The CFO's Expanding Data Governance Role
CFOs have always had a pivotal role in managing data across the enterprise. Initially the focus was on the financial data required to accurately account for the business and produce required financial statements. The chart of accounts and the accounting code block underpinned a framework of governance, discipline and management of data that has worked for many decades. Today, with the explosion of data that is available for accounting, reporting and analysis that data remit is expanding significantly. Almost every business interaction now has a digital footprint. In addition to financial data, organizations are increasingly tapping into market, customer and operational data. As CFOs and their teams embrace driver-based forecasting, sensitivity analysis, scenario planning, advanced analytics and other forward-looking tools, they need to assure the quality of data being used for critical decision-making. However, Finance is not always properly equipped to handle the high volumes of data and new requests for information sharing, especially when the requests seek insights into non-financial data. Market, customer and operational data may be housed in different parts of the organization and not subject to the rigorous data governance practices applied to financial data. Three Data Governance Approaches CFOs Should Assess
The first step is to define effective data standards and establish effective governance across all data. Sounds simple doesn't it? Unfortunately, while it is clear that finance has a mandate to govern finance data, that is not always the case with other datasets such as product, customer or channel data. Typically, I see three different ways in which organizations have addressed these challenges: Central Command and Control A single entity, it could be finance, IT or an independent role such as a Chief Data Officer, has the mandate to assure data quality across the enterprise. This model lends itself to tightly integrated businesses where large amounts of shared data has value for planning, control and risk management and where speed and transparency are important. ++++++++++++++++++++++++ Dig Deeper: Read the Syniti Central Finance White Paper ++++++++++++++++++++++++ Minimum Viable Agreed Principles The organization agrees on the minimum set of data that needs to be standardized across the enterprise for which common principles agreed. All other data is subject to local control and governance. This model typically works best for organizations that have a diverse set of businesses (e.g. conglomerates, portfolio holding companies) and hence data that reduce the value of universal common standards. This can also work very well for companies that engage in a high volume of M&A transactions. A minimal set of standards can speed up integration and also facilitate rapid divestiture. Mutually agreed standards An organization agrees on a set of data governance principles that are then applied by each data owner to the data within their domain. A co-ordinating committee representing all constituencies serves as a forum for discussing and agreeing to the evolution of governance principles over time. This model is only as effective as the level of commitment and buy-in by each constituency. It has the advantage of devolving some responsibility to those closest to the data. However, if this model evolves simply because of political or cultural barriers to uniform governance it can severely limit the ability of an organization to realize the full value of the data at its disposal. Regardless of the model adopted, the CFO needs to be fully engaged in order to build collaborative relationships with their fellow business and functional leaders in order to agree on mutually acceptable governance practices. The CFO also needs to have full authority to dictate acceptable standards for all data required to fulfil statutory financial reporting requirements.