which maybe outside the enterprise.  Increasingly data is being sourced from the extended business eco-system. CFOs need to be comfortable that the source is trusted, the data is accurate and that it is relevant for our business.



Once data has been collected or generated, we need to ensure that it is appropriately structured.  Is like data grouped together? Is the data formatted in a consistent way? Is the data organized in a way that meets our business, compliance and technical needs?



Is the data stored in an accessible and secure form?  Are there appropriate back-up and recovery procedures in place to mitigate potential loss? Have appropriate mitigation measures been instigated, for example, companies are increasing building data loss into their insurance coverage.



Are we using the data in the most appropriate manner?  Which data should be reported? To whom, when and in what format? What calculations or analyses should be performed? What tools should we be using? How is the accuracy of calculations and analyses assured? What education is required for recipients to interpret and use the data or analysis?



Are we acting on the data when required?  Remember, the value of data is only as good as the decisions or actions that result. It does not matter how rich the data is, the quality of the analysis performed, or the coolness of the visualization tools used if no actions or decisions result.


Adopting a pragmatic business perspective to management and use of data ensures that the technical dimensions of data governance are aligned with business needs and provide CFOs with the clarity and confidence to lead the journey to a data driven enterprise.



Next Time: Profiting From Data


David Axson has more than 35 years of experience working with CFOs across more than 40 countries.  He was a co-founder of The Hackett Group, Head of Corporate Planning at Bank of America and a partner at Accenture Strategy.  For more information visit

[1] From Ideas to Outcomes, David A.J. Axson & Haralds Robeznieks, Accenture 2019

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