The Impact of Poor Data Quality for the Public Sector
Like private sector companies, governments consider data to be an important asset. They collect data from multiple sources. This is used to make informed decisions for various purposes such as collecting taxes, managing social benefits, monitoring education systems and healthcare programs, issuing official documents and more.
Of course, for the decisions to make a positive difference, they must be based on accurate and trustworthy data. Let’s take a closer look at the impact poor quality data could have on the public sector.
Poor decisions
From deciding the price of flour to selecting routes where new roads are needed, public sector groups must make numerous decisions every day. All of these decisions are based on data analysis. Thus, poor-quality data could lead to unreliable conclusions that are detrimental to the organization’s goals.
New programs based on poor-quality data have a high risk of failing. A recent survey showed that 70% of government official respondents cited data issues as the main hurdle keeping them from delivering effective programs.
The impact can be felt at all levels from senior policymakers to grassroots-level service providers and even general citizens trying to make informed decisions about any government services they use.
Reduced trust in the government
Let’s say a large sum of money was spent building a well for a village based on the assumption that there was sufficient groundwater. But, when the well was dug, no water could be found. Of course, the villagers will be angered. They’re not likely to trust the government with anything else.
The lack of trust is an issue that snowballs. When citizens lose trust in the people designing programs, they’re not likely to participate in them. Thus, even good programs can fail.
Poor use of resources
Relying on poor-quality data can undermine everyday operations and cause issues with resource allocation. For example, inspectors may be sent to the wrong facility thereby wasting time and money.
Similarly, organizations may spend funds on programs with a high risk of failing and not have enough for other programs that could be more beneficial. In addition, the organization may have to spend additional resources on fixing poor decisions. Federal agencies spend $55 billion a year in improper payments due to poor record keeping.
Inefficiency
Public sector organizations that use data rely on a team of data scientists and analysts to make sense of the data available to them. The problem with poor quality data sets is that it leads to data scientists spending more time than necessary cleaning data and less on analyzing it.
Studies show that data scientists spend almost two-thirds of their time cleaning and organizing bad data — time they could otherwise spend gaining insights from their dataset. This slows down processes and can hamper the rate of innovation.
Challenges to Improving Data Quality for the Public Sector
Given the impact poor data quality can have on public sector activities, it is essential to make efforts to prioritize data quality. As with private sector enterprises, data is available abundantly. The challenge lies in making sure it meets quality parameters. Some of the key concerns that need to be addressed include:
· Unifying data
Government service providers rely on data collected from multiple sources. Left unaddressed, this could lead to data being siloed and creating duplicates. Public sector enterprises must find a way to unify data from all their sources and create a centralized database.
· Standardizing formats
With data being collected through multiple sources, there’s a higher risk of data being collected in different formats. For example, a citizen’s date of birth could be collected in the DD/MM/YY format through one source and the MM/DD/YYYY format through another. This causes inconsistencies that make it harder to create a high-quality central database.
· Maintaining relevance
‘1995’ could be a date, the number of people who availed of a service, or the price of a product. It’s difficult to understand what data represents when you have insufficient context. This problem is exaggerated for public sector organizations that deal with large complex, unstructured data sets.
· Duplicate records
Given the large size of the datasets used and the above challenges, citizen records are often duplicated. The same person may have a healthcare record by the name of John Smith and a school record by the name of John A. Smith. Having duplicates on file affects the quality of service provided as well as data analysis.
· Data decay
Once collected, data may be kept in the database for years. This means that even if the data met high quality standards at the time it was collected, it may decay with time thereby lowering the overall data quality. For example, let’s say a person shifted homes. This would make their old address on file incorrect.
Improving Data Quality
High-quality data has the potential to transform public sector services and increase citizen satisfaction rates while simultaneously saving resources. The first step to improving data quality is verifying that all data being collected meets quality standards. Doing so manually is next to impossible.
However, an automated verification tool can help verify citizen identities and contact details. When integrated with onboarding forms, it offers real-time verification results that keep records accurate and reliable.
In addition to checking new data being added to the database, existing records must also be regularly checked to ensure they remain valid. Data validation tools compare the data in your database against reliable third-party sources to ensure they are still valid. In cases such as where the city changes a street name, a validation tool can also append the records with updated information to fight data decay.
Summing It Up
Of course, the efficacy of verification and validation comes down to the tool being used. Public sector enterprises must consider their choices carefully and find a verification platform that can handle large volumes of data, validate the same against trustworthy up-to-date databases and provide value-add services for standardization and data enrichment. With the use of such tools to prioritize data quality, public sector organizations can quickly become agile and win the trust of millions of citizens.
