To make best use of your data asset across your organization, it is essential to have a solid data quality framework to ensure that your data is accurate and complete. But all data quality management models are not created equal: different organizations have different levels of data quality maturity, depending on organizational priorities and needs.
Data quality maturity model
To get your organization to a level of data quality that helps support data that is fit for purpose, evaluate your framework against the data quality maturity model. This model provides a benchmark for assessing your company’s processes around data management and data quality, to determine areas of risk and identify areas where additional investment may be needed.
There are four key stages of data quality sophistication:
How mature is your data quality framework?
While 88 percent of companies have some sort of data quality strategy in place, many are in early stages of sophistication. Only 18 percent of companies say they have reached the optimized state of data quality. By contrast, 43 percent of companies perform data analysis and cleansing only as issues arise.
Assessing where your organization fits on this model can help you determine what the next best steps are in building your data management practices, so your company can trust that its data asset can drive good business decisions.
To determine where your business sits on the data quality maturity model, you need to consider the following areas:
People are the critical component to any effective and robust data quality strategy. Having the right people in place to champion and implement your data management practices are essential to creating a business culture that supports data quality as status quo. To assess what your people investment in data quality should be, ask questions like:
Your processes are designed to clearly identify data quality roles and responsibilities. Without process in place, quality issues will inevitability unfold as the volume of data you handle increases. Consider taking a look at:
Data quality technology has evolved over recent years and involves more than just point solutions; they now offer critical support for managing information across its full lifecycle, from data discovery through to monitoring and visualization. Evaluate your company’s use of data management technology by asking:
By determining where your organization lies on the data quality maturity model, you can determine what your next best steps and areas for investment are in creating a robust data quality framework, and ultimately driving better quality data for better decision making.
Learn more about how you can create a more sophisticated strategy by reading our paper on creating an ideal data quality framework.