Public sector organizations require correct data to provide good public services, make relevant decisions and create effective policies. The data adopt various forms, such as budgetary figures, labor data, responses to surveys and data collected in digital services. For the private sector, national statistics are fundamental to analyze the environment, economic and social trends and are a fundamental factor in defining strategies and making decisions against business.
In both sectors, data and statistics are essential to verify and monitor progress towards national, regional and international objectives, which is essential for the transparency of decisions and measure impacts that generate policies or decision -making, where all interest groups play a fundamental role in guaranteeing the quality of data and statistics.
Good quality statistical data must be adequate and relevant enough to support the results for which they are used and ensure that they meet the needs of their interest groups. Having good quality data does not mean that all values must be perfect; Good quality varies according to the data set. Quality can be measured by six dimensions:
• Integrity: Describe the degree of presence of records and data, that is, the data set contains all the necessary records and that all essential values are complete.
• Singularity: Describe the degree of absence of duplication in records. This means that the data contains only one record for each entity they represent, and each value is stored only once.
• Consistency: Describe the degree to which the values of a data set do not contradict other values that represent the same entity. For example, the date of birth of a mother must be prior to that of her child.
• Punctuality: Describe the degree to which the data accurately reflects the period they represent and if both the data and their values are updated. The data is appropriate if the period between its compilation and their availability is suitable for the planned use.
• Validity: Describe the degree to which the data is within the expected range and format. For example, the date of birth is not after the current one and is in a reasonable range.
• Precision: Describe the degree to which the data conforms to reality. Data bias can affect precision. When the data is biased, it means that they are not representative of the entire population. If possible, keep the bias in your measurements and make sure you communicate to your users. In a data set, the precision of individual records or the entire data set can be measured. The decision you make will depend on the purpose of the data and the needs of the business.
The different uses of the data will require different combinations of these dimensions; There are no universal criteria for good data. It is important to actively manage quality and work to improve poor quality, which requires statistical processes that guarantee at the same time profitability and ensure that the workload for data suppliers is maintained at an acceptable level.
The quality of the data is important because it directly influences the precision and reliability of the information used for precise and informed decision making. In addition, money is saved by reducing erroneous data correction expenses and prevents expensive errors and interruptions, improves the accuracy of the analysis, which allows better business decisions that drive sales, optimize operations and offer a competitive advantage, generates confidence in analysis tools and business intelligence panels.
Reliable data encourages users and public policy makers to use these tools for decision making instead of based on intuitions or improvised spreadsheets. Efficient data quality management also allows data equipment to focus on more valuable tasks, such as helping users and analysts to use data to obtain strategic information and promote the best data quality practices to reduce errors in daily operations.
All these elements have been worked on by the National Statistical System (SEN) managed by the DANE, which has allowed the country to have reliable and certified statistics with different international standards that allow to have reliable and quality data that they contribute to the country’s development to monitor and have relevant data so that interest groups make better decisions and can continue to build country. I want to thank the DANE for the recognition made as an expert theme in the processes of statistical quality evaluation that has allowed to certify different statistics nationwide.
