DEMOGRAPHIC DATA QUALITY DIFFERENTIALS IN LOCAL GOVERNMENT AREAS OF ENUGU STATE

CHAPTER ONE

1.1 BACKGROUND OF THE STUDY

In the current digital age, where decisions cannot be made without data, the importance of data quality cannot be overstated. Take, for instance, the major error made by Public Health England during their daily COVID-19 contact tracing in 2020. Nearly 16,000 positive coronavirus cases were omitted due to the use of an outdated Excel format (XLS) that could only handle around 65,000 rows. The newer format (XSLX), with a significantly higher capacity, could have prevented this oversight. This example underscores the critical role of data quality in ensuring accurate and reliable information. (Public Health England. (2020). Technical issue with COVID-19 data.

Data quality according to Jack E. Olson (2017) refers to the level of accuracy, completeness, reliability, and consistency of the data. It's about the degree of which the data can be trusted and to make informed decision.

In the ancient city of Sumer (Mesopotamia), clay tablets served as the precursors to today’s databases. These clay tablets were handmade. During the process of copying these tablets, information could easily be left out or misspelled. Making it one of the early signs of issues in data quality. Another trace of data quality was found in the monasteries in medieval Europe. They documented vital information on manuscripts, setting the stage for more structured data management and the need to have high-quality information. This illustrates the early recognition of the need for accurate record-keep following into the footprints of ancient data quality. The middle ages of data quality began in the Industrial Revolution. This periled to increased businessactivities, for example, encouraging textile mills to keep accurate records of high quality for better efficiency in managing operations and resources.

In the 19th century, handwritten ledgers posed early challenges for data quality, plagued by errors due to illegible handwriting and miscalculations, exposing the difficulties of manual data entry. The advent of computers in the mid-20th century ushered in early databases like the Integrated Data Store (IDS), revolutionizing how organizations stored and retrieved data. CODASYL’s efforts in the 1960s led to the development of COBOL, a language crucial for maintaining data consistency within early databases like the IBM Information Management System (IMS). With the rise computers and data, the quality of data was an important factor that had to be considered.

The 1970s and 1980s witnessed the prominence of mainframe computing, emphasizing data integrity and the reduction of redundancy, laying the groundwork for modern data quality practices. This era introduced frameworks like Total Data Quality Management (TDQM), offering standardized approaches for ensuring data quality across organizations. Not only in the industry field data quality has become an important topic. In the 1980s, academic papers about data quality were being published.

Globalization introduced new challenges, with multinational corporations grappling with variations in data formats, currencies, and languages. Today, cloud-based data quality platforms like SODA, Collibra DQ, Ataccama, and Informatica exemplify the evolution of data quality tools, providing scalable solutions for dealing with vast and diverse data. As we navigate the digital landscape today, for example, e-commerce platforms leverage advanced machine learning for precise product recommendations, underscoring the pivotal role of high-quality data in shaping our digital experiences. Because without high-quality data these models are not able to make accurate predictions or offer personalized recommendation, compromising the effectiveness of e-commerce platforms and diminishing user experience. High-quality data is indispensable for ensuring the precision and relevance of machine learning models in today's land Scape.

1.2 STATEMENT OF PROBLEM

Inaccurate, incomplete or inconsistent demographic data can lead to poor decision making, misallocation of resources and ineffective policy implementation, However, we cannot just ascertain if the data is inaccurate or not subjectively so it becomes expedient that we evaluate data to know each quality to ascertain if it is true to make decision, This is what this study intend to achieve.

1.3 AIM AND OBJECTIVES

The aim of this study is to evaluate the quality of age and sex data collected in all the local government of Enugu using 2006 census data.

OBJECTIVES

To evaluate the quality of data reported in single year age in local government areas in Enugu state.

To evaluate the quality of data reported in five years age group in local government areas in Enugu state.

To evaluate the quality of sex data in local government area in Enugu state.

To perform the overall data quality evaluation for age and sex data in local government areas in Enugu state.

1.4 SIGNIFICANCE OF THE STUDY

The study is will enable us to evaluate the quality of data collected in different local government area in Enugu state in order to help us to know the differentials in the data quality and that can help to do an improvement.

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