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High-quality data is vital for informed decision making , effective policymaking and achieving the desired positive social and economic outcomes .
Often , public sector organizations fail to realize the importance or value of data quality . As well , they can be unsure of where to start with data quality and lack any investment efforts .
Info-Tech ’ s blueprint further states that many organizations , including those in the public sector , tend to adopt a project mentality when it comes to data quality instead of taking the strategic approach that would be more beneficial in the long term .
• Liability : Poor quality data can cause an agency to fail to meet compliance standards , which may directly damage its reputation
• Increased costs and inefficiency : Fixing bad data takes time , which reduces an agency ’ s capacity for important initiatives and hampers its ability to make data-driven decisions
• Barrier to adopting data-driven tech : Emerging technologies like predictive analytics and AI need accurate , complete and current data to work well . An organization cannot be an effective data-driven agency with poor data
• Bad citizen experiences : An agency with poorquality data in its services may not provide adequate service to its customers , leading to frustration and discouraging further engagement with its services
Info-Tech ’ s blueprint also breaks down how data quality will only be managed well if it is supported by a governance structure that sponsors , prioritizes investment and enforces data quality practices .
Data quality suffers most at the point of entry and is one of the causes of the domino effects of data quality .
The research also provides an example of a hierarchical data governance organizational structure :
Poor intake can be one of the costliest forms of data quality errors .
Maintaining data quality is difficult , but the blueprint highlights the following pitfalls IT leaders must avoid getting the true value out of their data :
• Data debt : Data debt hinders efficiency and can prevent the desired process efficiencies
• Lack of trust : If data can ’ t be trusted , it won ’ t be used effectively . A lack of confidence in data quality can negatively impact business outcomes
1 . Data stewards : These are operational leaders who enforce data management and quality in dayto-day operations . Data stewards can also include data custodians and other working groups
2 . Data trustees : This group comprises senior agency leaders who are accountable for investing in and maintaining data quality . This group can also include a steering committee
3 . Data governance council : This council establishes data management practices that span across the agency . It can also include an executive sponsor or champion p
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