What is Data Quality & Why Should You Care?

According to Forbes, every day, the human population generates 2.5 quintillion bytes of data and 90% of the data that exists in the world was only created in the last two years. With the onslaught of technology and advanced techniques in data science, data generated by individuals has become a precious resource that can be leveraged by businesses and organizations to make informed decisions. However, the accuracy and reliability of data are critical to its usefulness. Poor quality data can result in erroneous conclusions, misinformed decisions, and financial losses. Therefore, it is essential to ensure data quality to avoid these negative outcomes.

Data quality refers to the degree to which data is accurate, complete, consistent, and timely. Inaccurate data can occur due to errors in data entry, data migration, or data integration. Incomplete data may result from missing values, incomplete records, or inadequate sampling. Inconsistent data may occur due to variations in data formats, units of measurement, or data structures. Timeliness issues may arise due to delayed data updates or poor synchronization.

Why invest in high quality data? 

While ensuring data quality is a relatively new idea, it is a critical ingredient in ensuring that the data can be successfully used for a variety of use cases. 

Data Quality for Businesses 

Data quality is vital for businesses and organizations to make informed decisions. It is necessary to ensure that data is of high quality before using it for business intelligence, data analysis, or data visualization. For instance, a healthcare provider needs accurate and complete patient data to diagnose and treat medical conditions effectively. An e-commerce platform needs reliable customer data to personalize marketing campaigns and improve customer experiences.

Data quality is also crucial for businesses to maintain a competitive advantage. High-quality data enables businesses to identify trends, patterns, and insights that can improve operational efficiency, increase customer satisfaction, and drive revenue growth. Conversely, poor quality data can lead to incorrect insights, inaccurate predictions, and missed opportunities.

Data Quality for Service Delivery 

Similarly, data quality is also a critical consideration for governments and government use. Government organizations all over the world are engaging in data analysis to improve the quality of service delivery, decrease costs and processing time and identify key trends to make decisions. For example, health commissions can use historical data on disease incidence to predict outbreaks and epidemics. Similarly, by building chatbots to answer citizen queries, the government can ensure transparency and communication with the population. 

Data, therefore, has many uses in public policy and service delivery. However, data quality is once again a critical consideration. Without quality data, government agencies may construct models that are not able to provide accurate predictions and may do more harm than good. 

Similarly, there is also the risk of creating models that are biased and inadvertently perpetuating discrimination through service delivery. For example, low quality data which does not have information on minorities may result in biased AI technologies. Already there are facial recognition technologies on the market that do not recognize minority ethnicities as a result of missing data. 

Data Quality for Community 

The maintenance of high quality data is not just a problem for businesses or government organizations but for the community at large. High quality data on business and governments can ensure greater transparency and provide citizens and whistleblowers with a sound basis to support their arguments. Already, major publications like the Economist are using data analysis to disseminate key insights about government operations amongst the global population. 

Moreover, high data quality also points the way for potential opportunities of collaboration and investment. Data on industry growth in a country for example can help outside investors make decisions about whether or not to invest and these decisions in turn can create millions of job opportunities for locals.  Access to data on economic and demographic trends can also allow the community to better prepare themselves for the future and make informed decisions. 

Solving the Quality Problem 

To ensure data quality, organizations must adopt a data quality management strategy that includes data profiling, data cleaning, and data governance which means investing in resources and processes that are able to monitor this cycle end to end. 

Data profiling involves analyzing data to identify quality issues such as data redundancies, data inconsistencies, and data inaccuracies. Data cleaning involves correcting or removing inaccurate, incomplete, or inconsistent data. Data cleaning tools can automate this process and ensure data consistency and accuracy. Data governance involves defining policies and procedures for managing data quality. Data governance includes data quality rules, data standards, data ownership, and data stewardship. Data governance ensures that data is accurate, complete, consistent, and timely. Data governance also ensures that data is secured and protected against unauthorized access and data breaches. Governments and senior management in organizations can take the lead in defining and ensuring adoption of data governance policies.

Stakeholders such as key organizations and the government can take several steps to translate the above defined principles into action to ensure data quality. Some of these steps include:

1. Establishing data quality standards: Governments and organizations should define clear and comprehensive data quality standards that outline what constitutes good quality data. These standards should be communicated to all relevant stakeholders and should be regularly reviewed and updated to ensure they remain relevant.
2. Investing in data management systems: Governments and organizations should invest in data management systems that allow them to collect, store, and analyze data in a consistent and accurate manner. These systems should also provide data validation checks to ensure that data is entered correctly and consistently.
3. Conducting regular data audits: Governments and organizations should conduct regular data audits to identify any data quality issues and take corrective action. Audits can be conducted manually or through the use of automated tools.
4. Implementing data governance policies: Governments and organizations should implement data governance policies that outline who is responsible for data quality, how data should be managed and used, and what procedures should be followed to ensure data quality.
5. Encouraging collaboration and transparency: Governments and organizations should encourage collaboration and transparency in data collection and management. This can be achieved by making data available to relevant stakeholders and involving them in the data collection and analysis process. This will help to ensure that data is accurate, relevant, and useful for decision-making.