When designing and implementing a data governance framework, one of the key activities is to define the roles required to ensure that data is managed as an asset, including those roles which help to tackle the data quality problems that any governance programme will uncover and seek to fix.   As governance is a multi-faceted activity, embracing data management, business process improvement, organizational culture change and IT, many different roles will be needed.  These include the need for a data champion at executive level who will promote the importance of data governance and data quality to senior managers, a data governance lead who owns the overall framework and coordinates all activities to ensure the programme delivers its promised benefits, data owners, based in the business, who are accountable for defined data domains and who specify and enforce the policies and controls on the data they manage, and IT and data quality analysts and specialists who are able to quantify the scope and scale of data quality problems, design and implement improvement activities to address them, and are able to apply data quality tools as part of their armoury.    

All the above roles are essential and have an important part to play in ensuring data governance and data quality improvement become part of the fabric of any organization.  But there is one role which is critical to the success of any data governance and / or data quality initiative.  That role is that of a data steward.  In my experience of designing, implementing and reviewing both governance and quality programmes, whether or not they are successful usually critically depends on whether data stewards are in place and if they are in place, their effectiveness in the role.  Without embedded data stewardship, all too often initiatives quickly mutate into talking shops, where people ponder fixing data problems but do not have the time, skills or motivation to deliver the improvements required.   This can cause many well-intentioned governance and quality programmes to fail to meet expectations.  For example, in a recent Experian survey published in April 2024 which surveyed 250 data professionals in the UK and Ireland, 84% stated that data governance was ‘the backbone of effective data management’.  Despite this, only 15% stated that their data governance efforts are ‘fully meeting expectations’ with the remaining 85% conceding that their endeavours are only ‘partly meeting expectations’ or ‘falling short’. 

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