In the realm of public administration and governance, here are a few truths about Data Quality:
🚫 Data without quality isn’t credible. In the public sector, credibility is key to building trust with citizens.
📉 Data that isn’t credible doesn’t inform effective policy. Policymakers rely on accurate data to make informed decisions.
🔍 Data that doesn’t inform is a wasted public resource. Efficient use of resources is essential for public institutions.
🤖 Investing in AI and advanced analytics while the underlying public data is of low quality will never result in informed, effective policies.
Government agencies must emphasize as much on the quality of data as they do on policy analysis.
Data issues in the public sector are perhaps more detrimental than bureaucratic inefficiencies. While bureaucratic challenges might delay processes, data inaccuracies compromise the very essence of decision-making. Data inaccuracies breed mistrust, leading citizens to question the validity of governmental decisions.
A single unreliable public data source can misinform countless policies and strategic decisions. The ramifications are far-reaching.
Our guidance? Treat Data Quality not as a bureaucratic checkbox but as a fundamental pillar of public governance. By the time agencies recognise the gravity of data inaccuracies, rectifying them becomes an enormous challenge.
To maintain the integrity of public data, agencies must consider:
🎯 Purpose of the Data: What policy or decision does it inform?
📁 Data Origins: Where does the data come from, and how reliable are these sources?
🔒 Control Over Data Sources: Which sources are directly managed by the agency, and which aren’t?
📝 Public Expectations: How do citizens expect this data to be used?
🕰 Consistency Over Time: Will the data’s significance remain consistent over time?
🏢 Ownership and Stewardship: Which department or agency should be responsible for the data?
💬 Data Semantics: What does the data specifically represent?
🔄 Handling Data Changes: If there are changes or updates to the data, how are they managed?
📜 Documenting Data Evolution: How can we track the history and changes of the data?
📅 Data Versioning: How can we revert or reference previous versions of the data?
If a public sector entity can confidently address these ten points, they are well on their way to ensuring the data’s quality and its meaningful use in policy and decision-making.