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CDO Challenges – Improving data quality and data governance

This instalment of CDO Challenges examines addressing the issue of shadow IT within data offices and the wider organisation and how it can affect operations.

Educate the team 

As with many aspects of improving the scope of a data office, the first step is wider data education for the organisation. A CDO needs to ensure that key decision makers understand the difference between data quality and data governance, but also how they work together to achieve business objectives.  

Starting with the basics, make sure team members understand that data quality focuses on the value and integrity of information and data sets, whereas data governance provides the management and oversight of data within an organisation.  

Data governance provides a framework for data owners to view and utilise data in a safe, secure manner. It provides standardisation for the collection and storage of data, as well as how this data can then be disseminated and analysed to achieve specific goals. Confusion can come about as data governance can be considered an umbrella term that covers everything from data management plans to maintaining best practices rather than one specific, tangible thing.  

Data quality is more difficult to consistently monitor as quality can change depending on the end user, the objective and the way the data is being used. However, despite this flexibility, consistently praised data quality can be achieved with improved data literacy, a strong data culture and monitored best practices.  

A CDO needs to demonstrate clear links between business processes, KPIs and the quality of data assets. This can be achieved with improved storytelling. Once these connections have been shown and demonstrated to the teams, CDOs and data leaders can start targeting a quality improvement programme that has been developed to achieve individual targets, projects or business objectives.  

Setting the rules 

There is a greater chance for success if singular, digestible steps can be understood and implemented by the wider organisation. For setting a data governance standard, the following should be imbedded across the business: 

  • Identify organisational objectives. 
  • Outline the scope of data covered by the standard. 
  • Identify a position that is accountable for the standard. 
  • Ensure clarity on data ownership rights across the business. 
  • Educate on the handling of data collection. 
  • Ensure proper accessibility and sharing of relevant data sets and streams. 

For data quality, standardised practices need to be rigorously maintained and developed into a wider data culture that permeates through the business. This system will be able to monitor the integrity of the data and highlight errors or inconsistencies during processing. When a problem is shown, a comprehensive system of guidelines will allow for immediate and accurate troubleshooting, even from staff in non-data roles. 

It needs to also be shown that staff understand data quality is also judged and assessed by the end user, which can often be a customer. If the end user feels the data is not useable, regardless of how complete and compliant it is, the data will be considered of poor quality. This does mean that there can be an ever-changing set of criteria for what constitutes high quality data for some businesses, but this can be managed with entrenched quality protocols and education.  

Monitor and repeat 

Once the basics of data governance and quality have been agreed across the organisation it then falls onto the CDO and the data team to ensure the standards continue to be met and improved. This requires communication and monitoring of the processes.  

One solution is a data quality dashboard that can show quality to all stakeholders. Furthermore, these types of dashboards can also demonstrate trends from historic data which can frequently be used to predict trends.  

After extended use, a dashboard can also compare internal performance across the organisation (we need to remember that not all departments have the same data use) which is critical for business development and achieving objectives. This will in turn add momentum to a data-led decision-making culture within the business. 

Elsewhere, a data steward needs to be selected and made responsible for quality and compliance. This will help remove some of the tasks from the CDO but will also bring more people into the fold when it comes to data’s usage. For a mature organisation, a data steward must also champion good data practices and support all staff when quality issues arise. For an organisation that is evolving its data maturity level, a data steward can help spot areas of the business that need specific types of data training and help build a strong data culture. 

Data quality and data governance are ongoing tasks that continuously evolve, enhance and often change at a moment’s notice. It is because of this that a CDO needs to work with multiple stakeholders and staff at different levels of the organisation to create a culture that embraces data, understands the need for quality data and compliance and is open to collaboration.

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