The finance function is currently undergoing a major transformation in many companies. Driven by fierce global competition, the finance department must move from being a mere steward of the company’s figures to becoming an enabler of tomorrow’s business. In view of this change in role, the finance department or the Chief Financial Officer (CFO) has a greater responsibility than ever before: whether strategic and operational planning, reporting or forecasting – companies make decisions, plan strategies and take operational measures on the basis of financial figures – and do so in ever shorter cycles.



8 recommendations for action for effective data governance

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Incorrect figures have serious consequences

But what happens when companies cannot rely on their data basis and the figures are not correct? The consequences are not necessarily always immediately apparent, but they do have serious repercussions:

  • Possibly, wrong decisions are made, which can mean missing revenues.

  • There may be cumbersome processes and – as a result – increased costs that are not identified and give competitors an advantage.

  • Worst of all, there is uncertainty when confidence in the figures, and thus in the organization, dwindles. This development cripples the entire organization – from top management (who may be wrong or too hesitant) to the lowly employee (whose lack of confidence may lead directly to his or her termination).


Data governance and the financial data

Data governance describes the structure of the organization and the associated processes in the company that are responsible for the processing, traceability and quality of the data. As such, data governance plays a key role in finance transformation. It is critical to think fundamentally and actively about who the right people (and associated roles) are to handle the financial data, approve changes, and ensure its quality. There should be no overlap or gaps in responsibility.


It is also important to have effective and efficient processes for handling financial data. All too often, the courses of action in companies have grown historically and require a critical examination as to whether they are still purposeful in the digital age.


Documented processes, clear responsibilities and continuous data quality checks are the elementary basis on which data is correctly created, processed and used. This goes hand in hand with optimum data transparency, which, if necessary, can provide information at any time about where data came from, when and by whom it was changed, or where and how it was used.


Data governance: the basis for data quality and data protection

At the same time, data governance is an essential prerequisite for data protection and compliance – two issues that are particularly important for financial data. After all, financial data is confidential data and must be protected from unauthorized access and misuse.


It is important to understand that establishing data governance is only the first step in this regard. It forms the basis on which data quality can be clearly defined, measured and visualized. Only then can the appropriate measures be described and implemented to ensure high data quality in the long term.


Data governance is a crucial element in establishing trust in data, its accuracy, and its timeliness as part of the finance transformation. In addition, data governance provides the necessary prerequisite for compliance with legal requirements such as the German Federal Data Protection Act (BDSG) and the General Data Protection Regulation (GDPR). Likewise, data is elementary in the use of innovative concepts and technologies such as artificial intelligence: The systems and models are only as good as the data they are fed with or work with.


Conclusion: Transparent data and processes with data governance

Data governance alone cannot realize finance transformation. However, it is a decisive factor for its success. Transparent data quality can only be achieved and continuously improved with a clear data strategy and documentation as well as with a clean organizational and process definition. Reliable, high-quality data provides the finance function with the tools it needs for a successful finance transformation.


8 recommendations for action for effective data governance

Download for free (in German)


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